Informed Options Trading prior to M&A Announcements:

Insider Trading?∗

Patrick Augustin† Menachem Brenner‡ Marti G. Subrahmanyam§

McGill University, Desautels New York University, Stern New York University, Stern

December 11, 2013

Abstract

We investigate the possibility of insider trading in equity options around major informational

events such as corporate mergers and acquisitions (M&A) for a sample of 1,859 US events, fo-

cusing on both the target and the acquirer. Our goal is to quantify the likelihood of informed

trading and information leakage based on trading volume and implied volatility attributes. We

further focus our attention on options strategies which, in anticipation of public news announce-

ments and the presence of private information, should result in abnormal returns to insiders.

We find a highly positive abnormal trading volume and excess implied volatility in anticipation

of M&A announcements, with stronger effects for OTM call options on the target company. We

further document a decrease in the term structure of implied volatility and an average rise in

percentage bid-ask spreads of approximately 20 percentage points prior to the announcements.

Keywords: Asymmetric Information, Insider Trading, Mergers and Acquisitions, Market Mi-

crostructure, Equity Options

JEL Classification: G13, G14, G34, G38

∗We thank participants in the OptionMetrics Research Conference, New York, and a seminar at NYU Stern forcomments on a prior draft.†McGill University - Desautels Faculty of Management, 1001 Sherbrooke St. West, Montreal, Quebec H3A 1G5,

Canada. Email: Patrick.Augustin@mcgill.ca.‡New York University - Leonard N. Stern School of Business, 44 West 4th St., NY 10012-1126 New York, USA.

Email: mbrenner@stern.nyu.edu.§New York University - Leonard N. Stern School of Business, 44 West 4th St., NY 10012-1126 New York, USA.

Email: msubrahm@stern.nyu.edu.

1 Introduction

The recent announcement of the leveraged buyout of H.J. Heinz by an investor group made up

of Warren Buffett’s Berkshire Hathaway Inc. and Brazilian private-equity firm 3G Capital has

sparked concerns about the unusual option activity prior to the deal announcement. Was this

abnormal volume in the options of Heinz an indication of trading based on insider information?

Apparently, the Securities and Exchange Commission (SEC) thought so, alleging that a brokerage

account in Switzerland was used for illegal insider trading. Another noteworthy case from an

earlier period is the merger of Bank One with JP Morgan (JPM) Chase in July of 2004, where one

investor was alleged to have bought deep out-of-the-money (DOTM) calls just (hours) before the

announcement. While these cases received considerable publicity, they are by no means isolated

cases of such activity. Indeed, while the SEC has taken action in several cases where the evidence

was overwhelming, one can assume that there are many more cases that go undetected, or where the

evidence is not as clear-cut, in a legal/regulatory sense.1 Academic research on the role of informed

trading in equity options around major news events, and in particular, the announcements of

mergers and acquisitions (M&A), has been scanty.2 We aim to fill this gap in the research presented

in this paper.

The objective of our study is to investigate the pervasiveness of trading on inside information

in the context of M&A activity. We conduct a forensic analysis of volume and implied volatility

over the 30 days preceding a formal announcement. In addition, we examine alternative strategies

that may yield abnormal returns to informed traders. The focus is on options strategies, although

some of these may also involve trading in the underlying stocks. We examine companies on both

sides in an M&A transaction: the target and the acquirer. More specifically, we examine option

trading volume (and prices) prior to M&A announcements in the U.S. from January 1 1996 through

December 31 2012. We attempt to quantify the likelihood that a sudden and significant spike in

put or call trading volume prior to a major informational event is based on insider trading activity,

rather than being random. We also analyze the microstructure effects in the equity options market

arising from the arrival of informed trading prior to the M&A announcement dates, as a consequence

1See, for example, Options Activity Questioned Again in the Wall Street Journal, 18 February 2013.2The related issue of insider trading activity prior to earnings announcements and other important corporate

announcement has also received very little attention.

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of leakage of information due to the trading activity of insiders or some other means.

We find evidence of statistically significant average abnormal trading volume in equity options

written on the target firm over the 30 days preceding M&A announcements. Approximately 25%

of all cases have positive abnormal volume that is significant at the 5% level. The proportion

of cases with positive abnormal volume is relatively higher for call options (25%) than for put

options (15%). Stratifying the results by depth-in-moneyness, we find that there is significantly

higher abnormal trading volume (both in levels and frequencies) in OTM call options compared

to ATM and ITM calls. In addition, the ratio of average abnormal trading volume in OTM call

options relative to OTM put options, about 2.06, is significantly higher than the ratio of average

abnormal trading volume in ITM call options relative to ITM put options, which is lower at around

1.54. This is preliminary evidence that insiders may not only engage in OTM call transactions,

but potentially also sell ITM puts. In contrast, we find that there is no statistically significant

average cumulative abnormal trading volume for equity options written on the acquirer firms over

the 30 pre-announcement days, while it is positive and significantly different from zero for targets

(≈ 15,267 contracts).

In addition to evidence of abnormal trading volume in anticipation of M&A announcements, we

provide statistical evidence that the two-dimensional volume-moneyness distribution significantly

shifts, to options with higher strike prices, over 30 days as the announcement day approaches.

We further provide statistical tests of positive (no) excess implied volatility for target (acquirer)

firms in the pre-event window. Thus, informed trading has an impact on equity option prices and

we show that this leads to an attenuation of the term structure of implied volatility for target,

but not for acquirer firms. Similarly, the relative higher abnormal volume in OTM call options

for targets translates on average into a steepening of the implied volatility spread prior to the

announcement day. We also find that the percentage bid-ask spread on target equity options rises

from an average of 35% to 55% over the 30 days preceding the announcement. This effect is limited

to DOTM and OTM, as well as short- to medium-dated options. However, the increase in bid-ask

spread disappears if we adjust it by the options’ measure of leverage. Similar evidence for acquirer

equity options is weak.

This paper provides a preliminary forensic analysis of trading volume and implied volatility for

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equity options on both targets and acquirers involved in M&A announcements in the pre-event

window. It naturally suggests a classification scheme based on volume and price attributes that

may help regulators and prosecutors to detect illicit insider trading. Our evidence suggest that

informed trading is more pervasive than what one would expect based on the modest number of

prosecuted cases announced in the financial press.

The outline of the paper is as follows. In Section 2, we provide a review of the relevant literature.

We describe the data selection process and review the basic summary statistics in Section 3. The

main hypotheses and methodology are presented in Section 4. We analyze the results in the

subsections of Section 5. We end with a summary and preliminary conclusions in Section 6.

2 Literature Review

Our work relates more generally to the theoretical literature emphasizing the options market as a

preferred venue for informed traders, such as for example Easley et al. (1998).3 While the bulk

of the empirical research on options markets focuses on index options, there are fewer studies

using equity options (i.e., options on individual stocks), although they were trading for almost a

decade prior to the introduction of index options in the U.S.4 There are even fewer studies relating

to informed trading around major informational events, such as mergers and acquisitions, using

options strategies, and these are typically based on relatively small data sets.

Cao et al. (2005), for example, focus on the target firms in M&A transactions and study 78

U.S. merger or takeover firms between 1986 and 1994. They find evidence that the options market

displaces the stock market for information-based trading during periods immediately preceding

takeover announcements. More specifically, buyer-seller initiated call-volume imbalances, but not

stock imbalances, are associated with higher next-day stock returns. However, during periods of

normal trading activity, only buyer-seller initiated stock-volume imbalances exhibit predictability,

while option volume is uninformative. Option volume imbalances before M&A transactions are

concentrated in firms that eventually have successful takeovers and cannot be explained by target

3John et al. (2003) illustrate that the choice of informed trading in the derivative market depends on frictionssuch as margin requirements and wealth constraints.

4The main constraint in the earlier period was the availability of reliable data, which has changed dramaticallywith the advent of OptionMetrics as a reliable source for academic research in this area.

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firm characteristics. Chan et al. (2013), on the other hand, focus on the acquiring firms, using a

sample of 5,099 events for 1,754 acquirers, over the period 1996 to 2010. The one-day pre-event

implied volatility spread and the implied volatility skew, two proxies for informed option trading,

are respectively positively and negatively associated with acquirer cumulative abnormal returns.5

The predictive power of both measures increases if the liquidity of options is high relative to that

of the underlying stocks.

Barraclough et al. (2012) exploit the joint information set of stock and option prices to disen-

tangle true synergies from news in merger and acquisition transaction announcements. A sample

of 167 takeover announcements from April 1996 through December 2008 suggests that the increase

in trading volume from the pre-announcement period to the announcement day is most dramatic

for call options with an increase of 212.3% for bidder call options, and an increase of 1,619.8% for

target call options.

Acharya and Johnson (2010) study a sample of leveraged buyout deals from 2000-2006 and

document that the presence of more insiders lead to greater levels of insider activity, in the sense

that a larger number of equity participants in the syndicate is associated with suspicious stock and

options activity. Podolski et al. (2013) also provide some indirect evidence that the option-to-stock

volume ratio increases in the pre-takeover period, and increases relatively more for small deals that

are less likely to be detected. Finally, Nicolau (2010) studies the behavior of implied volatility

around merger announcements and interprets positive abnormal changes in implied volatility prior

to the announcement as evidence of information leakage.

Several other papers are peripherally related to the specific issue studied in this paper. Spyrou

et al. (2011) provide evidence of informed trading and the role of options markets in information

revelation around M&A announcements from the UK equity and options market. Keown and

Pinkerton (1981) studies excess stock returns earned by insider trading in the presence of merger

announcements, but does not investigate equity option activity. Meulbroek (1992) studies a sample

of illegal insider trading cases detected and prosecuted by the SEC from 1980 to 1989 and likewise

does not focus on option trading. Roll et al. (2010), on the other hand, study the relationship

5The implied volatility spread is calculated as the average difference between implied volatilities of same secu-rity/strike/maturity calls and puts). The implied volatility skew is calculated as the difference between impliedvolatilities of OTM puts and ATM calls.

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between the option-to-stock trading volume and post-earnings announcement returns. Bester et al.

(2011) and Subramanian (2004) develop theoretical option pricing models for the target in the case

of cash and stock-for-stock mergers respectively. Dubinsky and Johannes (2006) develop estimators

of the anticipated uncertainty of fundamental information released on earnings announcement dates

using equity option prices. Easley et al. (1998), Pan and Poteshman (2006), Xing et al. (2010),

Cremers and Weinbaum (2010), Johnson and So (2011), Driessen et al. (2012), Jin et al. (2012) and

Hu (2013) more generally relate various information-based measures derived from option trading

volume and prices to the predictability of stock returns. Finally, while Bollen and Whaley (2004)

and Garleanu et al. (2009) show how price pressure affects option prices, Cao and Ou-Yang (2009)

theoretically studies how differences in beliefs generate trades in options relative to stocks.

While the above studies tend to focus on either the target or the acquirer, we study the trading

patterns of equity options of both the target and acquirer, using data on both trading volume and

prices. More specifically, we focus on the behavior of the entire volume distribution and option-

implied volatility across the depth-in-the-money dimension, prior to takeover announcements. Im-

portantly, while some papers in the previous literature have investigated the informational content

of option trading volumes for post-announcement stock returns, none of them has focused on the

role of options strategies in illegal insider trading. Moreover, in contrast to the above studies, which

focus on various aspects of the M&A announcements using options data, our study focuses on the

extent to which informed trading, some of it illegal, can be detected by analyzing various option

strategies, using both puts and calls, of the target company and the acquirer. The likelihood of

informed trading in these cases will be quantified in our analysis. Our study is also more compre-

hensive in scope than the above prior studies, based on a much larger sample, with more rigorous

statistical tests.

3 Data Selection and Summary Statistics

The data for our study are obtained from three primary sources, Thomson Reuters SDC Platinum

Database, Center for Research in Securities Prices (CRSP) and OptionMetrics. The start date of our

sample period is dictated by the availability of option information in OptionMetrics, which initiated

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its reporting on 1 January 1996. Our final sample consists of 1,859 corporate transactions, for which

we could identify matching stock and option information for the target. These deals are undertaken

by 1,279 unique acquirers on 1,669 unique targets.6 For a sub-sample of 792 transactions, option

information is available for both the target and the acquirer. The starting point of our sample

selection is the full domestic Mergers and Acquisitions sample for US targets from the Securities

Data Company Platinum database over the time period January 1996 through December 2012.

We restrict the sample to deals with the intent of effecting change of control, i.e., to be included

in our sample, the acquirer needs to own less than 50% of the target’s stock before the transaction,

and is seeking to own more than 50% post transaction. Hence, we include only mergers, acquisitions

and acquisitions of majority interest in our sample, thereby excluding all deals that are acquisi-

tions of partial interest/minority stake purchases, acquisitions of remaining interest, acquisitions of

assets, acquisitions of certain assets, recapitalizations, buybacks/repurchases/self-tender offers and

exchange offers. In addition, we exclude deals for which the status is pending or unknown, i.e., we

only include completed, tentative or withdrawn deals. Next, we require available information on

the deal value and eliminate all deals with a transaction value below 1 million USD. Finally, we

match the information from SDC Platinum with price and volume information for the target in

both CRSP and OptionMetrics. We require a minimum of 90 days of valid stock and option price

and volume information on the target prior to and including the announcement date. We further

retain short-dated options expiring before the announcement date, as long as they are at-the-money.

All matches between SDC and CRSP/OptionMetrics are manually checked for consistency based

on the company name.7

Panel A in Table 1 reports the basic characteristics for the full sample, in which we require

option information availability only for the target. Pure cash offers make up 48.6% of the sample,

followed by hybrid financing offers with 22.3% and share offers with 21.7%. 82.9% of all transactions

are completed, and mergers are mostly cross-industry with 89.2% of all deals being undertaken with

a company not in the same industry based on the 4-digit SIC code. 90.2% of all deals are considered

6Thus, 190 of the targets were involved in an unsuccessful merger or acquisition that was ultimately withdrawn.However, we include these cases in our sample, since the withdrawal occurred after the takeover announcement.

