When It Cannot Get Better or Worse:

The Asymmetric Impact of Good and Bad News

on Bond Returns in Expansions and Recessions∗

Alessandro Beber Michael W. Brandt

Amsterdam Business School Fuqua School of BusinessUniversity of Amsterdam Duke University

and NBER

Abstract

We examine empirically the response of bond returns and their volatility togood and bad macroeconomic news in economic expansions and recessions. Wefind that the information content of macroeconomic announcements is mostimportant when it contains bad news for bond returns in expansions and, toa lesser extent, when it contains good news for bond returns in contractions. Inparticular, we observe the strongest bond market response to bad news in therelease of non-farm payrolls in expansions. During recessions, inflation news arerelatively more important when they contain good news. We also document thatmacroeconomic news impacts substantially the volatility of bond returns at allmaturities by increasing jump intensities and by altering the distribution of thejump size. While a large proportion of employment news results in a jump inexpansions at all maturities, inflation news becomes more important in recessionsand particularly at medium maturities.

∗We thank two anonymous referees, Andrew Ang, Jean Boivin, Jean-Pierre Danthine, Joost Driessen,Marc Giannoni, Pascal St-Amour, Josef Zechner (the editor) and seminar participants at Cass BusinessSchool, Columbia University, and University of Lausanne for helpful comments. We also thank Clara Vegaand Informa Global Markets for providing some of the announcement data.

1. Introduction

Investors receive every day a wealth of information, ranging from news about corporate

cash flows to news about the economic fundamentals. Investors process this information

and update their expectations of the economy’s future growth, inflation, and discount rates,

thus affecting asset prices. However, some macroeconomic information is more relevant than

other, and the same information can have a different importance at different stages in the

business cycle. Moreover, the same information can have a different impact on different

assets and, even for the same asset, a different impact on returns than on volatility.

This paper studies these different aspects of information jointly. In particular, we examine

empirically the response of U.S. Treasury bond returns and their volatility to good and bad

macroeconomic news in economic expansions and recessions. We will uncover interesting

asymmetric effects when investors and monetary authorities are confronted with different

type of news at different times.

The design of our analysis is straightforward. We first study the effect of macroeconomic

announcements on bond returns using a regression specification that allows for an asymmetric

impact of good and bad macroeconomic news in different phases of the business cycle. We

find that the information content of the announcements is most important for bond returns

when it contains bad news for the bond market in expansions and, to a lesser extent, good

news for the bond market in contractions. We observe the strongest bond market response

to bad news in the release of non-farm payrolls during expansionary periods. This effect is

apparent at all maturities. During recessionary periods, inflation news are relatively more

important when they contain good news and particularly at short maturities.

Motivated by the strong economic significance of these results, we then formulate and

estimate a statistical model of bond returns incorporating jumps that depend explicitly

on the timing and information content of scheduled macroeconomic news. The estimates

of this model show that macroeconomic news has a substantial impact on bond volatility

by increasing jump intensities and by affecting the distributions of the jump size. The

relation between macroeconomic news and jumps, however, depends on the combination

of the announcement type, the maturity of the bond, and the phase of the business cycle.

Specifically, the proportion of releases resulting in a jump decreases dramatically for short

maturities and is generally lower in recessions. Jumps on announcement days are associated

predominantly with non-farm payroll releases, but inflation news becomes relatively more

associated with jumps in recessions and at medium maturities.

The estimation of the model of bond returns with jumps confirms the findings from the

regression analysis that the response of the term structure to the macroeconomic release

1

depends on the multifaceted aspects of information. More importantly, given the rich

structure of the model that we hypothesize, the estimates provide a detailed account of

jump dynamics as the conduit through which macroeconomic information enters the bond

market.

A number of papers investigate the effect of macroeconomic announcements on

financial markets along different lines. Fleming and Remolona (1999) study the effect of

macroeconomic news on bond returns, while Jones, Lamont and Lumsdaine (1998) document

the effect on bond volatility.1 However, they do not condition their analysis on different

phases of the business cycle and they do not disentangle the effect of good versus bad news.

Recent work by Boyd, Hu and Jagannathan (2005) investigates the response of stock and

bond market returns to civilian unemployment rate news in expansions and recessions, but

they do not consider the release of non-farm payrolls and, most importantly, they do not

separate the effect of good versus bad news.2 Andersen, Bollerslev, Diebold and Vega (2003)

show that the responses of foreign exchange rates to macroeconomic news vary with the

sign of the news. However, their sample period is all in an expansion phase of the business

cycle. The contribution of our paper is thus to show that the effect of macroeconomic

announcements on bond market returns and volatility depends jointly on the type of news,

on the sign of the news, and on the phase of the business cycle.

Recent theoretical work describes the asymmetric response of stock prices to news in

good or bad times (e.g., Veronesi, 1999). The driving mechanism is the agents’ uncertainty

about the state of the economy and the associated learning process. Specifically, investors

believe that the economy follows a two-state regime-switching process, with low and high

states corresponding to recessions and expansions. Agents solve a signal extraction problem

to determine the probability of being in the high state. Suppose that investors believe

that the high state almost surely prevails. Then if bad news arrives, two things happen:

expected future asset values decrease, and second, state risk increases. Risk-averse investors

require additional returns for bearing this additional risk; hence they require an additional

discount on the asset price, which drops by more than it would in a present-value model.

In the bond market, the response to macroeconomic news is peculiar for the key role of

the monetary authority that has direct effects on short-term yields and some influence on

1Other papers document the effect of macroeconomic news on bond returns or volatility for differentsample periods, announcement sets, underlying assets. Recent work by Christiansen and Ranaldo (2007)and Brenner, Pasquariello, and Subrahmanyam (2006) focus instead on the effect of macroeconomic newson comovements of stock and bond markets.

2Andersen, Bollerslev, Diebold and Vega (2007) study a more comprehensive set of macroeconomicannouncements with respect to Boyd, Hu, and Jagannathan (2005), but their sample period includes onlyone short recession. Furthermore, they also do not separate the effect of good versus bad news.

2

long-term yields via inflation expectations. We can think, for example, of an asymmetric

Fed reaction function by which, at the beginning of an expansion with low rates, positive

economic news raise the likelihood of future increases in the target rate by more than bad

news raise the likelihood of future decreases in the target rate. In this setting, the response

of bond prices to macroeconomic news depends critically on the content of the news and on

the phase of the business cycle.

The paper proceeds as follows. In Section 2, we describe the announcements and bond

market data. In Section 3, we present the results of our regression specification. In Section

4, we describe and estimate the jump model. Section 5 concludes.

2. Data

Our data has a number of key features that allow us to address our research questions.

First, it covers a long sample period including several expansions and recessions. Second,

it contains detailed information on the timing and content of macroeconomic news. Lastly,

our bond market data is observed at a daily frequency, so that we can precisely relate asset

prices to macroeconomic releases. This section describes the announcement data, the data

characterizing the stages of the business cycle, and the U.S. Treasury bond market data.

2.1. Survey and Announcement Data

We obtain data on the dates, release times, actual released figures, and median forecasts for

five of the most important U.S. macroeconomic information releases from Money Market

Services (MMS) and Informa Global Markets, covering the period from February 1980

through December 2003. MMS conducts a survey of about 40 money market managers

on the Friday of the week before the release of each economic indicator. MMS reports the

median forecast from the survey, which is made available to the market and the business

press immediately after the survey is taken. The availability of a direct measure of the

expected release – the median forecast – allows us to avoid potential biases of using time-

series econometric models to infer market expectations.