7Overall, we have up to a maximum of one year of stock and option price information before and after theannouncement date. The cut-off of one year is arbitrary, but follows from the trade-off of the following two objectives:having of a sufficiently long time series before the announcement day to conduct the event study analysis, and keepingthe size of the dataset manageable to minimize computational complexity.

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to be friendly and only 3.4% are hostile, while 11.6% of all transactions are challenged. For a small

sub-sample of 6.5%, contracts contain a collar structure, 76.5% of all deals contain a termination fee,

and in only 3.5% of the transactions did the bidder already have a toehold in the target company.

Panel B shows that the average deal size is 3.8 billion USD, with cash-only deals being, on average,

small (2.2 billion USD), in comparison to stock-only transactions (5.4 billion USD). The average

one-day offer premium, defined as the offer price to the target’s closing stock price 1 day before the

announcement date, is 31%. Statistics for the sub-sample for which we have option information on

both the target and acquirer are qualitatively similar.

In Figure 1, we plot the average option trading volume in calls and puts for both the target

and acquirer, 60 days before, and after the announcement date. The run-up in volumes is a first

indication of information leakage prior to the public news announcements. However, these simple

averages mask significant cross-sectional differences across firms and options in abnormal trading

volumes. A more detailed analysis is provided in Section 5, the empirical section that follows the

discussion of our hypotheses.

4 Research Questions and Hypotheses

We attempt to quantify the likelihood of insider trading by investigating various trading strategies

that, in the presence of private information, should result in abnormal trading volumes and returns

in equity options in anticipation of public M&A announcements.8 We investigate several hypotheses

to test for such activity.9 The underlying assumption for all these hypotheses is that insiders are

capital constrained.

4.1 Insider Activity without Leakage of Information

• H1: There is evidence of positive abnormal trading volume in equity options, written on the

target firms, prior to M&A announcements.

If informed trading is present, without leakage of information, informed traders should benefit

8In this version, we do not present results for hypothesis H3. Additional evidence supporting the other hypothesesas well as robustness checks will be presented in the next version of the paper.

9We write these hypotheses as statements of what we expect to find in the data, rather than as null hypothesesthat would be rejected.

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relatively more from strategies that use options due to the leverage they obtain, if they are

capital constrained. A takeover announcement tends to be generally associated with a stock

price increase for the target (see Schwert 1996). An insider engaging in illegal trading is,

therefore, likely to engage in directional trading strategies to maximize his profits on his

private information, given his capital constraints. In that case, we would expect to see

significant positive abnormal trading volume in options for the target firms, in anticipation

of major corporate takeover announcements.

• H2: There is a higher ratio of the abnormal trading volume in (a) OTM call options compared

to ATM and ITM call options, and (b) ITM put options compared to ATM and OTM put

options, written on the target firms, prior to M&A announcements.

In the presence of informed trading, an option strategy involving the purchase of OTM call

options should generate significantly higher abnormal returns, as a consequence of the higher

leverage. Hence, we expect a relatively larger increase in abnormal trading volume for OTM

calls relative to ATM and ITM calls, in the presence of such activity.10 Moreover, an in-

sider taking advantage of his privileged knowledge of the direction of the target’s stock price

evolution is also likely to increase the trading volume through the sale of ITM puts.

• H3: In anticipation of major news events, there should be a volume increase in long-gamma

trading strategies for the acquirer firm, prior to M&A announcements.

Unlike the target firm, where the stock price almost always goes up after the announcement,

there is generally more uncertainty associated with the post-announcement direction of the

stock price of the acquiring firm. For the acquirer, we therefore anticipate an increase in

trading volume of option pairs which have high gamma, such as ATM straddle strategies for

example.

4.2 Insider Activity with Leakage of Information

• H4: There is positive (no) excess implied volatility for equity options, written on the target

(acquiring) firms, prior to M&A announcements.

10This case corresponds to the case study of JPM-Chase merging with Bank One, which exhibits such a pattern.

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Information about anticipated takeover targets may leak to financial markets and give rise to

informed trading. Jarrell and Poulsen (1989) argue that one such possible source of legitimate

information available to market participants is 13D filings when investors acquire more than

5% of the target firms stock. Thus, anticipating a price increase for target but not for

acquiring companies, we would expect to observe positive excess implied volatility on average

for takeover targets, and weak or no excess implied volatility for acquirers.

• H5: : The percentage bid-ask spread for options written on target firms should widen, prior

to M&A announcements.

Similar to H4, there should be no pattern in the bid-ask spread in the options on the target

firms as the announcement date approaches, absent insider activity with leakage of informa-

tion. An increase in the percentage bid-ask spread conditional on positive abnormal trading

volume would be evidence that information about a potential merger has leaked to the market

makers.

• H6: The convexity of the option smile, for target firms, should increase for call options and

decrease for put options, prior to M&A announcements.

According to H2, we expect abnormal trading volume in OTM call options and ITM put

options. Higher demand for OTM call options and ITM put options from the insider could

increase the relative expensiveness of OTM Call options (ITM puts) and hence, increase the

convexity of the option smile, in the presence of insider trading when the trading causes a

leakage of information to the market. Both real insider trading and higher trading activity

due to leaked information are associated with positive abnormal trading volume. Conditional

on positive abnormal trading volume, leakage should be identified through an increase in

both volume and prices, i.e., the convexity of the option smile should increase. However, if

we identify abnormal trading volume without a statistically significant shift in the slope of

the call option smile, then the increasing trading activity is more likely based on illegal insider

information, which has not yet leaked to the market.

• H7: The term structure of implied volatility should decrease for options on the target firms

before takeover announcements, and remain flat for options on the acquiring firms.

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Informed traders obtain the highest leverage by investing into short-dated OTM call options.

Demand pressure on short-dated options should lead to a relative price increase in options

with a short time to expiration compared to long-dated options. Thus, the term structure of

implied volatility should decrease for call options written on target firms and remain flat for

call options written on acquirer firms.

5 Analysis

We investigate the above hypotheses along two dimensions: volume and price. We begin by looking

into the behavior of volume prior to the M&A announcement dates.

5.1 Abnormal Volume

In order to address hypotheses H1 to H2, we conduct a forensic analysis of trading volume in

equity options during the 30 days preceding takeover announcements. We first summarize the

trading volume in our sample. We next look at specific trades that are most susceptible of insider

trading, the strongly unusual trading sample, and compare it to a matched random sample. We

further test for the presence of positive abnormal volume in call and put options across moneyness

categories, using the event study methodology. Next, we formally test, using an approximation to

the bivariate Kolmogorov-Smirnov test, whether the entire volume-moneyness distribution shifts in

anticipation of takeover news releases. Finally we show some evidence on the behavior of conditional

trading volume.

5.1.1 Statistics on Equity Option Trading Volume

We start by reporting basic summary statistics on option trading volume, stratified by time to

expiration and depth-in-money in Table 2.11 We classify three groups of time to expiration: less

or equal than 30 days, bigger than 30 but less or equal than 60 days and more than 60 days.

In addition, we assign five groups for depth-in-moneyness, where depth-in-moneyness is defined

as S/K, the ratio of the stock price S over the strike price K. Deep out-of-the-money (DOTM)

11Data on equity options volume have the particular feature of multiple zero-volume observations. These datapoints are omitted for the purpose of the basic summary statistics.

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corresponds to S/K ∈ [0, 0.80] for calls ([1.20,∞] for puts), out-of-the-money (OTM) corresponds

to S/K ∈ ]0.80, 0.95] for calls ([1.05, 1.20[ for puts), at-the-money (ATM) corresponds to S/K ∈

]0.95, 1.05[ for calls (]0.95, 1.05[ for puts), in-the-money (ITM) corresponds to S/K ∈ [1.05, 1.20[

for calls (]0.80, 0.95] for puts), and deep in-the-money (DITM) corresponds to S/K ∈ [1.20,∞[ for

calls ([0, 0.80] for puts). Panels A to C report statistics on all options in the sample, while Panels

D to F and G to I report the numbers separately for calls and puts.

A first observation is that, regardless of depth-in-money, the level of trading volume, as indicated

by the mean volume statistics, is significantly higher for short and medium-dated options compared

to long-dated options. For example, the average number of traded contracts in OTM options, for

the target (acquirer), is 370 and 285 (497 and 384) contracts for maturities of respectively less than

30 and 60 days, while it is 130 (193) contracts for options with more than 60 days to maturity.

This difference is more pronounced for call options than for put options. Second, the average

trading volume is higher for options on the acquirer firms (547 contracts) than on targets (283

contracts). Third, the distribution of volume as a function of depth-in-moneyness exhibits a hump-

shaped pattern for acquirers, irrespective of whether the options are short- or long dated. Hence,

trading volume tends to be highest ATM and decreases as S/K moves further into or OTM. In the

entire universe, for instance, the average volume is 1,084 contracts ATM, 497 and 398 contracts

respectively OTM and ITM, and 127 and 214 contracts respectively for DOTM and DITM options.

On the other hand, for targets, the highest average trading volume tends to be associated with

OTM options. Finally, we observe that the standard deviation of trading volume decreases with

time to expiration.

5.1.2 Strongly Unusual Trading Volume and Matched Random Sample

Our goal is to distinguish insider trading from random speculative bets. We are looking for un-

usual trading patterns that are considerably different from patterns exhibited by randomly selected

samples. Evidence of non-random trading would point to the existence of illicit option trades,

i.e., trades based on insider information. Thus, we start with cases that potentially are the most

likely ones to reflect insider trading. We define as strongly unusual trading (SUT) observations

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corresponding to the following four criteria for individual options:12 (1) the daily best recorded

bid is zero. This corresponds normally to DOTM options where the market maker, through his

zero bid, signals his unwillingness to buy but is willing to sell at a non-zero ask price. (2) The

option should expire on or after the announcement day, but is the first one to expire thereafter

(the so called front month option). Obviously, an insider would buy options that expire after the

announcement. In order to get the biggest bang for the buck, he would try to buy the cheapest ones

that are most likely to end up in the money. Short-dated OTM options tend to be cheaper and

provide the greatest leverage. (3) The option must have strictly positive trading volume. As many

individual equity options, especially OTM, have zero trading volume (all options have quotes in

a market making system) we focus on those that have positive volume assuming that the insiders

pick those OTM options that are the most likely to be exercised. (4) Finally, the transaction must

take place within 30 days of the event-date, defined as the 0 date (i.e., from event date -29 to 0).

An informed trader faces the tradeoff of leveraging on his private information prior to the event,

while likely not trading too close to the event, which may entail a higher risk of getting caught.13

Table 3 presents the sample statistics for the SUT sample. From the entire dataset, we identify

2,042 (1,847) option-day observations for the target (acquirer), passing the SUT selection criteria.14

The share of calls is slightly more than half, with a total of 1,106 (954) observations for the target

(acquirer). The average trading volume is 124 (110) contracts for targets (acquirers) and the

average trading volume for calls and puts is respectively 137 and 108 (126 and 94) for the target

(acquirer).15 Median trading volume is somewhat more stable, with a value of 20 contracts for

options written on the target, and around 15 contracts for options written on the acquirer. We

observe that, in the sample most susceptible of insider trading, the volume of the target firm, at

each percentile of the distribution, is significantly above that of the acquirer. The basic summary

statistics in Table 2 showed exactly the opposite.

We compare the statistics from the SUT sample to those from a randomly selected sample. The

12An observation corresponds to an option-day pair, i.e., the end-of-day volume for a given option on the target oracquirer.

13An additional aspect that we do not explicitly consider is the number of insiders involved and their connectionswith each other. Such information may reveal whether the information was shared by many players and potentiallyleaked to them.

14Note that the full sample has approximately 12,000,000 observations. For each event, the event time spans fromroughly 1 year before to 1 year after the announcement date.

15The average is taken across all observations satisfying the SUT selection criteria.

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sampling procedure is as follows: For each of the 1,859 (792) events with traded options on the

target (acquirer), we randomly select a pseudo-event date. We treat the pseudo-event date as a

hypothetical announcement date, and then apply the SUT selection criteria, i.e., we keep option-

day observations with a zero bid price, non-zero trading volume, that are within 30 days of the

pseudo-event date and which have an expiry date after the pseudo-event date.

The SUT sample statistics are compared to the Random Sample Trading (RST) statistics in

Panel B of Table 3.16 The number of observations, deals and options are somewhat higher in the

RST sample than in the SUT sample. The ratios of number of observations, deals and options in

the RST sample to those in the SUT sample range approximately between 1.4 and 1.8. However,

the average and median trading volume in the SUT sample is more than double that of the RST

sample. The maximum observed trading volumes are significantly higher in the SUT sample than in

the RST sample. However, the distributional statistics illustrate that this effect arises not because

of outliers. In the RST sample, around the 50th percentile of the distribution and upwards, volumes

are consistently less than half the trading volumes observed in the SUT sample at comparable cut-

offs of the volume distribution. Another interesting feature is that the distance between the median

and the mean is roughly constant around 100 traded contracts in the SUT sample. Finally, while a

similar pattern emerges for options written on the acquirer, the effect is overall weaker. In particular

statistics for put options are statistically similar across both samples. The significant difference in

average and median trading volumes between the SUT and RST samples for target firms is a first

indication of a shift in the volume distribution in anticipation of M&A announcements. We point

out that the difference between the two samples is likely to be conservative. For each event, we

have a maximum of 1 year of data before and after the event, rather than the whole universe of

traded options. Under normal circumstances, we should be using for each event all observations

going as far back as January 1996 until today. In such a situation, the difference is likely to be

even stronger.

To summarize, the entire distribution in trading volume differs significantly between the SUT

and RST samples for target firms, but not for acquirer firms. In particular, we observe that an

16As we have confined our study to a limited period and due to the fact that the variance may be large, we havedouble checked our results using 100 random samples of 1,859 (792) pseudo-events for the target (acquirer) in orderto minimize the standard error of our estimates. The results from this robustness check were very similar.

13

average trading volume above 100 contracts, with the mean to median distance of 100 contracts,

can be considered to be strongly unusual and non-random, when the transactions occurs at a zero-

bid within 30 days of the announcement date on options expiring after the announcement. This

first pass test is preliminary evidence in favor of the hypothesis H1 by showing that there is a

non-random increase in trading volume on targets, but not on acquirers, prior to a public M&A

announcement.

5.1.3 Shifts in Option Trading Volume Density

The previous section has illustrated that the 30 days prior to M&A announcement dates exhibit

strongly unusual trading patterns, in particular for targets. The question is whether there is a

monotonic shift in the option trading volume distribution as the announcement date approaches.

We formally test for a shift of the bivariate volume-moneyness distribution across time in anticipa-

tion of the announcement dates.

Figure 2 visually illustrates the shift in volume distribution for calls and puts written on the

target as we approach the announcement date. Each individual line reflects a local polynomial

function fitted to the volume-moneyness pairs. It is striking to see how the volume distribution,

for target call options, shifts to the tails and puts more weight on DITM and DOTM categories as

we approach the announcement date. In addition the volume level keeps increasing, in particular

in the event window [−4,−1]. The last event window [0, 0] incorporates the announcement effect,

whereby the overall average trading level is lifted upwards, and the distribution shifts to ITM call

options and OTM puts. The lower two panels illustrate the same graphs for the acquirer firms.

While it is possible to see a small shift in the volume distribution across time, the magnitudes

of the shifts are small. It is mainly the announcement effect that is clearly distinguishable for

acquirers. Another way to visualize the change in the distribution is given in Figure 3, although

this graph doesn’t reflect the bivariate dimension of the distribution. The dashed blue line and the

solid green line in each plot represent the 90th and 95th percentile of the distribution, whereas the

dotted red lines reflect the interquartile range. The percentage increase in the percentiles of the

volume distribution is clearly much more pronounced for the targets than for the acquirers. For

example, the interquartile range for target call options increases from a level below 50 contracts

14

to approximately 2000 contracts on the announcement day. For acquirer call options, on the other

hand, the interquartile range increase from approximately 200 to about 1,200 contacts on the

announcement day.

To summarize, there is a significant shift in both the mean and median trading volume in

anticipation of U.S. M&A transactions for target firms. This shift is more pronounced for DOTM

and OTM call options, compared to ITM and DITM options. On the other hand, the increase in

the level of trading volume for acquirers is more moderate and doesn’t seem to be much different

for ITM options relative to OTM calls and puts. This confirms our hypothesis H2 that there is

a higher abnormal trading volume in deep OTM call options, compared to ATM and ITM call

options, but that abnormal trading volume, if any, should be equal across moneyness categories for

acquirers. In what follows, we apply a formal statistical test for the shift in volume distribution.

In order to test whether the bivariate volume-moneyness distribution shifts across time prior

to announcement dates, we use a two-sample bivariate Kolmogorov-Smirnov test. The two-sample

Kolmogorov-Smirnov (KS) test is a non-parametric test for the equality of two continuous distri-

bution functions. Essentially, the KS statistic quantifies the distance between the two empirical

cumulative distribution functions. While the test statistic is straightforward to compute in the

univariate setting with distribution-free properties, the computation in the multivariate setting can

become burdensome, in particular, when the sample size is large. The reason for this is because

in the univariate setting, the empirical cumulative distribution function diverges only at its ob-

served points, while it diverges at an infinite number of points in the multivariate setting. To see

this, remember that in a multivariate setting, there is more than one definition of a cumulative

distribution function. In particular, in the bivariate setting, the four regions of interest are

H(1) (x, y) = P [X ≤ x, Y ≤ y] , H(1) (x, y) = P [X ≤ x, Y ≥ y] (1)

H(1) (x, y) = P [X ≥ x, Y ≤ y] , H(1) (x, y) = P [X ≥ x, Y ≥ y] , (2)

and we need to evaluate the empirical cumulative distribution function in all possible regions. To

reduce computational complexity, we rely on the Fasano and Franceschini generalization of the

two-sample bivariate KS test. Define the two sample sizes (x1j , y

1j

): 1 ≤ j ≤ n and

(x2j , y

2j

):

15

1 ≤ j ≤ m, with their corresponding empirical cumulative distribution functions H(k)n and H

(k)m for

regions k = 1, 2, 3, 4. The Fasano-Franceschini (Fasano and Franceschini (1987)) FF-test statistic

is then defined as

Z′n,m = maxT ′(1)n,m, T

′(2)n,m, T

′(3)n,m, T

′(4)n,m, (3)

where

T′(k)n,m = sup(x,y)∈R2

√nm

n+m

∣∣∣H(k)n (x, y)−H(k)

m (x, y)∣∣∣ . (4)

Although the analytic distribution of the test statistic is unknown, its p-values can be estimated

using an approximation, based on Press et al. (1992), to Fasano and Franceschini’s (FF) Monte

Carlo simulations.

Our prior is that the FF-statistic, which reflects the distance between the two bivariate empirical

distribution functions (EDF), should monotonically increase for target firms as we get closer to the

announcement date.17 In contrast, there should be no evidence for such an increase, or at worst

weaker evidence, for acquirer firms. Essentially, the difference in EDFs should be larger between

event windows [−29,−25] and [−24,−20], than between [−29,−25] [−19,−15]), etc. In addition,

the FF-statistics should increase relatively more for short-dated options, which are closer to the

announcement date. These predictions are clearly confirmed by the results in Tables 4 and 5. The

FF test reveals statistically significant differences in the bivariate volume-moneyness distributions,

as we move closer to the announcement date. We compare the distributions in event window blocks

of 5 days. A glance at the table reveals that the test is statistically significant, at the 1% level, for

almost all comparisons. In addition, the magnitude of the statistic is monotonically increasing as

we move from the left to the right, and as we move from the bottom to the top.

Panel A and B in Table 4 report results for calls and puts respectively for the target. For

example, the first row shows that the bivariate distribution significantly shifts from event window

[−29,−25] to [−24,−20], with an FF statistic of 0.0279. The test statistic increases to 0.1592, if we

17One can think of the FF-statistic as a KS-statistic in the multivariate setting. The FF-statistic is computationallyless intensive in the multivariate case, but is consistent and does not compromise power in large sample sizes. SeeSimon L. Greenberg, Bivariate Goodness-of-fit tests based on Kolmogorov-Smirnov Type Statistics.

16

compare event windows [−29,−25] and [−4,−1], and to 0.4070, for the event windows [−29,−25]

and [0, 0]. Overall, the largest test statistics seem to be associated with comparisons between the

announcement date ([0, 0]) and the event window immediately preceding it ([−4,−1]). However,

for short-dated options with time to expiration less than 30 days, the statistic for the difference

in distributions for the shift from event window [−29,−25] to [−4,−1], excluding the announce-

ment effect, is with a value of 0.3388 (0.34) for call (put) options higher than the announcement

effect going from the event window [−4,−1] to the announcement date. Changes in the bivariate

distributions are statistically significant at the 1% level for almost all event windows.

Results for the acquirer are reported in Table 5 and confirm our prior. The shifts in the

distributions, as indicated by the FF test statistics, are generally significantly smaller than for the

target. For instance, comparing the period [−29,−25] to [−4,−1] for call options in the entire

sample, the test-statistic indicates a value of 0.0499 for the acquirer and 0.1592 for the target. The

average ratio across comparisons is about 2. Even the shifts on the announcement dates are less

acute than for the target companies. Finally, statistical significance is much weaker for adjacent

event windows, and is mostly restricted to short-dated options just prior to the announcement date.

5.1.4 Abnormal Trading Volume - Event Study

Hypothesis H1 asserts that there is positive abnormal trading volume in call equity options written

on the target prior to a public M&A announcement. We test this by running a classical event

study. For each of the 1,859 deals in the sample, we source the aggregated total option volume

on the target’s stock, as well as the total aggregated volume traded in calls and puts. Option

information for the acquirer is limited to a subsample of 792 deals. To compute abnormal trading

volume, we use as a benchmark a constant-mean-trading-volume model, as well as two different

volume-based versions of the market models. We define the market trading volume as the median

(mean) total call and put trading volume across all options in the OptionMetrics database. As we

are interested in the abnormal trading volume in anticipation of the event, we use as the estimation

window the period starting 90 days before the announcement date until 30 days before the event.

The event window in our case stretches from 30 days before until one day prior to the event. To

account for the possibility of clustered event dates, we correct all standard errors for cross-sectional

17

dependence.

The results are reported in Table 6. The average cumulative abnormal trading volume is posi-

tive and statistically significant for the target across all model specifications. The magnitude of the

average cumulative abnormal volume over the 30 pre-event days is also economically meaningful

with an estimate of 11,969 contracts for call options using the median market model. For put

options on the target, average cumulative abnormal volume is also positive and highly statistically

significant, but the average cumulative abnormal volume over the 30 pre-event days at 3,471 con-

tracts is economically much smaller. The evolution of average abnormal and cumulative abnormal

trading volume for targets is illustrated in the upper two panels in Figure 4. It is apparent that the

average cumulative abnormal trading volume in put options is quantitatively less important than

the average cumulative abnormal trading volume in call options, which is primarily driving the

results for the overall sample. The daily average abnormal volume for call options is positive and

steadily increasing to a level of approximately 1,500 contracts the day before the announcement.

Individually, the number of deals with positive abnormal trading volume at the 5% significance

level ranges from 472 to 492 for calls, and from 271 to 319 for puts, corresponding to respectively

25% and 15% of the entire sample.18 These results confirm the null hypothesis H1, that there is

positive abnormal trading volume in call and put equity options written on the target prior to a

public M&A announcement.

Focusing on the acquirer in Table 6, the average cumulative abnormal trading volume over the

30 pre-event days is statistically indistinguishable from zero and fluctuates between positive and

negative values depending on the model specification. Also, the number of deals for which there is

positive and statistically significant abnormal volume on the acquirer is lower than for the targets.

Depending on the model specification, there about 54 to 56 such cases for call options and 45 to 51

for put options. This corresponds to about 7% and 6% of all deals for calls and puts respectively.

The bottom two panels in Figure 4 illustrate the evolution of the average abnormal and cumulative

abnormal trading volume for acquirers. In line with our prior, there is no clear evidence of positive

abnormal trading volume for acquirers, whether for calls or puts. Daily average abnormal volume

18Unreported results indicate that, at the 1% significance level, the number of deals with positive abnormal tradingvolume for the entire sample range from 278 to 292 for calls, and from 138 to 195 for puts, corresponding to afrequency of respectively 16% and 8%.

18

resembles more a random walk rather than any clear pattern.

In addition to the aggregated results, we stratify our sample by moneyness and conduct an

event study for each category. We find that there is significantly higher abnormal trading volume

for targets in OTM and ITM call options, compared to ATM and ITM calls, both in terms of

volume levels and frequencies. Using the median market model, for instance, the average cumulative

abnormal volume is 3,797 (1,860) contracts for OTM calls (puts) and 1,702 (1,110) contracts for

ITM calls (puts), while it is 1,059 (188) for ATM calls (puts). These values correspond to 383

(300, 448) deals or 21% (16%, 24%) for OTM (ATM, ITM) calls and 387 (254, 316) deals or 21%

(14%, 17%), for OTM (ATM, ITM) puts, respectively. In addition, while we find that the average

cumulative abnormal volume is positive and statistically significant for both OTM and ITM calls

and puts, it is only statistically significant at the 5% level for ATM call options, but not for put

options. Panel B reports the results from paired t-tests for differences in means of the cumulative

average abnormal volumes across different depths. Consistent with our hypothesis H2, these results

emphasize that there is a higher ratio of abnormal trading volumes for OTM call options compared

to ATM and ITM call options. The difference in means, using the median market model, for

OTM calls relative to ATM and ITM calls is 2,738 and 2,096 respectively, which is positive and

statistically different from zero. On the other hand, the difference in means between ATM and ITM

calls is slightly negative (-643), but not statistically different from zero. Even though we do confirm

that the ratio of the average cumulative abnormal volume for ITM put options is higher than for

ATM put options, we also find that there is a higher expected abnormal volume for OTM compared

to ITM put options, even though the difference of 750 contracts is small in magnitude, given that it

is a cumulative measure over 30 days. This is some preliminary evidence that insiders may not only

engage in OTM call transactions, but potentially also sell ITM puts. The corresponding statistics

for the acquirer stratified by moneyness do not show any clear pattern.

To summarize, the event study further supports hypotheses H1 and H2. In other words, there

is ample evidence of positive abnormal volume in equity options for the targets, but not for the

acquirers in M&A transactions, prior to the announcement date. In addition, we document that,

for the targets, there is a significantly larger amount of abnormal trading volume in OTM call

options compared to ATM and ITM call options. However, the evidence that insiders may also

19

engage in writing ITM put options is weak. The results of the event study are supported by formal

statistical evidence that the two-dimensional volume-moneyness distribution significantly shifts,

over both time and moneyness, over the 30 days preceding the announcement day. Hence, the level

of the volume distribution increases with a higher frequency of trades occurring in both OTM and

ITM options. Moreover, this shift in the bivariate distribution is not random, as we illustrate by

comparing the volume distribution from a suspicious trading sample to that of a randomly matched

sample.