We consider a set of four key announcements describing inflation dynamics by the

consumer price index (CPI) and the producer price index (PPI) and labor market

dynamics by the civilian unemployment rate (CUR) and non-farm payrolls (NFP).3 These

3In a previous version of this paper, we have also considered the announcements of the Federal OpenMarket Committee federal funds target rate (FOMC) to describe the conditions of the money market.However, we have not found any significant result and thus we have decided to leave it out. The FOMCcould be irrelevant in our setting because of the potential inaccuracy of the MMS forecast of FOMC withrespect to market based forecasts (e.g., Bernanke and Kuttner (2005) use the prices of the Federal FundsFutures). Another reason could be that changes of the target rate occur sometimes outside the scheduledeight meetings per year.

3

announcements are released widely and virtually instantaneously at a precise scheduled

time. The statistical agencies impose lock-up conditions to ensure that the information

is not released to the public before the scheduled time (see Fleming and Remolona, 1999).

These announcements are all timed at 8:30am ET and are monthly. A majority of the

announcements occur on a Friday and the employment report (CUR and NFP) is normally

the first government information release concerning economic activity in a given month.

Panel A of Table 1 describes in more detail the macroeconomic announcements in our sample.

Several studies have examined the accuracy of the MMS forecasts. We find strong

evidence that the MMS median forecast has predictive ability for the actual release. We

also find that the median forecast is usually an unbiased predictor. We observe only one

case in which the median forecast is not an efficient predictor. This happens whenever the

prediction of the CPI, which is always announced after the PPI, is collected before the PPI

actual release. In these few cases, the market learns about inflation from the PPI release and

the median forecast of the CPI should take this into account.4 We thus obtain the expected

CPI from a regression on the median MMS CPI forecast and the actual PPI release.

2.2. Expansions and Recessions

We measure expansions and recessions using the experimental coincident recession index

(XRI-C) constructed by Stock and Watson (1989) and available for all our sample period.

The XRI-C is a monthly estimate of the probability that the economy was in a recession in

that month, constructed using four series of leading indicators such as industrial production,

real personal income less transfer payments, real manufacturing and trade sales, and total

employee-hours in non-agricultural establishments. Several papers in the literature used

XRI-C to measure the state of the economy (e.g., Boyd, Hu, and Jagannathan (2005); Hong,

Tourous, Valkanov (2007)).

An important feature of the XRI-C is that it establishes a real-time coincident public

forecasting record. It is thus different from other indexes constructed by Stock and Watson,

such as the experimental leading index (XLI) and the experimental recession index (XRI),

which are based on leading indicators and forecast the growth of the economy in the following

six months. Furthermore, the XRI-C uses only information that is publicly available

at a certain point in time. In contrast, the NBER business cycle dates for expansions

and recessions make use of information that becomes available later. Stock and Watson

discontinued the publication of the index after the end of our sample, but the Federal

Reserve Bank of Chicago now publishes a monthly expansion index, the Chicago Fed National

4We find that in our sample period the actual PPI has strong predictive ability for the actual CPI andthus PPI information is relevant in determining CPI predictions.

4

Activity Index (CFNAI), which is based on an extension of the methodology used to construct

the original XRI-C. Positive (negative) values of the CFNAI index indicate that the economy

is expanding above (below) its historical growth trend. For our sample period, the XRI-C

and the CFNAI index have a large correlation equal to −0.84.Since we aim to describe the different response of the bond markets to macroeconomic

news according to the perceived state of the economy, we use the XRI-C in our empirical

analysis. The correlation between a dummy variable reflecting the NBER business cycle

dating and the XRI-C is equal to 0.78 during the period 1980–2003. It is also interesting to

note that the economy was in a recession phase, the XRI-C indicator was above 50%, for

about 15% of the time during our sample period 1980–2003.

2.3. Futures Data

We focus on the futures market as opposed to the cash market because futures contracts

are typically used to speculate on events such as macroeconomic news and because futures

market prices are not affected by the liquidity biases of the cash market (e.g., the on-the-run

versus off-the-run spread).5 We obtain daily prices of a set of U.S. Treasury bond futures

that require delivery of bonds with different maturities. The contracts mature in March,

June, September, and December. More specifically, we obtain data for the 30-year Treasury

bond futures (from February 1980 through December 2003), for the 10-year Treasury note

futures (from June 1982 through December 2003), for the 5-year Treasury note futures (from

May 1988 through December 2003), and for the 2-year Treasury note futures (from June 1990

through December 2003) traded at the CBOT. We also obtain data for the 90-day Treasury

bill futures (from February 1980 to August 2003), traded at the CME.

In Table 1, Panel B and C, we show summary statistics for daily returns computed on

the settlement prices of all these futures contracts.

3. Regression Model

In this section, we study the effect on bond returns of good and bad macroeconomic news

in expansions and recessions using a regression model. This is the simplest approach to

investigate whether there is any news effect and to what extent this effect is asymmetric

along two dimensions (different type and different times of the news). The regression model

is unsophisticated and does not suggest a specific mechanism for macroeconomic news to be

impounded into bond prices.

3.1. Methodology

5Other papers studying the effect of macroeconomic news on the bond market have used futures marketprices for the same reasons. See, among others, Ederington and Lee (1993), Bollerslev, Cai and Song (2000),Andersen, Bollerslev, Diebold, and Vega (2007).

5

To gauge the extent to which an announcement contains new information, we construct the

following standardized measure of surprise:

Skt =Akt −Xkt

σk, (1)

where Akt is the value of the main statistic released in announcement k at time t, Xkt

denotes the corresponding median survey forecast, and σk is the (unconditional) empirical

standard deviation of the innovations Akt − Xkt. Standardizing the surprise by σk allowsus to compare the regression coefficients across different announcement types. We allow

different announcement surprises to have different effects on the bond returns and we also

allow these effects to be different in expansion versus recession phases. We thus estimate for

each announcement type the following regression:

rt,t−1 = αk + βexp,k (1−XRIt) Sk + βrec,kXRItSk + ek, (2)

where rt,t−1 represents the day-to-day return for each of the Treasury bond futures and XRItis the probability of being in a recession phase at time t as indicated by the value of the

recession index XRI-C.6

We further examine whether the announcement effects vary with the sign of the surprise.

For this, we generalize equation (2) by allowing for different slope coefficients depending on

the good and bad news as follows:

rt,t−1 = αk + βGexp,k (1−XRIt) SktGkt + βBexp,k (1−XRIt) SkBkt+ βGrec,kXRItSktGkt + βBrec,kXRItSkBkt + ekt,

(3)

where Gkt = 1 and Bkt = 0 if the information released in announcement k at time t is

good news (for the Treasury market) and Gkt = 0 and Bkt = 1 otherwise. Whether an

announcement is good or bad news for the bond market is ultimately an empirical question.

However, to facilitate interpretation and consistency of our results, we define as good (bad)

news all negative (positive) surprises, as defined in equation (1). It is generally the case in

our sample that negative (positive) surprises generate positive (negative) bond returns.

3.2. Results

Table 2 presents the results of estimating equation (2) on bond returns at five different

maturities. The importance of allowing for different effects of macroeconomic news in

6For the announcements considered in this paper, CUR and NFP are always released jointly in theEmployment Report. In this case, we include both the CUR and the NFP surprise in the regression ofequation (2) to isolate the marginal effect of each announcement type.

6

expansions and recessions is apparent. Macroeconomic news matters more in expansions, as

the statistical significance of the coefficients readily shows. The explanatory power of this

model with respect to a non state-dependent model is in all cases substantially higher, with

an average increase in the R2 ranging between 19% for the short-end of the yield curve to a

31% increase for the long-end (results not tabulated). In most cases, a statistical test rejects

the restricted model with no state-dependence at least at the 10% significance level.

This asymmetric effect is striking also economically. For the release of NFP, which is

generally significant both in good and bad times, the effect of a surprise in expansions is on

average 30% larger than in recessions.

Another general result in Table 2 is the negative signs of all the statistically significant

coefficients, with the exception of the coefficient on the civilian unemployment rate (CUR).

The release of a higher than expected CPI, NFP, PPI or a lower than expected CUR are all

bad news for the bond market, since they lead to negative bond returns. These effects are

most relevant for the release of NFP.