5.1.5 Non-parametric Volume at Risk.

To supplement our forensic analysis on the behavior of options volume before takeover announce-

ments, we analyze trading volume patterns conditional on no trading volume for one to five earlier

days. This analysis may be insightful, given the significant amount of zero volume observations that

characterize the data for equity options. To do so, we calculate, non-parametrically, the probability

of spikes in trading volume, conditional on zero trading volume between one and five days prior to

the spike. We define as the conditionalVolume-at-risk (VolaR), at a confidence level α ∈ (0, 1), the

smallest trading volume ν, such that the probability of the volume V exceeding ν is less than or

equal to (1− α). Formally, V olaR is given by:

V olaRiα = infν ∈ R : P (V > ν) ≤ 1− α,i∑

j=1

Vt−j = 0. (5)

The statistics in Panel A of Table 9 show the VolaR statistics for the 30 days preceding the

announcement dates. We see that for target companies, if there is a day with zero option trading

volume on a given company, the probability that the volume exceeds 100 (200; 1,000) contracts

during the next day is less than 10% (5%, 1%). When we look at the options written on the

acquiring firm, the numbers look very similar. The probability of observing more than 96 (191;

1,000) traded contracts on a company when there was no option trading volume on its stock during

the preceding day, is less than 10% (5%, 1%). Naturally, the V olaRiα levels decrease for the more

restrictive cases with several preceding days with zero trading volume (i > 1). However, the

values are close to the benchmark case (i = 1) of zero trading volume for one preceding day only.

20

In the case with five earlier days with no recorded option trading, the probabilities of exceeding

respectively more than 65, 126 or 710 traded contracts are less than 10%, 5% and 1%. Statistics

for the acquirer sample are again very similar, with V olaR5α values of 70, 143 and 700 contracts at

the confidence levels 90, 95 and 99.

We compare these critical levels of trading volumes to the sample statistics from the SUT se-

lection, which correspond to the same 30 days before the announcement dates. We have argued

that the unconditional probability of exceeding more than 100 traded contracts on any given com-

pany, when there was no trading volume on the preceding day, is less than 10%. This volume of

100 contracts seems small in comparison to the average trading volume in call options, written

on the target, from the SUT sample. The average trading volume of option trades characterized

as strongly unusual, which occurred during the 30 days preceding a M&A announcement, is 124

contracts, with the volume for call options being slightly higher (137 contracts). Thus, the VolaR

statistics further emphasize the strongly unusual trading patterns in anticipation of these publicly

unexpected events.

In addition, we compare the VolaR statistics to those of a randomly matched sample. Similarly

to the earlier exercise, we randomly choose 1,859 (792) pseudo-event dates for the target (acquirer)

companies, and then investigate the VolaR statistics over the 30 days preceding the pseudo-event

dates. Given that trading picks up during the pre-announcement period, one would expect to see

fewer days with zero trading volume for individual equity options, and as a consequence, the VolaR

statistics should be lower in sample. Panel B in Figure 9 compares our prior. The VolaR levels

are higher in the randomly matched sample. For instance, the 1% VolaR level for target (acquirer)

companies is 1,525 (1,492) option contracts in the random sample compared to 1,000 (986) in the

real sample.

The forensic analysis of the trading volume behavior in equity options confirms our priors stated

in hypotheses H1 and H2. The next step is to investigate hypotheses H4 to H7 by focusing on the

information embedded in equity options prices, based on their implied volatilities.

21

5.2 Implied Volatiliy

The price behavior of options is embedded in the summary statistic of implied volatility. As a

complement to the volume results, we conduct a forensic analysis on implied volatility over the 30

days preceding the announcement date. We first conduct an event study to test for the presence of

positive excess implied volatility relative to a market benchmark. Second, we study the behavior

of the convexity of the option smile, in anticipation of news releases, followed by investigations of

the bid-ask spread. Finally, we address the hypothesis related to the term structure of implied

volatility.

5.2.1 Excess Implied Volatility - Event Study

As informed traders benefit from their private information only to the extent that the outcome of

the announcement on stock prices is certain, we expect to find positive excess implied volatility for

equity options on target, but not acquirer companies. We use the interpolated volatility surface

in OptionMetrics for this exercise. To analyze the behavior of ATM implied volatility, we use

the 50 delta (or 0.50) options, and we reference the 80 and 20 delta options for ITM and OTM

options respectively. We test two different model specifications for our results: a simple constant

mean volatility model and a market model, where we use the S&P 500 VIX index as the market’s

benchmark for implied volatility. The estimation window runs from 90 to 31 days before the

announcement date, while our event window relates to the 30 days before the event, excluding the

announcement itself. All standard errors are clustered by time to account for the clustering of

events.

Panel A in Table 7 documents that excess implied volatility is quite pervasive in our sample. At

the 5% significance level, using the market model, there are about 812 cases (44% of the 1,859 deals)

with positive excess implied volatility for call options and about 798 cases (43% of the 1,859 deals)

with positive excess implied volatility for put options. The frequencies are similar for OTM implied

volatilities and slightly lower for ITM implied volatilities, where positive excess implied volatility

is documented for 39% (calls) and 41% (puts) of all cases. In addition, cumulative excess implied

volatilities are consistently positive and statistically significant for the target companies. The

average cumulative excess implied volatility over the 30 pre-announcement days is approximately

22

50% for calls and 40% for puts.

In contrast to the results for targets, average cumulative excess implied volatilities fluctuate

around zero and are statistically indistinguishable from zero. However, on a case by case basis,

39% (33%, 38%) of the deals exhibit positive and statistically significant positive excess implied

volatility for ATM (ITM, OTM) call options, while 38% (36%, 32%) of the deals exhibit positive

and statistically significant positive excess implied volatility for ATM (ITM, OTM) put options.

To summarize, the event study confirms our hypothesis H4, which asserts that there should be

on average positive cumulative excess implied volatility for the target companies, but not for the

acquirer firms. These results are graphically presented in Figure 5 for ATM implied volatilities.

The daily average excess ATM implied volatility starts increasing about 18 days before the an-

nouncement date and then steadily rises. In contrast, for acquirers, the daily average ATM excess

implied volatility fluctuates around zero and behaves much more like a random walk.

5.2.2 Information Dispersion and Bid-Ask Spreads

To address hypothesis H5, we study the evolution of the bid-ask spread in anticipation of the M&A

announcement. The prediction of the null hypothesis H5 is that the percentage bid-ask spread in

option premia should widen prior to the announcement. Strong evidence in favor of this hypothesis

would indicate that the information about a potential merger has leaked to the market makers and

that unusual trading activity is more likely to be associated with illicit trading.

Figure 6 plots the evolution of the average percentage bid-ask spread from 90 days before the

announcement date to 90 days after the event. Figure 6a shows that the average percentage bid-

ask spread on target options rises from about 35% to 55% and then jumps up to approximately

80% because of the announcement effect. Interestingly, this rise in bid-ask spreads is restricted to

DOTM and OTM options.19 In Figure 6b, the equivalent graph for the subsample for which we have

information on options on the acquirer is plotted. While the level of the average percentage bid-ask

spread is also increasing as we approach the announcement date, the increase is economically less

significant than for the options on the target, as it rises from approximately 13%, 90 days before

the announcement date, to 14%, and then stabilizes at event time zero. However, when we adjust

19These graphs are not reported because of space constraints but are available upon request.

23

the bid-ask spread to the leverage (using the options leverage measure omega, Ω = ∆Sc ), we find

no increase in the bid-ask spread before the announcement (see Figures 6c and 6d).

To summarize, although we find that the percentage bid-ask spread in targets’ option premia

increases prior to the announcement, which could be argued to be evidence that information about

the future transaction leaked to the market, the leverage-adjusted measure does not support this

conclusion.

5.2.3 Volatility Smile and the Term Structure of Implied Volatility

Hypotheses H6 predicts that the convexity of the option smile, for target firms, should increase

for call options and decrease for put options, prior to M&A announcements. We investigate this

question by plotting in Table 7 various measures relating to the convexity of the option smile.

Graphs 7a and 7c illustrate two documented measures of the implied volatility skewness. The first

measure, on the left scale, is measured as the difference between OTM call and put implied volatility,

standardized by ATM implied volatility. The second measure, on the right axis, is measured as

the difference between OTM implied put and ATM implied call volatility. To our surprise, both

measures seem to remain flat prior to the announcement date, whether for target or for acquiring

companies.

Finally, hypothesis H7 states that the term structure of implied volatility should decrease for

options on the target firms before takeover announcements, and remain flat for options on the

acquiring firms. The justification for this hypothesis is that informed traders obtain the highest

leverage by investing into short-dated OTM call options. Hence, demand pressure on short-dated

options should lead to a relative price increase in options with a short time to expiration compared to

long-dated options. Thus, a confirmation of our hypothesis would be supportive of the fact that, on

average, activity in the options market before major takeover announcements is partially influenced

by investors with inside information. Figure 7b documents that the average term structure of

implied volatility, calculated by the difference between the implied volatility of 3-month and 1-

month options, is decreasing from -1.8% by about 2.5 percentage points to approximately -4.3%

over the 30 days before the announcement date. This result obtains for both call and put options.

However, the evidence is much weaker for acquirers and is restricted, if any, to call options. Figure

24

7d illustrates that the level of the implied volatility term structure decreases from about -0.95%

to -1.45% for call options, and from roughly -1.2% to -1.45% for put options. These decreases are

statistically insignificant.

In a nutshell, we find supportive evidence of the fact that the average implied volatility spread

significantly increase for target firms, but not for acquirer firms, prior to M&A announcements. In

addition, the term structure of implied volatility becomes more negative for targets and remains

roughly flat for acquirers, as we approach the announcement.

5.3 Joint evolution of abnormal volume and implied volatility

We document that abnormal volume and excess implied volatility is more pervasive ahead of merger

and acquisitions than what would be expected based on the number of insider trading litigations.

The ultimate goal of our forensic study is to provide regulators with a clear guidance on which

suspicious trades are worth pursuing. In the future, we therefore plan to double sort all cases

based on their abnormal volume and excess implied volatility levels. This should in theory lead to

a classification scheme to those cases that are most susceptible of insider trading. An additional

filter given by the SUT selection criteria should further sharpen the monitoring results. This

analysis will follow in our future research.

6 Conclusion

Research on trading in individual equity options has been scanty, and even more so, centered on

major informational events, such as M&As. In light of recent investigations into insider trading

based on unusual abnormal trading volume in anticipation of major corporate acquisitions, we

investigate the presence of informed options trading around such unexpected public announcements.

We focus on equity options written on both the target and the acquirer. Our goal is to quantify the

likelihood of informed trading by investigating various options trading strategies, which should a

priori lead to unusual abnormal trading volume and returns in the presence of private information.

An analysis of trading volume and implied volatility over the 30 days preceding formal takeover

announcements suggests that that informed trading, whether based on inside information or leakage,

25

is more pervasive than what would be expected based on the actual number of prosecuted cases.

We find that there is statistically significant abnormal trading volume in call options written on

the target, prior to M&A announcements, with particular pronounced effects for OTM calls. We

provide formal tests of shifts in the bivariate volume-moneyness distribution and illustrate that the

levels of unusual volume in options trading cannot be replicated in a randomly matched sample.

We further find strong support for positive excess implied volatility for the target companies, but

not for the acquirers. In addition, for targets, the term structure of implied volatility becomes

more negative and the implied volatility spread widens, not so for acquirers. The evidence from

the bid-ask spread is more ambivalent.

Future analysis, based on the attributes of abnormal volume and excess implied volatility, will

lead to a classification that should ultimately be reflective of those cases that are most likely of

insider trading. We thereby hope to provide some guidance to regulators on where to start their

investigations into illicit options trading. While some insider cases are obvious, detecting others is

like finding the proverbial needle in a hay stack.

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Meulbroek, L. K. (1992). An empirical analysis of illegal insider trading, The Journal of Finance47(5): pp. 1661–1699.

Nicolau, A. A. (2010). An examination of the behavior of implied volatility around merger an-nouncements, Bachelor of Science Thesis, New York University, Leonard N. Stern School ofBusiness .

Pan, J. and Poteshman, A. M. (2006). The information in option volume for future stock prices,The Review of Financial Studies 19(3): pp. 871–908.

Podolski, E. J., Truong, C. and Veeraraghavan, M. (2013). Informed options trading prior totakeovers does the regulatory environment matter?, Journal of International Financial Markets,Institutions & Money .

Roll, R., Schwartz, E. and Subrahmanyam, A. (2010). O/s: The relative trading activity in optionsand stock, Journal of Financial Economics 96(1): 1 – 17.

27

Schwert, G. (1996). Markup pricing in mergers and acquisitions, Journal of Financial Economics41(2): 153 – 192.

Spyrou, S., Tsekrekos, A. and Siougle, G. (2011). Informed trading around merger and acquisitionannouncements: Evidence from the uk equity and options markets, Journal of Futures Markets31(8): 703–726.

Subramanian, A. (2004). Option pricing on stocks in mergers and acquisitions, The Journal ofFinance 59(2): pp. 795–829.

Xing, Y., Zhang, X. and Zhao, R. (2010). What does the individual option volatility smirk tell usabout future equity returns?., Journal of Financial & Quantitative Analysis 45(3): 641 – 662.

28

Tab

le1:

Des

crip

tive

and

Fin

anci

alov

ervie

wof

M&

AS

amp

le-

Fu

llS

amp

le

Panel

Ain

this

table

pro

vid

esan

over

vie

wof

the

M&

Adea

lch

ara

cter

isti

csfo

rall

US

dom

esti

cm

erger

sand

acq

uis

itio

ns

inth

eT

hom

son

Reu

ters

SD

CP

lati

num

data

base

over

the

tim

ep

erio

dJanuary

1996

thro

ugh

31

Dec

emb

er2012,

wher

ea

matc

hw

as

found

for

the

targ

etin

the

CR

SP

mast

erfile

and

inO

pti

onM

etri

cs

base

don

the

6-d

igit

CU

SIP

.T

he

sam

ple

excl

udes

dea

lsw

ith

an

unknow

nor

pen

din

gdea

lst

atu

s,in

cludes

only

dea

lsw

ith

available

dea

lin

form

ati

on,

wher

e

the

dea

lva

lue

isab

ove

1m

illion

USD

and

wher

ean

effec

tive

change

of

contr

ol

was

inte

nded

.In

addit

ion,

we

requir

eva

lid

pri

ceand

volu

me

info

rmati

on

in

both

CR

SP

and

Opti

onM

etri

csfo

rth

eta

rget

for

at

least

90

day

spri

or

toand

on

the

announce

men

tday

.P

anel

Billu

stra

tes

the

financi

al

stati

stic

sof

the

dea

ls.

P1d

(P1w

,P

4w

)re

fers

toth

eP

rem

ium

1D

ay(1

wee

k,

4w

eeks)

pri

or

toA

nnounce

men

tD

ate

inp

erce

nta

ge

term

s.E

xpla

nati

ons:

The

dea

lva

lue

is

the

tota

lva

lue

of

consi

der

ati

on

paid

by

the

Acq

uir

er,

excl

udin

gfe

esand

exp

ense

s.T

he

dollar

valu

ein

cludes

the

am

ount

paid

for

all

com

mon

stock

,co

mm

on

stock

equiv

ale

nts

,pre

ferr

edst

ock

,deb

t,opti

ons,

ass

ets,

warr

ants

,and

stake

purc

hase

sm

ade

wit

hin

six

month

sof

the

announce

men

tdate

of

the

transa

ctio

n.

Lia

bilit

ies

ass

um

edare

incl

uded

inth

eva

lue

ifth

eyare

publicl

ydis

close

d.

Pre

ferr

edst

ock

isonly

incl

uded

ifit

isb

eing

acq

uir

edas

part

of

a100%

acq

uis

itio

n.

Ifa

port

ion

of

the

consi

der

ati

on

paid

by

the

Acq

uir

eris

com

mon

stock

,th

est

ock

isva

lued

usi

ng

the

closi

ng

pri

ceon

the

last

full

tradin

gday

pri

or

toth

e

announce

men

tof

the

term

sof

the

stock

swap.

Ifth

eex

change

rati

oof

share

soff

ered

changes

,th

est

ock

isva

lued

base

don

its

closi

ng

pri

ceon

the

last

full

tradin

gdate

pri

or

toth

edate

of

the

exch

ange

rati

och

ange.

For

public

targ

et100%

acq

uis

itio

ns,

the

num

ber

of

share

sat

date

of

announce

men

t(C

AC

T)

is

use

d.

The

pre

miu

mpaid

isdefi

ned

as

the

Pre

miu

mof

off

erpri

ceto

targ

etcl

osi

ng

stock

pri

ce1

day

(1w

eek,

4w

eeks)

pri

or

toth

eori

gin

al

announce

men

t

date

,ex

pre

ssed

as

ap

erce

nta

ge.

Sourc

e:T

hom

son

Reu

ters

SD

CP

lati

num

.

Panel

A:

Deal

Info

rmati

on

Off

er

Str

uctu

reC

ash

Only

Hybri

dO

ther

Share

sU

nknow

nT

ota

l

Desc

ripti

on

No.

%of

Tot.

No.

%of

Tot.

No.

%of

Tot.

No.

%of

Tot.

No.

%of

Tot.

No.

%of

Tot.

Nbr.

of

Dea

ls903

48.6

%415

22.3

%80

4.3

%403

21.7

%58

3.1

%1,8

59

100.0

%C

om

ple

ted

Dea

ls746

40.1

%357

19.2

%67

3.6

%339

18.2

%33

1.8

%1,5

42

82.9

%H

ost

ile

Dea

ls35

1.9

%14

0.8

%3

0.2

%7

0.4

%4

0.2

%63

3.4

%Sam

eIn

dust

ryD

eals

53

2.9

%83

4.5

%6

0.3

%54

2.9

%5

0.3

%201

10.8

%C

hallen

ged

Dea

ls111

6.0

%55

3.0

%7

0.4

%32

1.7

%11

0.6

%216

11.6

%C

om

pet

ing

Bid

der

83

4.5

%32

1.7

%3

0.2

%20

1.1

%4

0.2

%142

7.6

%C

ollar

Dea

l4

0.2

%54

2.9

%3

0.2

%52

2.8

%7

0.4

%120

6.5

%T

erm

inati

on

Fee

698

37.5

%352

18.9

%51

2.7

%292

15.7

%29

1.6

%1,4

22

76.5

%B

idder

has

ato

ehold

42

2.3

%11

0.6

%2

0.1

%7

0.4

%3

0.2

%65

3.5

%

Panel

B:

Deal

Fin

ancia

ls

Off

er

Str

uctu

reC

ash

Only

Hybri

dO

ther

Share

sU

nknow

nT

ota

l

Desc

ripti

on

Mea

nSd

Mea

nSd

Mea

nSd

Mea

nSd

Mea

nSd

Mea

nSd

DV

al

(mil)

$2,2

42.0

$4,1

47.2

$5,8

80.9

$10,0

71.5

$5,0

74.2

$10,3

87.7

$5,4

29.8

$15,1

58.5

$1,6

35.7

$2,5

03.7

$3,8

48.4

$9,4

01.3

P1d

33.6

%31.7

%28.5

%27.5

%25.1

%40.5

%28.3

%39.5

%33.3

%29.6

%31.0

%33.1

%P

1w

36.6

%31.0

%32.4

%29.1

%29.5

%42.5

%33.6

%61.5

%33.4

%29.8

%34.7

%39.8

%P

4w

41.1

%35.6

%35.0

%32.4

%31.2

%46.1

%36.7

%45.3

%38.0

%33.6

%38.3

%37.7

%

29

Tab

le2:

Su

mm

ary

Sta

tist

ics

-O

pti

onT

rad

ing

Vol

um

e(W

ith

out

Zer

oV

olu

me

Ob

serv

atio

ns)

This

table

pre

sents

basi

csu

mm

ary

stati

stic

son

opti

on

tradin

gvolu

me,

excl

udin

gze

ro-v

olu

me

obse

rvati

ons,

stra

tified

by

tim

eto

expir

ati

on

and

dep

th-i

n-

money

nes

s.W

ecl

ass

ify

thre

egro

ups

of

tim

eto

expir

ati

on:

less

or

equalth

an

30

day

s,big

ger

than

30

but

less

or

equalth

an

60

day

sand

more

than

60

day

s.W

e

ass

ign

5gro

ups

for

dep

th-i

n-m

oney

nes

s,w

her

edep

th-i

n-m

oney

nes

sis

defi

ned

asS/K

,th

era

tio

of

the

stock

pri

ceS

over

the

stri

ke

pri

ceK

.D

eep

out-

of-

the-

money

(DO

TM

)co

rres

ponds

toS/K∈

[0,0.8

0]

for

calls

([1.2

0,∞

]fo

rputs

),out-

of-

the-

money

(OT

M)

corr

esp

onds

toS/K∈

]0.8

0,0.9

5]

for

calls

([1.0

5,1.2

0[

for

puts

),at-

the-

money

(AT

M)

corr

esp

onds

toS/K∈

]0.9

5,1.0

5[

for

calls

(]0.9

5,1.0

5[

for

puts

),in

-the-

money

(IT

M)

corr

esp

onds

toS/K∈

[1.0

5,1.2

0[

for

calls

(]0.8

0,0.9

5]

for

puts

),and

dee

pin

-the-

money

(DIT

M)

corr

esp

onds

toS/K∈

[1.2

0,∞

[fo

rca

lls

([0,0.8

0]

for

puts

).Sourc

e:O

pti

onM

etri

cs

Target

(N

=2,2

14,2

60)

Acquirer

(N

=3,5

82,3

94)

DITM

Mean

SD

Min

Med

p75

p90

Max

Panel

A:

All

opti

ons,

TT

E=

[0,3

0]

DO

TM

(3%

)246

1,9

73

120

76

300

94,1

77

OT

M(5

%)

370

1,9

90

141

164

596

88,0

86

AT

M(7

9%

)273

1,2

91

140

152

531

231,2

04

ITM

(5%

)356

6,2

14

120

80

333

539,4

82

DIT

M(5

%)

275

3,2

64

110

40

171

200,0

00

Tota

l(1

00%

)283

2,1

35

135

138

500

539,4

82

DITM

Mean

SD

Min

Med

p75

p90

Max

Panel

A:

All

opti

ons,

TT

E=

[0,3

0]

DO

TM

(10%

)127

594

120

71

231

27,3

77

OT

M(2

2%

)497

1,4

97

179

355

1,2

07

55,1

67

AT

M(2

6%

)1,0

84

3,0

38

1204

927

2,7

44

198,1

46

ITM

(23%

)398

5,2

09

142

175

624

679,6

20

DIT

M(1

6%

)214

3,2

86

116

54

191

300,8

41

Tota

l(1

00%

)547

3,3

61

152

279

1,1

46

679,6

20

Panel

B:

All

opti

ons,

TT

E=

]30,6

0]

DO

TM

(6%

)163

863

120

63

229

29,0

45

OT

M(9

%)

285

1,2

01

132

128

500

55,2

22

AT

M(7

1%

)184

855

125

95

328

71,8

22

ITM

(6%

)190

3,2

44

120

65

254

475,5

13

DIT

M(6

%)

208

5,2

88

110

37

137

523,0

53

Tota

l(1

00%

)194

1,7

87

123

90

316

523,0

53

Panel

B:

All

opti

ons,

TT

E=

]30,6

0]

DO

TM

(14%

)141

838

120

76

245

95,0

00

OT

M(2

7%

)384

1,3

88

169

269

830

94,5

52

AT

M(2

5%

)551

1,6

66

1101

425

1,2

99

90,4

97

ITM

(20%

)236

3,4

88

130

108

367

458,0

19

DIT

M(1

2%

)334

12,5

43

111

40

133

1,6

09,0

02

Tota

l(1

00%

)354

4,8

41

141

183

659

1,6

09,0

02

Panel

C:

All

opti

ons,

TT

E=

]60,...]

DO

TM

(25%

)117

1,0

35

115

45

143

339,7

51

OT

M(2

4%

)130

847

115

50

175

101,8

85

AT

M(2

0%

)131

845

115

50

180

116,4

16

ITM

(14%

)99

923

110

35

111

142,6

47

DIT

M(1

5%

)83

1,1

05

110

27

89

137,8

04

Tota

l(1

00%

)115

949

113

42

142

339,7

51

Panel

C:

All

opti

ons,

TT

E=

]60,...]

DO

TM

(24%

)112

774

120

59

176

137,4

30

OT

M(2

5%

)193

1,0

72

126

100

328

246,5

07

AT

M(1

8%

)208

927

128

108

382

88,1

31

ITM

(15%

)106

678

117

53

164

125,0

27

DIT

M(1

5%

)80

1,7

74

110

30

86

582,5

00

Tota

l(1

00%

)145

1,0

82

120

67

224

582,5

00

Panel

D:

Call

opti

ons,

TT

E=

[0,3

0]

DO

TM

(2%

)285

1,9

14

120

78

334

78,9

37

OT

M(4

%)

438

2,2

66

149

194

711

83,6

37

AT

M(7

8%

)302

1,4

61

144

171

592

231,2

04

ITM

(7%

)446

7,3

63

122

97

431

539,4

82

DIT

M(7

%)

220

3,1

61

110

40

152

200,0

00

Tota

l(1

00%

)311

2,5

64

137

150

545

539,4

82

Panel

D:

Call

opti

ons,

TT

E=

[0,3

0]

DO

TM

(6%

)96

434

113

49

185

18,5

53

OT

M(2

1%

)523

1,5

72

175

361

1,2

81

55,1

67

AT

M(2

5%

)1,2

85

3,5

98

1244

1,1

06

3,2

39

198,1

46

ITM

(24%

)499

6,5

95

150

215

750

679,6

20

DIT

M(2

3%

)192

3,3

79

117

58

192

300,8

41

Tota

l(1

00%

)603

4,1

43

150

283

1,2

33

679,6

20

Panel

E:

Call

opti

ons,

TT

E=

]30,6

0]

DO

TM

(4%

)168

790

120

70

250

25,0

00

OT

M(8

%)

313

1,2

92

137

144

533

36,9

55

AT

M(7

0%

)202

923

127

100

363

55,2

08

ITM

(7%

)213

3,8

28

120

73

293

475,5

13

DIT

M(8

%)

213

5,9

67

110

34

116

523,0

53

Tota

l(1

00%

)212

2,1

97

125

96

342

523,0

53

Panel

E:

Call

opti

ons,

TT

E=

]30,6

0]

DO

TM

(9%

)123

907

120

70

225

95,0

00

OT

M(2

7%

)425

1,4

71

172

296

935

53,0

60

AT

M(2

4%

)657

1,9

34

1123

528

1,5

93

90,4

97

ITM

(21%

)297

4,4

80

133

128

432

458,0

19

DIT

M(1

7%

)349

14,2

51

111

39

119

1,6

09,0

02

Tota

l(1

00%

)412

6,3

86

142

200

741

1,6

09,0

02

Panel

F:

Call

opti

ons,

TT

E=

]60,...]