In particular, our results that employment report surprises matter for bond returns in

recessions are in contrast with the non-significant findings of Boyd, Hu, and Jagannathan

(2005). This discrepancy could be due to several reasons. First, our two sample periods

do not coincide. We study the period 1980 through 2003, whereas Boyd et al. (2005) look

at 1972 through 2000. About one third of their sample, including the 1973-1975 recession,

is affected by the U.S. monetary policy of the pre-Volker period, which has been shown

to respond less strongly to inflation expectations (e.g., Boivin and Giannoni, 2002) and

to depart from Taylor-type rules (see, Primiceri, 2005). Second, Boyd et al. (2005) focus

on the release of CUR and ignore the contemporaneous announcement of NFP. The NFP

release is considered the most relevant of all scheduled macroeconomic announcement for

bond returns by a number of papers (e.g., Balduzzi, Elton, Green, 2001). Furthermore, since

the release of the employment report occurs at the same time, failing to control for all the

news component can affect the results.7 Finally, we measure the expected outcome of the

macroeconomic release as the median analyst forecast from MMS, as it is now standard in

the announcement literature. Boyd et al. (2005) use instead an econometric model, which

is typically estimated using data that were not available in real-time. In contrast, survey

median forecasts are widely available before the release. Furthermore, survey forecasts seem

to be more efficient and less biased than econometric models (e.g., Ang et al., 2007), at least

for inflation forecasting.

7We rerun our analysis ignoring NFP to have an indication of the importance of this effect for our sample.We find, like Boyd et al. (2005), that the release of the CUR has no significant effect in recessions for bondreturns of the US, TY, FV and TU. We do find a significant effect, however, for TB returns.

7

The results of Table 2 do not allow to disentangle the effect of good and bad news

in expansions and recessions. We may have reasons to believe, however, in an asymmetric

response of bond prices in good and bad times. For example, we could think of an asymmetric

Fed reaction function by which, at the beginning of an expansion with low rates, positive

economic news raise the likelihood of future increases in the target rate by more than bad

news raise the likelihood of future decreases in the target rate.

For this purpose, we estimate the richer specification of equation (3) and present the

results in Table 3. We observe that the explanatory power of this specification basically

doubles for the release of the CPI and slightly increases for the employment report and the

PPI. Accordingly, the restriction that the coefficients on good and bad news are the same in

expansions and in recessions is always rejected at least at the 5% level for the CPI, is never

rejected for the PPI, and is rejected in most cases for the release of the employment report.

However, when the NFP is considered in isolation (separately from the CUR), we always

reject the restriction of equal coefficients for good and bad news.

The overwhelming evidence in Table 3 is that bad news is what matters in good times

and, to a slightly lesser extent, good news is what matters in bad times. When signals of

overheating of the economy materialize, because of higher than expected releases of CPI or

NFP with the economy in an expansion phase, bond returns show a negative response at all

maturities. In contrast, signals of a deteriorating economy in a recession, e.g., the release

of lower than expected NFP, determine positive bond returns. Bond returns also seem to

have a positive response to news of a decelerating economy in expansions arising from lower

than expected NFP or lower than expected PPI. Notice that we defined a negative surprise

as good news. As a result, all the estimated coefficients on good news are multiplied by

negative quantities and thus have an opposite effect on bond returns. Finally, news of an

accelerating economy in recessions do not seem to impact bond returns at any horizon.

The interpretation of these results and the comparison of the economic significance across

maturities is more meaningful if we translate the effect of a surprise on returns to the effect

of a surprise on yields. We map returns into yields using a simple duration adjustment for

each segment of the yield curve, using the standard formula for the modified duration:

dP

P= −DmdY (4)

dY = −dPP

/Dm (5)

where dP/P is the return on the underlying, Dm is the modified MacCauley duration, and dY

is the change in yields. We obtain the return on the underlying dP/P as the return generated

8

by a one-standard deviation announcement surprise. We then calculate the duration of each

Treasury futures contract as the modified duration of a deliverable bond with the average

maturity in the maturity range.8 Using the measure for bond returns suggested above and

the appropriate modified duration, we obtain a measure of the effect on yields.

In Figure 1, we use the formula in (4) and the estimates of Table 3 to plot the effect of

a one standard deviation surprise in CPI, NFP, CUR, and PPI on bond yields at different

maturities. The upper plot shows that in good times the effect on yields of bad news is

greatest at the shorter horizon and declines for longer maturities. The NFP, and the CPI

to a lesser extent, are the only announcements that, besides showing the strongest effect on

short-term yields, have economically significant effect on yields at longer maturities. The

central plot shows that the effect of good news in bad times is dominated by CPI and CUR

at the short-horizon, whereas only the release of NFP has consistent significant impacts on

longer yields. The bottom plot shows that the effect of good news in good times is weaker

in economic terms with respect to the previous cases and is concentrated in the short-end of

the yield curve.

We repeat all the analysis carried out in this section using two alternative definitions of

expansions and recessions. First, we use a dummy variable reflecting the NBER business

cycle dates, set equal to zero in expansions and equal to one otherwise. Although the NBER

dates are not an ideal indicator because the dates of the business cycle were not known

in real-time, it is still useful for comparison with some of the literature. Second, we use

the CFNAI expansion index, which is considered the natural successor of the XRI-C and is

constructed from a broader set of indicators. We find that using a NBER dummy does not

change the results. More specifically, we obtain the same significant coefficients across the

board, although the explanatory power is always lower with the NBER dummy. Moreover,

when a coefficient is not significant with XRI-C is also not significant with the NBER dummy.

When we use the CFNAI expansion index, more precisely (-CFNAI), we obtain the same

results of using XRI-C and a similar explanatory power.

4. State-Dependent Jump Model

In this section, we extend the analysis of the effects on bond returns of good and bad

macroeconomic news in expansions and recessions using a state-dependent jump model. This

model puts a detailed structure on the conduit through which macroeconomic information

enters the bond market. We start by specifying the model and then present the results of

the estimation.8We obtain the following modified durations for each Treasury futures: 11.82 for the 30-year Treasury

bond futures, 6.09 for the 10-year Treasury note futures, 3.93 for the 5-year Treasury note futures, 1.71 forthe 2-year Treasury note futures, 0.24 for the Treasury-bill futures.

9

4.1. Methodology

Our econometric specification for the volatility of bond returns builds on the model of Maheu

and McCurdy (2004) for individual stock returns. We model innovations to the daily log

return as the sum of a heteroskedastic shock associated with diffusive information flow and

a jump term capturing the effect of a sudden release of important news.9 However, the

Maheu and McCurdy’s autoregressive conditional jump intensity parametrization is not well

suited for the bond market, because anecdotal evidence shows that jumps tend to occur on

macroeconomic announcement days and these information releases are not autocorrelated.

Therefore, we explicitly allow the jump arrival rate to increase on announcement days. We

also let the jump size mean on announcement days to depend on announcement surprises.

We start by specifying the log futures return as the sum of a time-varying conditional

mean, a normal innovation ²1,t+1 representing diffusive information flow, and a jump

innovation ²2,t+1 representing the effect of sudden information arrival:

xt+1 = ln Ft+1 − ln Ft = µt+1 + ²1,t+1 + ²2,t+1. (6)

We assume that both innovations have a conditional mean of zero and are contemporaneously

independent. Furthermore, the first innovation is distributed:

²1,t+1 = σt+1zt+1, where zt+1 ∼ N[0, 1]. (7)

The second innovation is governed by a Poisson jump distribution with a time-varying

conditional jump intensity λt. That is, the conditional probability of observing nt+1 = j

jumps between dates t and t + 1 is:

Prob[nt+1 = j

∣∣Φt]

=exp (−λt+1) λjt+1

j!, for j = 1, 2, . . . . (8)

The jump innovation can then be expressed as:

²2,t+1 =

nt+1∑

k=1

Yt+1,k − θt+1λt+1, (9)

where Yt+1,k represents the size of the kth jump, if it occurs, drawn independently from a

normal distribution with time-varying conditional mean θt+1 and constant volatility δ.

9Some other papers in the literature have used econometric specifications with time-varying jumpintensities. For example, Bekaert and Gray (1998) and Neely (1999) both model probability of realignmentsfor target zones exchange rates as a function of interest rate differentials and other economic variables. Das(2002) allows the jump intensity of Fed fund rates to vary with the day of the week or with FOMC meetings.

10

4.1.1 Conditional Variance

The conditional variance of returns is also divided into two components. First, we assume

that the volatility of the normal innovation ²1,t+1 follows a standard GARCH process:

σ2t+1 = ω + α²2t + βσ

2t , (10)

where ²t denotes the sum of the two return innovations ²1,t + ²2,t. This specification allows

both return innovations to affect future volatility through the coefficient α and for volatility

shocks to be persistent through the coefficient β. Second, the conditional volatility of the

jump innovation ²2,t+1 is governed by the jump process and is given by:

var[²2,t+1

∣∣Φt+1]

= λt+1(θ2t+1 + δ

2), (11)

where the information set Φt+1 contains all the macroeconomic information up to time t + 1

but not the financial information, i.e. the bond return innovation ²2. The contribution of

jumps to the total conditional variance is time-varying with the conditional intensity λt+1

and the square of the jump-size mean θ2t+1.

The conditional variance function of equation (10) constrains the normal and the jump

innovations to affect future volatility in the same way through the parameter α. However,

the two components of ²t could have in general a different effect. Since we cannot perfectly

disentangle the two innovations from observed returns, we use a proxy for the importance

of the jump innovation. We thus estimate the ex-post expected number of jumps occurred

during period t and allow this estimate to affect the feedback of past innovations on expected

future volatility:

σ2t+1 = ω +(α + αjE

[nt

∣∣Φt])

²2t + βσ2t , (12)

where E[nt

∣∣Φt]

is the ex-post estimate of the expected number of jumps occurred between

time t− 1 and t using period t information. We provide further details on the methodologyused to estimate the ex-post number of jumps in section 4.1.4 that describes the properties

of the distribution of returns implied by the model.

Given the focus of this paper on asymmetric effects of good versus bad news across the

business cycle, we also estimate richer specifications of the conditional variance function of

11

equation (12). More specifically, we model the conditional variance as follows:

σ2t+1 = ω + g(Φt

)²2t + βσ

2t (13)

g(Φt

)=

(1−XRIt

)(αexp + αexpj E

[nt

∣∣Φt]+ αexpa I (²t) + α

expaj I (²t) E

[nt

∣∣Φt])

+ (14)

XRIt(αrec + αrecj E

[nt

∣∣Φt]+ αreca I (²t) + α

recaj I (²t) E

[nt

∣∣Φt])

where E[nt

∣∣Φt]

is again the ex-post estimate of the expected number of jumps occurred

between time t− 1 and t using period t information, I (²t) is an indicator variable equal toone when ²t < 0, and XRIt is the recession index XRI-C at time t.

10 The function g(Φt

)

needs to be positive to have a well-specified GARCH process. However, this constraint is

never binding with the parameter estimates obtained in the empirical section of this paper.

Expected volatility responds to past innovations through the function g(.). This function

allows the feedback to be unconditionally different in expansions versus recessions (αexp and

αrec). The feedback can also be different depending on whether the observed innovation

has likely been a jump, through the additional effect of αj. Furthermore, observed negative

innovations also generate an additional response of volatility, through the parameters αa, in

case of normal innovations, and αaj, in case of expected jump. The jump and asymmetric

feedback effects are allowed to be different in expansion versus recession phases.

4.1.2 Effect of Macroeconomic News

The ultimate goal of our model is to capture changes in the futures returns in response to

macroeconomic announcements for different phases of the business cycle. To accomplish this,

we allow the macroeconomic announcements to affect the return dynamics in three distinct

ways. First, we let the mean log return µt be different in expansions and recessions:

µt+1 = µexpt+1(1−XRIt+1) + µrect+1XRIt+1, (15)

where, following our notation above, XRIt is the probability of being in a recession phase

at time t as indicated by the value of the recession index XRI-C.

The second and more prominent role of macroeconomic announcements is through the

conditional jump intensity. Consistent with the empirical fact that the majority of extreme

Treasury market moves have occurred on announcement days (e.g., Fleming and Remolona,

1999), we allow the jump intensity to increase on announcement days from λ0 to λ0 + λk,

where k denotes either the CPI release, the Employment Report (CUR and NFP) release,

10Maheu and McCurdy (2004) use a similar specification for the conditional variance of stocks and stockindices. However, they do not distinguish between expansion and recession phases.

12

or the PPI release. More concretely, we model the conditional jump intensity as:

λt+1 = λ0 +∑

k=cpi,er,ppi

λkDk,t+1, (16)

where Dk,t+1 is a dummy variable signaling whether the announcement k is scheduled between

dates t and t + 1. Notice that unless λ0 = 0, which the model accommodates, jumps can

occur on non-announcement days, as they do in the data. We do not expect the intensity

of the jump process to be different in expansion versus recession phases. Our intuition is

supported by a likelihood ratio test that shows no significant improvement for more general

versions of equation (16) (results not reported).

Finally, we model the conditional mean of the jump size distribution on announcement

days as a function of the new information released. To gauge the extent to which an

announcement contains new information, we use the standardized measure of surprise

constructed in equation (1). We allow different announcement surprises to have different

effects on the jump size mean and we also allow these effects to be different in expansion

versus recession phases:

θt+1 = (1−XRIt+1)(

θexp0 +∑

k=cpi,cur,nfp,ppi

θexpk Sk,t+1

)+

XRIt+1

(θrec0 +

∑

k=cpi,cur,nfp,ppi

θreck Sk,t+1

).

(17)

The intuition underlying this specification is that the sign and magnitude of the immediate

market reaction to the announcement depends on the news content. However, this link is far

from deterministic in the data, which leads us to model the mean jump size, as opposed to

the jump itself, as a function of the surprise. On non-announcement days, the mean jump

size is equal to θexp0 in expansions and θrec0 in recessions.

4.1.3 Asymmetric Effects of Macroeconomic News

Given the focus of the paper on asymmetric effects of macroeconomic news, we also estimate

our model with a more general version of equation (17) that allows good and bad news to

have different effects in expansions and recessions. More concretely, we express the mean of

13

the jump size as:

θt+1 = (1−XRIt+1)(

θexp0 +∑

k=cpi,cur,nfp,ppi

θexpGk Gk,t+1Sk,t+1 + θexpBk Bk,t+1Sk,t+1

)+

XRIt+1

(θrec0 +

∑

k=cpi,cur,nfp,ppi

θrecGkGk,t+1Sk,t+1 + θrecBkBk,t+1Sk,t+1

) (18)

where Gkt = 1 and Bkt = 0 if the information released in announcement k at time t is a

negative surprise, and Gkt = 0 and Bkt = 1 otherwise, similarly to the equation (3) for bond

returns.

We allow the mean of the jump size to be different than zero during non-announcement

days and when the actual release is equal to the median forecast.11 Furthermore, the intercept

will absorb the effects of macroeconomic news on the jump size mean that are not related

to the surprise.

4.1.4 Distribution of Returns

Conditional on j jumps occurring, our model implies the following distribution of log returns:

f (xt+1|nt+1 = j, Φt) =1√

2π(σ2t+1 + jδ

2) exp

(−(xt+1 − µt+1 + θt+1λt+1 − θt+1j)

2

2(σ2t+1 + jδ

2)

).

(19)

The unconditional distribution is then obtained by forming an expectation over the number

of jumps for each date:

f (xt+1|Φt) =∞∑

j=0

f (xt+1|nt+1 = j, Φt) Prob (nt+1 = j|Φt) . (20)

Finally, the likelihood function is the product of the T unconditional log return distributions.