DO

TM

(23%

)108

1,1

49

115

46

140

339,7

51

OT

M(2

6%

)124

829

115

50

169

101,8

85

AT

M(2

0%

)137

931

115

50

182

116,4

16

ITM

(13%

)108

1,0

83

110

34

111

142,6

47

DIT

M(1

6%

)82

1,2

49

110

25

79

137,8

04

Tota

l(1

00%

)114

1,0

40

112

41

139

339,7

51

Panel

F:

Call

opti

ons,

TT

E=

]60,...]

DO

TM

(19%

)111

744

120

63

187

137,4

30

OT

M(2

7%

)199

1,1

67

128

106

347

246,5

07

AT

M(1

8%

)214

954

130

115

398

88,1

31

ITM

(15%

)110

753

117

56

171

125,0

27

DIT

M(2

0%

)75

1,9

76

110

28

80

582,5

00

Tota

l(1

00%

)147

1,2

31

120

70

230

582,5

00

Panel

G:

Put

opti

ons,

TT

E=

[0,3

0]

DO

TM

(4%

)220

2,0

10

120

75

275

94,1

77

OT

M(6

%)

306

1,6

89

139

141

508

88,0

86

AT

M(8

1%

)234

1,0

03

135

130

455

58,8

19

ITM

(4%

)139

976

115

50

189

42,7

08

DIT

M(2

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485

3,6

27

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Tota

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431

100,0

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Panel

G:

Put

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DO

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90

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1,1

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AT

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2,1

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ITM

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1,6

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2,9

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105,0

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Put

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29,0

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54,0

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DO

TM

(21%

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795

122

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94,5

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1,9

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113

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50

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83,0

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AT

M(2

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115

50

175

56,0

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ITM

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40,9

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33

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70,0

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Panel

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Options

Mea

nVol

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Vol

Min

Vol

1st

pctile

5th

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75th

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95th

pctile

99th

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MaxVol

All

2,042

437

1,243

123.78

20

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16

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2,076

13,478

Calls

1,106

299

570

137.23

20

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65

543

2,517

6,161

Puts

936

316

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107.9

20

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17.5

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390

1,494

13,478

Acquirer

Obs

#Dea

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Options

Mea

nVol

Med

Vol

Min

Vol

1st

pctile

5th

pctile

25th

pctile

75th

pctile

95th

pctile

99th

pctile

MaxVol

All

1,847

204

776

110.36

14

11

15

50

394

1,829

16,837

Calls

954

145

364

126.03

12

11

14

53

437

1,899

16,837

Puts

893

155

412

93.61

16

11

25

50

370

1,829

5,004

Panel

B:1random

sample

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(acq

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TargetObs

#Dea

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Options

Mea

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Vol

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Vol

1st

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5th

pctile

25th

pctile

75th

pctile

95th

pctile

99th

pctile

MaxVol

All

3,412

574

1,901

57

10

11

15

32

200

813

5,000

Calls

1,813

351

941

64

11

11

15

40

232

893

5,000

Puts

1,599

387

960

49

10

11

15

30

182

759

3,000

Acquirer

Obs

#Dea

ls#

Options

Mea

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Med

Vol

Min

Vol

1st

pctile

5th

pctile

25th

pctile

75th

pctile

95th

pctile

99th

pctile

MaxVol

All

2,878

337

1,253

73

12

11

15

40

294

1,102

5,938

Calls

1,781

226

576

60

10

11

14

36

224

1,000

4,287

Puts

1,097

228

677

94

15

11

17

50

401

1,540

5,938

31

Table 4: Bivariate Kolmogorov-Smirnov Tests - Target

The table reports the test statistics from a generalization of the bivariate two-sample Kolmogorov Smirnov test based

on Fasano and Franceschini (Fasano and Franceschini (1987)) . The null hypothesis of the test is that two bi-variate

samples come from the same empirical distribution function. ∗∗∗, ∗∗ and ∗ denote statistical significance at the 1%,

5% and 10% level respectively.

Panel A: CallsFull Sample

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0279∗∗∗ 0.0482∗∗∗ 0.0616∗∗∗ 0.1007∗∗∗ 0.1592∗∗∗ 0.4070∗∗∗

[−24,−20] . . 0.0228∗∗∗ 0.0368∗∗∗ 0.0744∗∗∗ 0.1334∗∗∗ 0.3911∗∗∗

[−19,−15] . . . 0.0173∗∗ 0.0556∗∗∗ 0.1134∗∗∗ 0.3694∗∗∗

[−14,−10] . . . . 0.0410∗∗∗ 0.0988∗∗∗ 0.3581∗∗∗

[−9,−5] . . . . . 0.0606∗∗∗ 0.3256∗∗∗

[−4,−1] . . . . . . 0.2798∗∗∗

[0, 0] . . . . . . .TTE = [0,30]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0348 0.1255∗∗∗ 0.2157∗∗∗ 0.2750∗∗∗ 0.3388∗∗∗ 0.6102∗∗∗

[−24,−20] . . 0.1212∗∗∗ 0.2121∗∗∗ 0.2645∗∗∗ 0.3340∗∗∗ 0.6093∗∗∗

[−19,−15] . . . 0.0979∗∗∗ 0.1667∗∗∗ 0.2377∗∗∗ 0.5105∗∗∗

[−14,−10] . . . . 0.0979∗∗∗ 0.1700∗∗∗ 0.4408∗∗∗

[−9,−5] . . . . . 0.0867∗∗∗ 0.3607∗∗∗

[−4,−1] . . . . . . 0.2854∗∗∗

[0, 0] . . . . . . .TTE = ]30,60]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0605∗∗∗ 0.0859∗∗∗ 0.0905∗∗∗ 0.1341∗∗∗ 0.1843∗∗∗ 0.4324∗∗∗

[−24,−20] . . 0.0390∗∗ 0.0453∗∗∗ 0.0874∗∗∗ 0.1421∗∗∗ 0.3925∗∗∗

[−19,−15] . . . 0.0246 0.0628∗∗∗ 0.1111∗∗∗ 0.3746∗∗∗

[−14,−10] . . . . 0.0554∗∗∗ 0.1050∗∗∗ 0.3605∗∗∗

[−9,−5] . . . . . 0.0611∗∗∗ 0.3232∗∗∗

[−4,−1] . . . . . . 0.2885∗∗∗

[0, 0] . . . . . . .TTE = [60,...]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0227∗∗∗ 0.0323∗∗∗ 0.0364∗∗∗ 0.0675∗∗∗ 0.1195∗∗∗ 0.3897∗∗∗

[−24,−20] . . 0.0165∗ 0.0210∗∗∗ 0.0503∗∗∗ 0.1009∗∗∗ 0.3763∗∗∗

[−19,−15] . . . 0.0158∗ 0.0390∗∗∗ 0.0885∗∗∗ 0.3623∗∗∗

[−14,−10] . . . . 0.0350∗∗∗ 0.0853∗∗∗ 0.3599∗∗∗

[−9,−5] . . . . . 0.0549∗∗∗ 0.3324∗∗∗

[−4,−1] . . . . . . 0.2883∗∗∗

[0, 0] . . . . . . .Panel B: PutsFull Sample

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0331∗∗∗ 0.0414∗∗∗ 0.0382∗∗∗ 0.0607∗∗∗ 0.0820∗∗∗ 0.2760∗∗∗

[−24,−20] . . 0.0209∗∗ 0.0242∗∗∗ 0.0403∗∗∗ 0.0677∗∗∗ 0.2657∗∗∗

[−19,−15] . . . 0.0176∗ 0.0301∗∗∗ 0.0524∗∗∗ 0.2549∗∗∗

[−14,−10] . . . . 0.0295∗∗∗ 0.0561∗∗∗ 0.2564∗∗∗

[−9,−5] . . . . . 0.0389∗∗∗ 0.2351∗∗∗

[−4,−1] . . . . . . 0.2132∗∗∗

[0, 0] . . . . . . .TTE = [0,30]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0318 0.1246∗∗∗ 0.1978∗∗∗ 0.2886∗∗∗ 0.3400∗∗∗ 0.5275∗∗∗

[−24,−20] . . 0.1280∗∗∗ 0.1978∗∗∗ 0.2893∗∗∗ 0.3407∗∗∗ 0.5266∗∗∗

[−19,−15] . . . 0.1003∗∗∗ 0.1752∗∗∗ 0.2280∗∗∗ 0.4149∗∗∗

[−14,−10] . . . . 0.0961∗∗∗ 0.1484∗∗∗ 0.3397∗∗∗

[−9,−5] . . . . . 0.0653∗∗∗ 0.2509∗∗∗

[−4,−1] . . . . . . 0.2104∗∗∗

[0, 0] . . . . . . .TTE = ]30,60]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0670∗∗∗ 0.0975∗∗∗ 0.0907∗∗∗ 0.1228∗∗∗ 0.1355∗∗∗ 0.3370∗∗∗

[−24,−20] . . 0.0465∗∗ 0.0430∗ 0.0672∗∗∗ 0.0896∗∗∗ 0.3047∗∗∗

[−19,−15] . . . 0.0353 0.0484∗∗∗ 0.0747∗∗∗ 0.2895∗∗∗

[−14,−10] . . . . 0.0619∗∗∗ 0.0983∗∗∗ 0.3094∗∗∗

[−9,−5] . . . . . 0.0514∗∗ 0.2729∗∗∗

[−4,−1] . . . . . . 0.2361∗∗∗

[0, 0] . . . . . . .TTE = ]60,...]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0293∗∗∗ 0.0309∗∗∗ 0.0264∗∗ 0.0371∗∗∗ 0.0657∗∗∗ 0.2706∗∗∗

[−24,−20] . . 0.0288∗∗∗ 0.0288∗∗∗ 0.0337∗∗∗ 0.0553∗∗∗ 0.2703∗∗∗

[−19,−15] . . . 0.0187 0.0184∗ 0.0487∗∗∗ 0.2525∗∗∗

[−14,−10] . . . . 0.0175 0.0454∗∗∗ 0.2534∗∗∗

[−9,−5] . . . . . 0.0361∗∗∗ 0.2429∗∗∗

[−4,−1] . . . . . . 0.2235∗∗∗

[0, 0] . . . . . . .

32

Table 5: Bivariate Kolmogorov-Smirnov Tests - Acquirer

The table reports the test statistics from a generalization of the bivariate two-sample Kolmogorov Smirnov test based

on Fasano and Franceschini (Fasano and Franceschini (1987)) . The null hypothesis of the test is that two bi-variate

samples come from the same empirical distribution function. ∗∗∗, ∗∗ and ∗ denote statistical significance at the 1%,

5% and 10% level respectively.

Panel A: CallsFull Sample

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0098 0.0220∗∗∗ 0.0248∗∗∗ 0.0287∗∗∗ 0.0499∗∗∗ 0.1074∗∗∗

[−24,−20] . . 0.0207∗∗∗ 0.0267∗∗∗ 0.0267∗∗∗ 0.0475∗∗∗ 0.1051∗∗∗

[−19,−15] . . . 0.0091 0.0183∗∗∗ 0.0320∗∗∗ 0.0880∗∗∗

[−14,−10] . . . . 0.0154∗∗∗ 0.0291∗∗∗ 0.0867∗∗∗

[−9,−5] . . . . . 0.0239∗∗∗ 0.0847∗∗∗

[−4,−1] . . . . . . 0.0695∗∗∗

[0, 0] . . . . . . .TTE = [0,30]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0608 0.0866∗∗ 0.1154∗∗∗ 0.1259∗∗∗ 0.1374∗∗∗ 0.1732∗∗∗

[−24,−20] . . 0.0666 0.0877∗∗ 0.1040∗∗∗ 0.1100∗∗∗ 0.1625∗∗∗

[−19,−15] . . . 0.0443 0.0674∗∗∗ 0.0742∗∗∗ 0.1198∗∗∗

[−14,−10] . . . . 0.0370 0.0359 0.1077∗∗∗

[−9,−5] . . . . . 0.0222 0.1077∗∗∗

[−4,−1] . . . . . . 0.0982∗∗∗

[0, 0] . . . . . . .TTE = ]30,60]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0357∗ 0.0537∗∗∗ 0.0535∗∗∗ 0.0686∗∗∗ 0.0666∗∗∗ 0.1159∗∗∗

[−24,−20] . . 0.0394∗∗ 0.0429∗∗∗ 0.0462∗∗∗ 0.0461∗∗∗ 0.1133∗∗∗

[−19,−15] . . . 0.0159 0.0289∗ 0.0244 0.0840∗∗∗

[−14,−10] . . . . 0.0253 0.0265 0.0918∗∗∗

[−9,−5] . . . . . 0.0345∗∗ 0.0917∗∗∗

[−4,−1] . . . . . . 0.0779∗∗∗

[0, 0] . . . . . . .TTE = [60,...]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0100 0.0140∗∗ 0.0136∗∗ 0.0141∗∗∗ 0.0339∗∗∗ 0.0897∗∗∗

[−24,−20] . . 0.0142∗∗ 0.0133∗∗ 0.0138∗∗∗ 0.0340∗∗∗ 0.0890∗∗∗

[−19,−15] . . . 0.0103 0.0087∗∗∗ 0.0242∗∗∗ 0.0815∗∗∗

[−14,−10] . . . . 0.0121∗∗∗ 0.0283∗∗∗ 0.0839∗∗∗

[−9,−5] . . . . . 0.0247∗∗∗ 0.0841∗∗∗

[−4,−1] . . . . . . 0.0678∗∗∗

[0, 0] . . . . . . .Panel B: PutsFull Sample

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0165∗∗ 0.0205∗∗∗ 0.0232∗∗∗ 0.0356∗∗∗ 0.0483∗∗∗ 0.1174∗∗∗

[−24,−20] . . 0.0163∗∗ 0.0192∗∗∗ 0.0293∗∗∗ 0.0405∗∗∗ 0.1072∗∗∗

[−19,−15] . . . 0.0149∗∗ 0.0238∗∗∗ 0.0357∗∗∗ 0.1030∗∗∗

[−14,−10] . . . . 0.0172∗∗∗ 0.0305∗∗∗ 0.0975∗∗∗

[−9,−5] . . . . . 0.0218∗∗∗ 0.0860∗∗∗

[−4,−1] . . . . . . 0.0726∗∗∗

[0, 0] . . . . . . .TTE = [0,30]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0668 0.0739 0.0712 0.0843∗∗∗ 0.1283∗∗∗ 0.2036∗∗∗

[−24,−20] . . 0.0774 0.0640 0.0713∗∗∗ 0.1159∗∗∗ 0.1861∗∗∗

[−19,−15] . . . 0.0393 0.0769∗∗∗ 0.1034∗∗∗ 0.1798∗∗∗

[−14,−10] . . . . 0.0616∗∗∗ 0.0917∗∗∗ 0.1547∗∗∗

[−9,−5] . . . . . 0.0624∗∗∗ 0.1390∗∗∗

[−4,−1] . . . . . . 0.0928∗∗∗

[0, 0] . . . . . . .TTE = ]30,60]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0331 0.0382∗ 0.0450∗∗ 0.0577∗∗∗ 0.0663∗∗∗ 0.1235∗∗∗

[−24,−20] . . 0.0460∗∗∗ 0.0410∗∗ 0.0556∗∗∗ 0.0642∗∗∗ 0.1294∗∗∗

[−19,−15] . . . 0.0198 0.0381∗∗ 0.0379∗∗ 0.0972∗∗∗

[−14,−10] . . . . 0.0295 0.0350∗ 0.1038∗∗∗

[−9,−5] . . . . . 0.0318 0.0923∗∗∗

[−4,−1] . . . . . . 0.0828∗∗∗

[0, 0] . . . . . . .TTE = ]60,...]