Note that the likelihood function involves infinite summations over the number of possible

jumps. In practice, we truncate the summations at 20. It turns out that, for our parameter

estimates, the conditional Poisson distribution has zero probability for more than ten jumps.

We can use the unconditional distribution of returns implied by our model to construct

an ex-post probability distribution for the number of jumps occurred at t+1, conditional on

11For all the model specifications, we use a likelihood-ratio test to reject the restriction of a zero interceptin the equation for the jump size mean. In all cases, we reject the restriction and, consistently, we find thatthe intercept is statistically different from zero, either in expansions, or in recessions, or in both.

14

the information at time t + 1:

Prob (nt+1 = j|Φt+1) = f (xt+1|nt+1 = j, Φt) Prob (nt+1 = j|Φt)f (xt+1|Φt) . (21)

The probability that at least one jump has occurred at time t+1 can then easily be obtained

as the complement to one of the probability of having had no jumps at all:

Prob (nt+1 ≥ 1|Φt+1) = 1− Prob (nt+1 = 0|Φt+1) . (22)

4.2. Results

We estimate the state-dependent jump model by standard maximum likelihood using daily

data on the returns of Treasury bond futures for contracts with different maturities.

Table 4 shows the estimates of the model where we allow for the effect of macroeconomic

news on the jump dynamics in expansions and contractions, retaining however the simpler

specifications for the conditional variance in equation (12) and for the mean of the jump size

in equation (17).12 The results on the conditional variance parameters are intriguing. The

feedback of the current innovation and volatility on future conditional volatility is similar

at all maturities, except for the shortest, where current innovations are relatively more

important. When we allow the feedback of the innovation on future volatility to depend

on the ex-post probability of a jump, we infer that jump innovations are not persistent.

More specifically, we observe that the feedback coefficient α is almost completely offset by

αj when the observed innovation was very likely to be a jump. This pattern holds for all

the maturities. Our evidence is consistent with the results of Jones, Lamont and Lumsdaine

(1998), but it is more general, since we identify jump innovations endogenously rather than

imposing that only announcement day innovations can have a different feedback on future

volatility.

The conditional variance of the estimated state-dependent jump model leads to

interesting volatility dynamics for different segments of the yield curve. In Figure 2, we plot

the conditional variance implied by the model for the 30-year Treasury bond futures and for

the 5-year Treasury-note futures over the recent period 2001 to 2003. The total conditional

variance is the sum of a GARCH component, as in equation (12), and a jump component, as

in equation (11). The plot illustrates well that the GARCH component describes the smooth

changes in daily volatility, whereas jumps take care of abnormal episodes of volatility.

12The main results of the model are unaffected by the use of these simpler specifications. We return tothe most general version of the model later in the paper.

15

Table 4 also shows that in general the mean return µ is statistically positive both in

expansions and in contractions, but it is always more economically significant in recessions

for all maturities. This is evidence of higher risk premiums in contraction phases and is

consistent with previous results on counter-cyclical risk-aversion (e.g., Engle and Rosenberg,

2002; Gordon and St-Amour, 2004).

The significant results on the jump parameters confirm the importance of augmenting a

basic GARCH specification. The unconditional jump intensity λ0 is significantly positive for

all the maturities. This confirms our intuition that jumps can be triggered by macroeconomic

announcements that are not included in our sample or by other categories of events. However,

the macroeconomic announcements we consider play a leading role in determining the

jump dynamics. We obtain a significantly positive estimate of λk for the CPI release, the

Employment Report, and the PPI release, indicating that the jump intensity rises sharply

on these announcement days for all the maturities. The Employment Report is the most

important announcement in this respect. In contrast, the CPI release is usually the least

important in increasing the intensity of a jump. When we compare jump intensities across

maturities, we observe that the CPI is most relevant for the jump intensity of the five-year

futures, but it is not significant for the Treasury-bill. The employment report has equally

important effects for all maturities, less so for the two-year futures. Finally, the PPI release

affects less the intensity of a jump for shorter maturities, but it turns out to have always

a stronger impact than the CPI announcement. This last result is likely to depend on the

timing of the announcements, where the PPI is always released at least one day before the

CPI in our sample.

When we look at the parameters describing the jump magnitudes, we observe that,

both in expansions and recessions, the size of the jumps is drawn unconditionally from a

distribution with a negative mean, except for the shortest maturity in recessions, and a

volatility decreasing with shorter maturities. Again, the macroeconomic announcements

play an important role. In expansion phases of the business cycle, the estimates that are

significant are generally negative, that is positive (negative) surprises in the announcements

have a negative (positive) impact on the mean of the jump size. This effect is highly

statistically significant for the release of NFP: when payrolls are higher than expected in

periods of economic growth, the size of jumps in bond returns is drawn from a distribution

with a lower mean, i.e. there is a greater probability of observing higher yields. We register

the opposite effect when payrolls are lower than expected in periods of economic growth.

Economically, a one standard deviation positive NFP surprise in expansion decreases the

jump-size mean by 0.3%. Considering that the average number of jumps induced by the

employment report in one year is 1.4 (0.34+1.06), this corresponds to a more negative mean

16

of the jump size distribution of 0.42%. The release of the CPI in expansions has similar

effects on the long maturity, it is not significant for intermediate maturities, and it switches

sign in the short term.

In contraction phases of the business cycle, however, the effects of economic releases are

less clear. When the NFP is higher than expected, the mean of the jump size increases and

viceversa for lower than expected NFP. The effect of CPI on bond returns at the longer

maturities is also reversed. The release of the PPI has always a negative effect, significant

however only at the two and five year maturities. This evidence suggests that it is indeed

important to allow for different responses to the same announcements in expansions versus

contractions. We can nevertheless obtain further insights and a deeper interpretation of these

kind of results by disentangling the effect of good and bad news in good and bad times. We

will devote Section 4.2.2 to this exercise.

Table 5 presents the characteristics of the jump and volatility dynamics implied by the

estimated state-dependent jump model. Panel A shows summary statistics for the jump

intensity λt, for the jump size mean θt, and for the part of the total variance induced by the

jumps (var[²2,t+1

∣∣Φt]/

var[rt+1

∣∣Φt]), using the estimated model on all the days in our sample,

considering the actual timing of scheduled macroeconomic news, and separating expansions

versus contractions. We conduct non-parametric tests on the samples of λt, θt, and the

variance induced by the jumps for the different segment of the yield curve and we find in all

cases that the samples come from distributions with different medians.13

We observe that the jump intensity is highest for the longest maturity, it is about at the

same level for the ten and five year maturity, and it decreases for shorter maturities. These

intensities range from 0.34 jumps per year (about one jump every three years) at the long

end to 0.13 jumps per year (about one jump every seven and a half years) at the short end

of the yield curve. The median jump intensities clearly resemble the unconditional jump

intensity estimates presented in Table 4. The mean of the jump size distribution is always

negative, less so for shorter maturities. There are no appreciable differences of these statistics

between expansion and recession phases, except for the jump-induced variance that is lower

in recessions at the very long- and short-end of the yield curve.

The proportion of the total variance induced by the jumps is highest for the two-year

futures, where jumps generate on average more than half of the variance, and lower for longer

maturities. This evidence on the prominence of the middle maturity range for the variance

induced by the jumps is consistent both with a macroeconomic news explanation and with an

order flow explanation. In fact, Fleming and Remolona (1999b) find that the high-frequency

13The results of the non-parametric tests are not reported, but are available on request from the authors.

17

responses of 5-minutes bond yields to macroeconomic announcements over the period 1991 to

1995 have a hump-shaped pattern that peaks around two to three years. We do not observe

this pattern in our analysis of returns in the previous section for our longer sample at the

daily frequency. There would thus be a closer link between high-frequency returns dynamics

and jump-induced variance then there is with daily returns. However, Brandt and Kavajecz

(2004) observe that, on non-announcement days, the strongest response of yields to order

flow imbalance is at the two-five-year maturity range. We argue that the jump innovations

to the price process, triggered either by macroeconomic announcements or price discovery,

are most important for the maturity range containing securities that are more universally

held and used for hedging.

Panel B of Table 5 investigates more closely the pattern of jumps implied by the estimated

model. We use the ex-post probability of having at least one jump derived in equations (21)

and (22) to determine the average frequency of jumps in our sample. We observe the highest

number of average jumps per year for the five-year futures. The comparison of expansions

and recessions shows that for all the different futures contracts the average number of jumps

is lower in recessions. We obtain further insights when we relate the jump dynamics to the

pattern of macroeconomic announcements. For example, we observe that the proportion of

jumps occurring on the same day of a macroeconomic release varies a lot with the maturity.

Less than 35% of the total number of jumps occurs on announcement days for the two-year

futures, i.e. the majority of observed jumps are unrelated to our set of macroeconomic

releases, but more than 60% of the jumps occurs on announcement days for the longest

maturity futures.14 The analysis across business cycle phases shows that in recessions jumps

are slightly less related to macroeconomic news than in expansions for all maturities, except

at the short-end of the yield curve, where jumps on announcement days are more frequent

in recessions than in expansions.

We can investigate the same issues from a different perspective, that is the proportion

of macroeconomic announcements that results in a jump. We observe that less than 30%

of the macroeconomic releases in our sample causes a jump for the shortest maturities -

the two-year futures and the Treasury bill -, whereas more than 60% of the announcements

generates a jump for the five-year and longer maturity securities. For all maturities except

at the short-end of the yield curve, the proportion of macroeconomic releases resulting in

14Note that this descriptive analysis has three limitations. First, we cannot talk about a causal linkbetween macroeconomic news and jumps in a strict sense. We are only arguing that, since we observenews and jump on the same day, they are very likely to be related. Second, we are only considering fourmacroeconomic releases, but the set of influential announcements is certainly wider. If this is the case, thereported proportions are biased downward. Lastly, we consider that a jump has occurred when the ex-postprobability is greater than 0.5 (see Table 3, Panel B), but this threshold is arbitrary.

18

a jump is lower in recessions, indicating that unconditionally learning about the state of

the economy from news is less than a surprise when the economy is in contraction. We can

further develop this reasoning by looking at each macroeconomic release separately. The

release of the CPI is more frequently a cause of jumps for the intermediate maturities and

particularly for the two-years in recessions. The employment report (ER) is always the most

relevant release and increases in importance with shorter maturities, whereas the PPI exhibit

the opposite pattern of decreasing importance with maturity.

In Figure 3, we summarize these results. We observe more clearly that announcements

are relatively more important in causing jumps in expansions rather than in recessions and

for longer than for shorter maturity securities. The release of the employment report is

invariably the most influential, both in expansions and recessions. The only exception seems

to be the two-year horizon, where the role of inducing jumps of the three releases that we

consider is more balanced.

4.2.1 Specification Tests

To ensure that the model is reasonably well specified with respect to the inclusion of the

timing and information of macroeconomic news, we conduct a sequence of specification tests.

Specifically, starting with a version of the model with constant jump intensity (i.e., λt = λ0),

we use likelihood ratio tests to sequentially examine each restriction with respect to the

general model estimated in Table 4.

Table 6 shows the results of three specification tests. First, we find that, for all the

Treasury futures, a constant jump intensity is strongly rejected in favor of the most general

version of the model. It is thus statistically important to allow the intensity of the jump to

be time-varying. The second tested restriction is to allow the intensity of the jump to be

different on announcement days, but with no distinction of the announcement type. Again,

we strongly reject this restriction for all the different segments of the yield curve, except

for the two years. It seems therefore essential to allow different announcements to affect

differently the intensity of the jump process. Lastly, we constrain the mean of the jump size

to be independent on the information content of the macroeconomic release. This restriction

is again strongly rejected for all the maturities. From these specification tests, we conclude

that the features of the proposed state-dependent jump model are all statistically important

to accurately describe the returns of the bond futures.

4.2.2 Asymmetric Effect of Macroeconomic News

We argue that the information content of macroeconomic news can have different effects on

bond markets depending on observing good news versus bad news, in good or bad times.

19

To disentangle these asymmetric effects, we use the general specification for the jump size

mean formalized in equation (18).

Table 7 presents the results for the effects on the mean of the jump size. The results

on the other characteristics of the model are not different from the estimates of the simpler

specification presented in Table 4 and are thus not reported. A likelihood ratio test shows in

all cases a significant statistical improvement in generalizing the specification for the mean

of the jump size. More specifically, it is statistically important to allow good and bad news

to have different effects in good or bad times and this characteristic of the model seems

especially crucial for the shorter maturities.

We observe that the parameter estimates are mostly significant for bad news in expansion

phases of the business cycle. In contrast, good news in good times do not seem to

systematically affect the distribution of the size of the jump in bond returns. We register

the most relevant effects of the information in macroeconomic news on jump dynamics when

the release of the NFP is higher than expected in an expansion phase of the business cycle.

In this case, the distribution of the size of jumps in Treasury bond futures returns has a

negative conditional mean for all maturities. We observe a very similar pattern for PPI

announcements, with higher than expected releases leading to more negative jump size

means. The announcement of CPI has the opposite impact, except for the longest maturity:

a positive CPI surprise leads to a distribution of the jump size with a higher conditional

mean.

In recession phases, the pattern of the relevant information is less clear, but good news

(negative surprises) tend to be more important in affecting jump dynamics. More specifically,

when the released NFP is lower than expected, the size of the jump in returns for shorter

maturities is drawn from a distribution with a negative mean.15 This pattern is similar,

although less significant, for the CPI. In contrast, jump after good news in the PPI release

are drawn from a distribution with a higher mean, although this effect is significant only for

intermediate maturities.

The interpretation of these results is again more meaningful, if we translate the effect of

a surprise on returns to the effect of a surprise on yields. The mapping between returns and

yields goes again simply through the duration of each segment of the yield curve as shown

before in equation (4). In this case, we obtain the return on the underlying dP/P as the

mean size of a jump triggered by a one-standard deviation announcement surprise occurring

with intensity λt. We then calculate the duration of each Treasury futures contract as the

15We observe a negative impact on the mean of the jump size, because the parameter estimate θrecG,nfp ispositive, but the surprise is always negative.

20

modified duration of a deliverable bond with the average maturity in the maturity range.

Using the measure for bond returns suggested above and the appropriate modified duration,

we obtain a measure of the effect on yields.16

Figure 4 plots the effect of a one standard deviation surprise in NFP, PPI and CPI on the

mean of the jump size distribution of yields at different maturities. We also plot two standard

deviation bands, obtained from the standard errors of the parameter estimates.17 Panel A

shows the most significant case, the effect of positive surprises in an expansion phase of the

business cycle. The exception is the release of the CUR, for which we actually show the effects

of negative surprises, since the sign of the surprise has the opposite implications with respect

to the other macroeconomic announcements. The release of a lower than expected CUR has

similar implications for the labor market than the release of an higher than expected NFP.

Yields for all maturities rise in response to higher than expected numbers for NFP and PPI

releases. In contrast, the results for the release of CUR are never significantly affecting the

mean of the jump size in expansion. We interpret the results for NFP and PPI as evidence

that an unexpected pickup in economic growth when the economy is rising is perceived as

indication of overheating, which leads short and long term interest rates to increase. The

effect of a positive CPI surprise is less intuitive. We observe a general reduction in yields,

with a higher yield only at the longest maturity, implying a steeper yield curve. Either an

expected pickup in economic growth or higher expected future inflation would tend to raise

long-term rates relative to short rates, steepening the yield curve. It is more difficult to

explain why a higher than expected CPI release would reduce the mean of the jump size in

yields at the intermediate maturities. In fact, the market seems to interpret positive shocks

to CPI differently than PPI. However, any interpretation of the CPI results should be taken

cautiously, because the CPI is often released the day after the PPI and, although we control

for the PPI release in forming the CPI expectation, the results could still be affected by

overreaction or underreaction to PPI news.