Event Window [−29,−25] [−24,−20] [−19,−15] [−14,−10] [−9,−5] [−4,−1] [0, 0][−29,−25] . 0.0153∗ 0.0160∗∗ 0.0126 0.0182∗∗ 0.0268∗∗∗ 0.0919∗∗∗

[−24,−20] . . 0.0157∗∗ 0.0205∗∗∗ 0.0172∗∗ 0.0224∗∗∗ 0.0843∗∗∗

[−19,−15] . . . 0.0139 0.0128 0.0166∗ 0.0885∗∗∗

[−14,−10] . . . . 0.0155∗∗ 0.0243∗∗∗ 0.0914∗∗∗

[−9,−5] . . . . . 0.0137 0.0839∗∗∗

[−4,−1] . . . . . . 0.0774∗∗∗

[0, 0] . . . . . . .

33

Table 6: Positive Abnormal Trading Volume

Panel A reports the number and frequency of events with positive cumulative abnormal volume, as well as the t-

statistic on the average cumulative abnormal volume. Panel B reports the t-test statistics for the differences in the

average cumulative abnormal volumes across moneyness categories. We report heteroscedasticity-robust standard

errors. For the market model, the market option volume is defined as either the mean or the median of the total daily

trading volume across all options (respectively calls or puts) in the OptionMetrics database. The estimation window

starts 90 days before the announcement date and runs until 30 days before the event. The event window stretches

from 30 days before until one day prior to the event.

Panel A

Market Model (Median) Market Model (Mean) Constant Mean Model

Option Type All Calls Puts All Calls Puts All Calls Puts

All Options - TargetSign.t-stat 5% (#) 462 490 271 455 472 276 467 492 319Sign.t-stat 5% (freq.) 0.25 0.26 0.15 0.24 0.25 0.15 0.25 0.26 0.17E [CAV ] 15266.93 11969.28 3471.78 12955.74 10202.45 2688.79 14904.28 11546.02 3357.93t ¯CAV 5.19 5.69 3.70 4.33 4.70 2.72 5.12 5.51 3.59OTM Options - TargetSign.t-stat 5% (#) 405 383 387 394 383 397 462 572 591Sign.t-stat 5% (freq.) 0.22 0.21 0.21 0.21 0.21 0.21 0.25 0.31 0.32E [CAV ] 5650.09 3797.47 1859.50 5271.57 3581.55 1689.58 5477.21 3662.97 1814.23t ¯CAV 5.27 5.52 4.04 5.56 5.56 4.07 5.58 5.58 4.25ATM Options - TargetSign.t-stat 5% (#) 298 300 254 278 283 255 408 420 498Sign.t-stat 5% (freq.) 0.16 0.16 0.14 0.15 0.15 0.14 0.22 0.23 0.27E [CAV ] 1246.45 1059.16 188.04 1246.45 753.14 129.54 1307.18 1059.04 248.14t ¯CAV 1.85 2.34 0.79 1.14 1.45 0.49 1.92 2.27 1.00ITM Options - TargetSign.t-stat 5% (#) 358 448 316 354 434 317 424 596 619Sign.t-stat 5% (freq.) 0.19 0.24 0.17 0.19 0.23 0.17 0.23 0.32 0.33E [CAV ] 2804.58 1701.87 1109.71 2724.04 1644.19 1057.57 2791.03 1694.86 1096.17t ¯CAV 4.91 7.08 2.45 5.15 7 2.52 5.18 7.10 2.53All Options - AcquirerSign.t-stat 5% (#) 118 110 92 108 98 91 111 107 99Sign.t-stat 5% (freq.) 0.15 0.14 0.12 0.14 0.12 0.11 0.14 0.14 0.13E [CAV ] 934.35 310.10 796.90 -8447.54 -8806.43 -2606 -893.69 -1458.84 565.16t ¯CAV 0.08 0.05 0.17 -0.78 -0.99 -0.60 -0.08 -0.21 0.12OTM Options - AcquirerSign.t-stat 5% (#) 154 134 148 151 122 141 169 182 181Sign.t-stat 5% (freq.) 0.19 0.17 0.19 0.19 0.15 0.18 0.21 0.23 0.23E [CAV ] 11753.22 5945.83 5634.11 10629.36 5485.38 5134.54 11667.89 5981.59 5686.30t ¯CAV 5.75 4.96 5.13 5.65 4.65 4.84 6.32 5.20 5.58ATM Options - AcquirerSign.t-stat 5% (#) 247 235 163 230 225 154 254 240 210Sign.t-stat 5% (freq.) 0.31 0.30 0.21 0.29 0.28 0.19 0.32 0.30 0.27E [CAV ] 11969.14 7713.45 4231.43 11969.14 7122.31 4030.29 12082.69 7734.02 4348.66t ¯CAV 6.05 5.98 5.45 5.58 5.40 5.30 6.12 5.96 5.58ITM Options - AcquirerSign.t-stat 5% (#) 133 133 142 123 129 133 139 199 211Sign.t-stat 5% (freq.) 0.17 0.17 0.18 0.16 0.16 0.17 0.18 0.25 0.27E [CAV ] 6971.36 4975.12 2113.15 6137.50 4135.27 1562.08 6761.78 4827.16 1934.62t ¯CAV 3.12 2.79 1.56 2.77 2.24 1.15 3.04 2.73 1.43Panel B

Statistics Diff s.e. p-val Diff s.e. p-val Diff s.e. p-val

All Options - TargetOTM-ATM 4403.64 995.00 0.00 4414.89 1001.70 0.00 4170.03 965.00 0.00OTM-ITM 2845.51 679.97 0.00 2547.53 625.35 0.00 2686.17 644.32 0.00ATM-ITM -1558.13 768.04 0.04 -1867.35 870.18 0.03 -1483.86 803.99 0.07Call Options - TargetOTM-ATM 2738.31 640.40 0.00 2828.41 697.69 0.00 2603.93 655.36 0.00OTM-ITM 2095.60 609.21 0.00 1937.35 577.47 0.00 1968.11 587.85 0.00ATM-ITM -642.71 454.39 0.16 -891.06 514.97 0.08 -635.82 462.95 0.17Put Options - TargetOTM-ATM 1671.46 478.39 0.00 1560.04 443.08 0.00 1566.10 449.78 0.00OTM-ITM 749.79 300.46 0.01 632.01 313.97 0.04 718.06 310.18 0.02ATM-ITM -921.67 500.32 0.07 -928.03 499.72 0.06 -848.04 498.29 0.09All Options - AcquirerOTM-ATM -215.92 1607.93 0.89 -563.34 1491.05 0.71 -414.79 1512.74 0.78OTM-ITM 4781.86 2466.52 0.05 4491.87 2455.85 0.07 4906.11 2391.71 0.04ATM-ITM 4997.78 2424.68 0.04 5055.21 2517.13 0.04 5320.90 2447.53 0.03Call Options - AcquirerOTM-ATM -1767.63 1345.59 0.19 -1636.92 1321.14 0.22 -1752.44 1340.94 0.19OTM-ITM 970.71 2054.63 0.64 1350.12 2065.22 0.51 1154.43 2017.51 0.57ATM-ITM 2738.33 1813.65 0.13 2987.04 1916.52 0.12 2906.86 1816.19 0.11Put Options - AcquirerOTM-ATM 1402.68 881.56 0.11 1104.24 940.74 0.24 1337.64 806.92 0.10OTM-ITM 3520.95 1674.58 0.04 3572.46 1779.40 0.05 3751.68 1671.35 0.03ATM-ITM 2118.28 1383.63 0.13 2468.21 1434.58 0.09 2414.04 1394.93 0.08

34

Table 7: Positive Excess Implied Volatility

This table reports the results from a classical event study where we test whether there was statistically significant

positive excess implied volatility in anticipation of the M&A announcement. Two different models are used: excess

implied volatility relative to a constant-mean-volatility model, as well as a market model, where we use as the

market-implied volatility the CBOE S&P500 Volatility Index (VIX). The estimation window starts 90 days before

the announcement date and runs until 30 days before the event. The event window stretches from 30 days before until

one day prior to the event. Panel A reports the number and frequency of events with positive excess implied volatility,

as well as the t-statistic on the average cumulative excess implied volatility. Panel B reports the t-test statistics for

the differences in means of the average cumulative excess implied volatilities across moneyness categories. We report

heteroscedasticity-robust standard errors.

Panel A

Market Model (VIX) Constant Mean Model

Option Type Calls Puts Calls Puts

30-day ATM Implied Volatility (|δ| = 50) - TargetSign.t-stat 5% (#) 812 798 794 766Sign.t-stat 5% (freq.) 0.44 0.43 0.43 0.41E [CEIV ] 0.52 0.40 0.46 0.37t ¯CEIV 6.34 5.00 5.27 4.2930-day ITM Implied Volatility (|δ| = 80) - TargetSign.t-stat 5% (#) 733 756 712 762Sign.t-stat 5% (freq.) 0.39 0.41 0.38 0.41E [CEIV ] 0.51 0.42 0.46 0.41t ¯CEIV 5.66 4.81 4.72 4.3730-day OTM Implied Volatility (|δ| = 20) - TargetSign.t-stat 5% (#) 791 671 772 668Sign.t-stat 5% (freq.) 0.43 0.36 0.42 0.36E [CEIV ] 0.49 0.35 0.43 0.29t ¯CEIV 6.02 4.01 5.01 3.2630-day ATM Implied Volatility (|δ| = 50) - AcquirerSign.t-stat 5% (#) 305 302 265 264Sign.t-stat 5% (freq.) 0.39 0.38 0.33 0.33E [CEIV ] 0.02 0.00 -0.01 -0.04t ¯CEIV 0.24 0.02 -0.18 -0.5430-day ITM Implied Volatility (|δ| = 80) - AcquirerSign.t-stat 5% (#) 258 284 250 262Sign.t-stat 5% (freq.) 0.33 0.36 0.32 0.33E [CEIV ] 0.03 0.02 0.01 -0.02t ¯CEIV 0.35 0.25 0.10 -0.1930-day OTM Implied Volatility (|δ| = 20) - AcquirerSign.t-stat 5% (#) 297 257 265 223Sign.t-stat 5% (freq.) 0.38 0.32 0.33 0.28E [CEIV ] -0.02 0.02 -0.04 -0.07t ¯CEIV -0.33 0.27 -0.52 -0.83

Panel B

Market Model (VIX) Constant Mean Model

Statistics Diff s.e. p-val Diff s.e. p-val

Call Options - TargetITM − OTM 0.02 0.05 0.68 0.02 0.05 0.64Put Options - TargetITM − OTM 0.08 0.06 0.17 0.12 0.05 0.02Call Options vs. Put Options - TargetCITM − PITM 0.09 0.05 0.10 0.04 0.05 0.38CITM − POTM 0.16 0.07 0.02 0.16 0.06 0.01COTM − PITM 0.06 0.06 0.26 0.01 0.07 0.89COTM − POTM 0.14 0.05 0.01 0.14 0.05 0.00Call Options - AcquirerITM − OTM 0.05 0.04 0.23 0.05 0.04 0.19Put Options - AcquirerITM − OTM -0.00 0.06 0.97 0.05 0.04 0.22Call Options vs. Put Options - AcquirerCITM − PITM 0.01 0.04 0.87 0.03 0.03 0.43CITM − POTM 0.00 0.07 0.95 0.08 0.05 0.09COTM − PITM -0.04 0.05 0.36 -0.06 0.07 0.41COTM − POTM -0.05 0.06 0.44 0.03 0.03 0.41

35

Tab

le8:

Non

-par

amet

ric

Vol

um

e-at

-Ris

k

Panel

Are

port

sst

ati

stic

s(N

um

ber

of

obse

rvati

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freq

uen

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mea

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gvolu

me

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tradin

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ng

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to5

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ab

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mean

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sdm

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ax

Pan

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1:Vt>

0|V

t−1

=0

All

Op

tion

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0,01

30.