Panel B shows the effect of negative surprises in the NFP, PPI, CPI release and positive

surprise in the CUR release, in a recession phase of the business cycle. When the release of

PPI is lower than expected, we observe a general reduction in yields. The monetary policy

16The measure of bond returns is the effect of a one standard deviation surprise in the macroeconomicannouncement obtained as an expected jump size times an intensity measure. As such, the change in yieldscan be interpreted in the same logic as the jump effect of a macroeconomic surprise.

17We obtain the error bands of the effect on yields using the bond return dP/P computed as the jump sizemean parameters plus/minus two standard errors and the procedure outlined above to convert bond returnsinto bond yields. This methodology delivers a good approximation of the true confidence bands, since thecovariance between the intercept and the slope term of the jump size mean is negligible and negative (e.g.,the covariance between θexp0 and θ

expB,nfp for the 30-year Treasury bond futures is -1.01e-08 when θ

expB,nfp is

1.35e-02) and the standard error of the jump intensity term exerts a second order effect.

21

has the expected expansionary bias, since the economy is in recession and inflation shows

indications of being below analyst estimates. Furthermore, during a recession, the negative

inflation shock is also likely to lower investors’ inflation expectation. This latter effect could

explain the drop in long term yields. The release of weaker than expected NFP numbers

in a recession phase does not have very significant effects. We observe a flattening of the

yield curve that is consistent with future rate hikes being less likely when the economy is in

a recession and payrolls are showing no sign of recovery. Alternatively, lower than expected

NFP numbers could lower payroll pressure on production costs, resulting in lower inflation

expectations, and therefore lower long term yields. The release of higher than expected CUR

has significant effects mostly on the ten and five-year yields, where we observe reduction in

yields consistent with signs of slow growth in recessions. It is interesting to see that CUR has

some significant effects in recession, but was never relevant in expansions, indicating that

the bond market looks at different indicators to gauge the state of the economy in different

phases of the business cycle. The release of a lower than expected CPI does not significantly

affect the mean of the jump size, except that it implies higher short term yields. It is hard

to interpret this result in isolation, it only makes sense when we observe that the yield curve

becomes flatter. However, the release of the PPI very shortly before implies again that all

inferences should be taken with caution.

4.2.3 Jumps and Asymmetric Feedbacks on Conditional Volatility

We further examine the effect of macroeconomic news on volatility dynamics using a

richer specification for conditional volatility. More specifically, Table 8 presents the results

of estimating the state-dependent jump model with the GARCH component specified in

equation (13), where we allow for different feedback on future volatility of normal versus

jump innovations in expansions versus recessions, and asymmetries of positive and negative

innovations. We do not report the estimates for the parameters of the model that are not

related to volatility, because they are very similar to the general estimation results described

in Table 4.

A likelihood ratio test indicates that the richer specification for the conditional volatility

function is especially important for the longest and shortest maturity. In general, we observe

that the feedback of innovations on future conditional volatility is stronger in recessions. We

attribute the low or non-significant αrec for the five and two year maturity to the shorter

sample that contains only two recessionary periods. The feedback of the jump innovations

αexp,recj is always negative and statistically significant, both in expansions and in recessions,

indicating that the effect of jumps on future volatility tends to not persist. We find evidence

of asymmetric volatility for the longest and shortest maturity, but only in expansions. In

22

both cases, if the negative innovation is likely to be a jump, the higher persistence αexpa is

almost completely offset by the asymmetric jump feedback term αexpaj .

Figure 5 exploits the parameters estimated in Table 8 to plot the effects of positive and

negative innovations in expansions and recessions, separating normal innovations from jump

innovations, i.e. the innovations that were jumps with a probability of 50%. We observe

the lower persistence of positive normal innovations in expansions versus recessions, but the

higher persistence of negative normal innovations in expansions versus recessions because of

asymmetric volatility. We also notice how jump innovations dampen persistence and this is

true in expansions, recessions, positive and negative innovations.

5. Conclusion

We analyze the effects of good and bad macroeconomic news on bond market returns and

volatility, during economic expansions and economic contractions. Specifically, we identify

a high-frequency relation between yields and the economy, whereas the macroeconomic

literature has typically modeled interactions at a lower frequency.

We find that the information content of the announcements is most important for bond

returns when it contains bad news for the bond market in expansions and, to a lesser extent,

good news for the bond market in contractions. Furthermore, the response of the bond

market depends on the announcement type, on the bond maturity, and on the business

cycle. We also find that macroeconomic news impact substantially the volatility of bond

returns at all maturities through increased jump intensities and different distributions of the

jump size. The proportion of the total variance induced by the jumps is greatest for the

medium term maturities of two and five years. Finally, we observe that the impact of jumps

on volatility does not persist, consistent with Jones, Lamont, and Lumsdaine (1998).

In bond markets, monetary authorities play a key role, with direct effects on short-

term yields and some influence on long-term yields via inflation expectations. Our results

are consistent, for example, with an asymmetric Fed reaction function by which positive

economic news induce a strong increase in the likelihood of higher future target rates in

expansions, but only a marginal increase in contractions. In this setting, the response of

bond prices to macroeconomic news depends critically on the content of the news and on

the phase of the business cycle.

Furthermore our paper shows the importance of modeling volatility in the bond market

using both a diffusive and a jump component and incorporating the timing and information

of macroeconomic news, allowing for different dynamics in expansions and recessions. A

challenging area for future study is to incorporate these results in developing no-arbitrage

models of the term structure of Treasury bond yields.

23

References

Andersen, T.G., Bollerslev, T., Diebold, F.X., and Vega, C. (2007) Real-time pricediscovery in stock, bond and foreign exchange markets, Journal of International Economics,forthcoming.

Andersen, T.G., Bollerslev, T., Diebold, F.X., and Vega, C. (2003) Micro effects of macroannouncements: Real-time price discovery in foreign exchange, American Economic Review93, 38–62.

Ang, A., Bekaert, G., and Wei, M. (2007) Do macro variables, asset markets or surveysforecast inflation better?, Journal of Monetary Economics 54, 1163–1212.

Bekaert, G. and Gray, S.F. (1998) Target zones and exchange rates: An empiricalinvestigation, Journal of International Economics 45, 1–35.

Bernanke, B.S. and Kuttner, K.N. (2005) What explains the stock market’s reaction toFederal Reserve policy?, Journal of Finance 60, 1221–1257.

Bollerslev, T., Cai, J., and Song, F.M. (2000) Intraday periodicity, long memory volatility,and macroeconomic announcement effects in the U.S. Treasury bond market, Journal ofEmpirical Finance 7, 37–55.

Boyd, J.H., Hu, J., and Jagannathan, R. (2005) The stock market’s reaction tounemployment news: Why bad news is usually good for stocks, Journal of Finance 60,649–672.

Brandt, M.W. and Kavajecz, K.A. (2004) Price discovery in the U.S. Treasury market: Theimpact of orderflow and liquidity on the yield curve, Journal of Finance 59, 2623–2654.

Brenner, M., Pasquariello, P., and Subrahmanyam, M. (2006) Financial markets and themacro economy, Working Paper, University of Michigan.

Christiansen, C. and Ranaldo, A. (2007) Realized bond-stock correlation: Macroeconomicannouncement effects, Journal of Futures Markets, forthcoming.

Das, S.R. (2002) The surprise element: Jumps in interest rates, Journal of Econometrics106, 27–65.

Ederington, L.H. and Lee, J.H. (1993) How markets process information: News releases andvolatility, Journal of Finance 48, 1161–1191.