075

70.0

310

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ls51

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10

1,0

92.3

01

415,0

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0.1

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2,8

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11,6

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02

Pu

ts38

0,63

20.

032

72.0

010

517.1

81

79,0

04

576,4

70

0.0

96

81.3

911

611

1100,1

03

Pan

elB

1:Vt>

0|V

t−1

=0,Vt−

2=

0A

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pti

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619,

190

0.05

162

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10

691.1

91

200,0

00

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0.0

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64.9

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Cal

ls34

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028

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395,0

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Pu

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023

64.6

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elC

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61.2

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Pu

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62.1

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489.3

21

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0.0

46

63.3

510

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160,0

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Pan

elD

1:Vt>

0|V

t−1

=0,Vt−

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3=

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200,0

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36

60.6

910

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11,6

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Cal

ls20

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0.03

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970.2

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Pu

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36

Tab

le9:

Non

-par

amet

ric

Vol

um

e-at

-Ris

k

This

table

rep

ort

sth

etr

adin

gle

vel

sν

such

that

the

pro

babilit

yof

exce

edin

ga

tradin

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sult

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men

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ple

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Panel

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me,

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x1

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x1

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25

50

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%ν

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52

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x1

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Pan

elA

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(Vt≥ν|∑ n i

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%ν

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52

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x1

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2550

75

90

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ν770

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50

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Pan

elA

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(Vt≥ν|∑ n i

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0;i

=4)≤x

%ν

743

150

7525

10

52

11

x1

510

2550

75

90

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ν750

131

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10

41

11

x1

510

25

50

75

90

95

99

Pan

elA

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(Vt≥ν|∑ n i

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i=

0;i

=5)≤x

%ν

710

126

6522

10

41

11

x1

510

2550

75

90

95

99

ν700

143

70

25

10

52

11

x1

510

25

50

75

90

95

99

Pan

el

B:

Ran

dom

Sam

ple

Targ

et

Acqu

irer

Pan

elB

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(Vt≥ν|∑ n i

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%ν

1525

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125

4012

52

11

x1

510

2550

75

90

95

99

ν1492

307

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44

13

52

11

x1

510

25

50

75

90

95

99

Pan

elB

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(Vt≥ν|∑ n i

Vt−

i=

0;i

=2)≤x

%ν

1187

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3410

52

11

x1

510

2550

75

90

95

99

ν1127

235

107

35

10

52

11

x1

510

25

50

75

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95

99

Pan

elB

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(Vt≥ν|∑ n i

Vt−

i=

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=3)≤x

%ν

1044

213

100

3010

52

11

x1

510

2550

75

90

95

99

ν1005

202

100

30

10

42

11

x1

510

25

50

75

90

95

99

Pan

elB

.4:P

(Vt≥ν|∑ n i

Vt−

i=

0;i

=4)≤x

%ν

1000

200

100

3010

52

11

x1

510

2550

75

90

95

99

ν990

196

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30

10

42

11

x1

510

25

50

75

90

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Pan

elB

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(Vt≥ν|∑ n i

Vt−

i=

0;i

=5)≤x

%ν

902

191

9030

105

21

1x

15

1025

5075

90

95

99

ν843

175

85

28

10

41

11

x1

510

25

50

75

90

95

99

37

Figure 1: Trading Volumes around Announcement Dates

These graphs plot the average option trading volume (left axis) and the average number of options used in the

calculation (right axis) 60 days before and after the announcement date. Graphs (1a) and (1b) plot the average call

trading volume for respectively the Acquirer and target. Graphs (1c) and (1d) plot the average put trading volume

for respectively the Acquirer and target. For each deal, the total trading volume on each day across calls and puts

is calculated. The bars represent the average daily total trading volume across all deals. Volume is defined as the

number of option contracts.

(a) (b)

(c) (d)

38

Figure 2: Volume vs Depth-in-Moneyness across Event Windows

These graphs illustrate local polynomial functions fitted to the volume-moneyness observations across 7 different

event windows and for the full sample excluding the event windows. Graphs (2a) and (2b) show the polynomial fits

for respectively call and put options on the target companies. Graphs (2c) and (2d) illustrate the polynomial fits for

respectively call and put options on the acquiring companies. Volume is defined as the number of option contracts.

Depth-in-moneyness is defined as the ratio of the stock price over the strike price. Volume is winsorized at the upper

99th percentile. Source: OptionMetrics

(a)

050

100

150

Vol

ume

(# C

ontr

acts

) -

Pol

ynom

ial F

it

0 .5 1 1.5 2S/K

Full Sample ex-EW [-29,-25] [-24,-20] [-19,-15][-14,-10] [-9,-5] [-4,-1] [0,0]

Call Options - Target

(b)

020

4060

8010

0V

olum

e (#

Con

trac

ts)

- P

olyn

omia

l Fit

0 .5 1 1.5 2S/K

Full Sample ex-EW [-29,-25] [-24,-20] [-19,-15][-14,-10] [-9,-5] [-4,-1] [0,0]

Put Options - Target

(c)

050

100

150

200

Vol

ume

(# C

ontr

acts

) -

Pol

ynom

ial F

it

0 .5 1 1.5 2S/K

Full Sample ex-EW [-29,-25] [-24,-20] [-19,-15][-14,-10] [-9,-5] [-4,-1] [0,0]

Call Options - Acquirer

(d)

050

100

150

Vol

ume

(# C

ontr

acts

) -

Pol

ynom

ial F

it

0 .5 1 1.5 2S/K

Full Sample ex-EW [-29,-25] [-24,-20] [-19,-15][-14,-10] [-9,-5] [-4,-1] [0,0]

Put Options - Acquirer

39

Figure 3: Trading Volume Distribution around Announcement Dates

These graphs plot distributional statistics of options trading volume, defined as the number of traded contracts, from

30 days before until 20 days after the announcement date. The left axis plots the 90th and the 95th percentile of the

volume distribution, while the right axis refers to the interquartile range. Graphs (3a) and (3b) refer to respectively

call and put volume for target companies. Graphs (3c) and (3d) refer to respectively call and put volume for acquiring

companies. Source: OptionMetrics

(a)

5010

015

020

0In

terq

uart

ile R

ange

050

010

0015

0020

00V

olum

e (#

Con

trac

ts)

-30 -20 -10 0 10 20Event Time

90th percentile 95th percentile Interquartile Range

Call Options Volume Distribution - Target

(b)

4060

8010

012

014

0In

terq

uart

ile R

ange

050

010

0015

0020

00V

olum

e (#

Con

trac

ts)

-30 -20 -10 0 10 20Event Time

90th percentile 95th percentile Interquartile Range

Put Options Volume Distribution - Target

(c)

8010

012

014

0In

terq

uart

ile R

ange

200

400

600

800

1000

1200

Vol

ume

(# C

ontr

acts

)

-30 -20 -10 0 10 20Event Time

90th percentile 95th percentile Interquartile Range

Call Options Volume Distribution - Acquirer

(d)

6080

100

120

Inte

rqua

rtile

Ran

ge

200

400

600

800

1000

1200

Vol

ume

(# C

ontr

acts

)

-30 -20 -10 0 10 20Event Time

90th percentile 95th percentile Interquartile Range

Put Options Volume Distribution - Acquirer

40

Figure 4: Abnormal Trading Volumes Before Announcement Dates

Graph 4c plots the average abnormal trading volume for respectively all equity options, call and put options over the

30 days preceding the announcement date. Graph 4d reflects the average cumulative abnormal trading volume over

the same event period.

(a)

050

010

0015

0020

00A

vera

ge A

bnor

mal

Vol

ume

(# C

ontr

acts

)

-30 -20 -10 0Event Time

All Call Put

Average Abnormal Volume

(b)

050

0010

000

1500

0A

vera

ge C

umul

ativ

e A

bnor

mal

Vol

ume

(# C

ontr

acts

)

-30 -20 -10 0Event Time

All Call Put

Average Cumulative Abnormal Volume

(c)

-100

0-5

000

500

1000

1500

Ave

rage

Abn

orm

al V

olum

e (#

Con

trac

ts)

-30 -20 -10 0Event Time

All Call Put

Average Abnormal Volume

(d)

-300

0-2

000

-100

00

1000

Cum

ulat

ive

Ave

rage

Abn

orm

al V

olum

e (#

Con

trac

ts)

-30 -20 -10 0Event Time

All Call Put

Average Cumulative Abnormal Volume

41

Figure 5: Excess Implied Volatility Before Announcement Dates

Graphs 5a (target) and 5c (acquirer) plot the average excess implied volatility relative to the VIX index for the

30-day ATM implied volatility from respectively call and put options, over the 30 days preceding the announcement

date. Graphs 5b (target) and 5b (acquirer) reflect the average cumulative excess implied volatility over the same

event period. Source: OptionMetrics.

(a)

.01

.02

.03

.04

.05

-30 -20 -10 0Event Time

Call Put

Average Excess Implied Volatility

(b)

0.1

.2.3

.4.5

-30 -20 -10 0Event Time

Call Put

Average Cumulative Excess Implied Volatility

(c)

-.00

4-.

002

0.0

02.0

04.0

06A

vera

ge E

xces

s Im

plie

d V

olat

ility

-30 -20 -10 0Event Time

Call Put

Average Excess Implied Volatility

(d)

-.00

50

.005

.01

.015

.02

Ave

rage

Cum

ulat

ive

Exc

ess

Impl

ied

Vol

atili

ty

-30 -20 -10 0Event Time

Call Put

Average Cumulative Excess Implied Volatility

42

Figure 6: Information Dispersion - Bid-Ask Spreads

These graphs illustrates the evolution of the percentage bid-ask spread from 90 days before the announcement date

to 90 days after the announcement date. The left column illustrates the results for target companies, while the

right graph illustrates results for the acquiring companies. Graphs (6a) and (6b) are simple average percentage

bid-ask spreads, Figures (6c) and (6d) reports the average omega-adjusted bid-ask spread. All bid and ask prices are

right-winsorized at the top 99.9th percrentile. Source: OptionMetrics.

(a)

.3.4

.5.6

.7.8

Per

cen

tag

e B

id-A

sk S

pre

ad

-80 -60 -40 -20 0 20 40 60 80Event Time

Percentage Bid-Ask Spread, Target

(b)

.125

.13

.135

.14

.145

.15

Per

cen

tag

e B

id-A

sk S

pre

ad

-80 -60 -40 -20 0 20 40 60 80Event Time

Percentage Bid-Ask Spread, Acquirer

(c)

.2.2

5.3

.35

.4.4

5

Om

ega-

Ad

just

ed B

id-A

sk S

pre

ad

-100 -50 0 50 100Event Time

Omega-Adjusted Bid-Ask Spread, Target

(d)

.2.2

5.3

.35

Om

ega-

Ad

just

ed B

id-A

sk S

pre

ad

-100 -50 0 50 100Event Time

Omega-Adjusted Bid-Ask Spread, Acquirer

43

Figure 7: Implied Volatility Smile and Term Structure

These graphs characterize the evolution of implied volatility around M&A announcement dates. Each node represents

the cross-sectional average within a time window defined on the x-axis. The top panel is for the target firms while the

bottom panel represents the data for the acquirier firms. Figures (7a) and (7c) plot the evolution of two additional

IV skewness measures for respectively the target and acquirer: the difference between the OTM IV for puts with a

delta of 25 and OTM IV for calls with a delta of 25, scaled by the average ATM IV with a delta of 50 (left axis); the

difference between OTM IV for puts with a delta of 20 and ATM IV for calls with a delta of 50 (right axis). Figures

(7b) and (??) depict the IV term structure for call options, defined as the difference between the ATM call options

(delta = 50) for respectively 91 and 30 days to maturity (left axis), and the IV term structure for put options, defined

as the difference between the ATM put options (delta = 50) for respectively 91 and 30 days to maturity (left axis).

Source: OptionMetrics.

(a).0

4.0

5.0

6.0

7.0

8.0

9IV

p-de

lta20

- IV

c-de

lta50

.01

.02

.03

.04

.05

.06

(IV

p-de

lta25

- IV

c-de

lta25

)/de

lta50

]...,-30] [-24,-20] [-14,-10] [-4,-1] [1,5] [11,15] [21,25] [31,...[Event Time

Carr-Wu (Mean) Cremers et al. (Mean)

IV-Skew (30-day options)

(b)

-.05

-.04

-.03

-.02

-.01

IVp-

delta

50/9

1 -

IVp-

delta

50/3

0

-.05

-.04

-.03

-.02

-.01

IVc-

delta

50/9

1 -

IVc-

delta

50/3

0

]...,-30] [-24,-20] [-14,-10] [-4,-1] [1,5] [11,15] [21,25] [31,...[Event Time

IV-Term Calls (Mean) IV-Term Puts (Mean)

IV-Term Structure (91day - 30day options)

(c)

.044

.046

.048

.05

.052

.054

IVp-

delta

20 -

IVc-

delta

50

.02

.022

.024

.026

.028

(IV

p-de

lta25

- IV

c-de

lta25

)/de

lta50

]...,-30] [-24,-20] [-14,-10] [-4,-1] [1,5] [11,15] [21,25] [31,...[Event Time

Carr-Wu (Mean) Cremers et al. (Mean)

IV-Skew (30-day options)

(d)

-.01

2-.

01-.

008

-.00

6-.

004

IVp-

delta

50/9

1 -

IVp-

delta

50/3

0

-.01

4-.

012

-.01

-.00

8-.

006

IVc-

delta

50/9

1 -

IVc-

delta

50/3

0

]...,-30] [-24,-20] [-14,-10] [-4,-1] [1,5] [11,15] [21,25] [31,...[Event Time

IV-Term Calls (Mean) IV-Term Puts (Mean)

IV-Term Structure (91day - 30day options)

44