Engle, R.F. and Rosenberg, J. (2002) Empirical pricing kernels, Journal of FinancialEconomics 64, 341–372.

Fleming, M.J. and Remolona, E.M. (1997) What moves the bond market?, Federal ReserveBank of New York Economic Policy Review, 31–50.

Fleming, M.J. and Remolona, E.M. (1999) Price formation and liquidity in the U.S. Treasurymarket: The response to public information, Journal of Finance 54, 1901–1915.

Fleming, M.J. and Remolona, E.M. (1999b) The term structure of announcement effects,Working Paper, Bank for International Settlements.

Gordon, S. and St-Amour, P. (2004) Asset returns and state-dependent risk preferences,Journal of Business and Economic Statistics 22, 241–252.

24

Hong, H., Torous, W., and Valkanov, R. (2007) Do industries lead stock markets?, Journalof Financial Economics 83, 367–396.

Jones, C.M., Lamont, O., and Lumsdaine, R.L. (1998) Macroeconomic news and bondmarket volatility, Journal of Financial Economics 47, 315–337.

Maheu, J.M. and McCurdy, T.H. (2004) News arrival, jump dynamics and volatilitycomponents for individual stock returns, Journal of Finance 59, 755–793.

Neely, C.J. (1999) Target zones and conditional volatility: The role of realignments, Journalof Empirical Finance 6, 177–192.

Stock, J. and Watson, M. (1989) New indexes of coincident and leading economic indicators,in Olivier, J., Fisher, S. (ed.): NBER Macroeconomics Annual 1989 (Cambridge, MA).

Veronesi, P. (1999) Stock market overreaction to bad news in good times: A rationalexpectations equilibrium model, Review of Financial Studies 12, 975-1007.

25

Table 1: Macroeconomic News and Futures Returns Summary Statistics

This table shows summary statistics for macroeconomic news and for daily returns on the 30-yearTreasury bond futures (US), the 10-year Treasury note futures (TY), the 5-year Treasury notefutures (FV), the 2-year Treasury note futures (TU), and the 90-day Treasury bill futures (TB).Panel A shows the announcements, their abbreviations, the reported units of the variables, the timesat which the announcements are normally released, and the number of times two announcementsare concurrent (same date and time). Panel B shows statistics on daily continuously compoundedreturns for the full sample that is available for each futures contract. Panel C shows statistics ondaily returns for the longest sample common to all the futures contracts.

Panel AConcurrent

AnnouncementsAnnouncement Abbrev. Units Time CPI CUR NFP PPI

Consumer Price Index CPI % Change 8:30 ET 282 0 0 0Civilian Unemployment CUR % Level 8:30 ET 0 282 282 13Nonfarm Payrolls NFP Thousands 8:30 ET 0 282 282 13Producer Price Index PPI % Change 8:30 ET 0 13 13 282

Panel B

US TY FV TU TB

First Obs Feb-1980 Jun-1982 May-1988 Jun-1990 Feb-1980Last Obs Dec-2003 Dec-2003 Dec-2003 Dec-2003 Sep-2003Obs 6054 5469 3968 3526 5965Mean+ 0.0399 0.0449 0.0238 0.0079 0.0079StDev+ 0.1189 0.0718 0.0425 0.0211 0.0214Skewness -0.0692 -0.0357 -0.4764 -1.0419 0.3349Kurtosis 5.1034 5.9126 6.4016 10.8327 14.5480Min -0.0324 -0.0286 -0.0222 -0.0103 -0.0071Max 0.0394 0.0342 0.0118 0.0065 0.0140

Panel C

US TY FV TU TB

First Obs Jun-1990 Jun-1990 Jun-1990 Jun-1990 Jun-1990Last Obs Sep-2003 Sep-2003 Sep-2003 Sep-2003 Sep-2003Obs 3353 3353 3353 3353 3353Mean+ 0.0420 0.0358 0.0276 0.0084 0.0077StDev+ 0.0921 0.0615 0.0421 0.0210 0.0074Skewness -0.4209 -0.5271 -0.5872 -1.0220 0.7597Kurtosis 4.5016 5.5767 6.5843 10.8010 12.1037Min -0.0298 -0.0286 -0.0222 -0.0103 -0.0036Max 0.0246 0.0142 0.0114 0.0065 0.0043

+ Daily mean return and standard deviation are annualized.

26

Table 2: Effect of Macroeconomic News on Bond Returns

This table shows parameter estimates for the following regression:

rt,t−1 = αk + βexpk (1−XRIt) Sk + βreckXRItSk + ek,

where rt,t−1 represents the day-to-day return for each of the Treasury bond futures, XRIt is theprobability of being in a recession phase at time t, and S denotes the standardized announcementsurprise. The day-to-day return is computed on the 30-year Treasury bond futures (US), the 10-year Treasury note futures (TY), the 5-year Treasury note futures (FV), the 2-year Treasury notefutures (TU), and the 90-day Treasury bill futures (TB).

US TY FV TU TB

CPI α 0.00028 0.00020 0.00018 0.00018 0.00011βexp -0.00123∗ -0.00085∗∗ -0.00091∗∗ -0.00043∗∗ -0.00017∗

βrec -0.00244 -0.00211∗∗ -0.00057 -0.00003 -0.00035R2 0.045 0.052 0.055 0.047 0.037

CUR α 0.00060 0.00021 0.00014 0.00007 0.00019∗

βexp 0.00091 0.00096∗∗ 0.00083∗∗ 0.00042∗∗ 0.00020∗

βrec 0.00297 -0.00001 0.00012 0.00027 0.00097∗∗

NFP βexp -0.00446∗∗∗ -0.00320∗∗∗ -0.00240∗∗∗ -0.00101∗∗∗ -0.00061∗∗∗

βrec -0.00303 -0.00211∗ -0.00170∗∗ -0.00102∗∗∗ -0.00059∗∗∗

R2 0.181 0.216 0.294 0.308 0.171

PPI α 0.00028 0.00075 0.00047 0.00015 0.00001βexp -0.00220∗∗∗ -0.00096∗∗ -0.000212 0.00001 -0.00043∗∗∗

βrec 0.00135 0.00111 0.00097∗ 0.00035 -0.00011R2 0.038 0.017 0.015 0.012 0.048

∗ ∗ ∗, ∗∗, and ∗ denote statistical significance at the one, five, and 10 percent levels,respectively.

27

Table 3: Effect of Good and Bad News on Bond Returns

This table shows parameter estimates for the following regression:

rt,t−1 = αk + βGexpk (1−XRIt) SktGkt + βBexpk (1−XRIt) SkBkt+ βGreckXRItSktGkt + βBreckXRItSkBkt + ekt,

where rt,t−1 represents the day-to-day return for each of the Treasury bond futures, XRIt is theprobability of being in a recession phase at time t, S denotes the standardized announcementsurprise, and Gkt = 1 and Bkt = 0 if the information released in announcement k at time t is goodnews (for the Treasury market) and Gkt = 0 and Bkt = 1 otherwise. The day-to-day return iscomputed on the 30-year Treasury bond futures (US), the 10-year Treasury note futures (TY), the5-year Treasury note futures (FV), the 2-year Treasury note futures (TU), and the 90-day Treasurybill futures (TB).

US TY FV TU TB

CPI α 0.00073 0.00109∗∗∗ 0.00092∗∗∗ 0.00047 0.00016βGexp 0.00080 0.00109 0.00043 0.00010 0.00015βBexp -0.00286∗∗∗ -0.00266∗∗∗ -0.00211∗∗∗ -0.00101∗∗∗ -0.00043∗∗∗

βGrec -0.00665∗∗∗ -0.00357∗∗ 0.00028 0.00023 -0.00116∗∗

βBrec -0.00019 -0.00148∗ -0.00117 -0.00016 0.00016R2 0.095 0.100 0.099 0.084 0.103

CUR α 0.00089 0.00014 0.00001 -0.00001 0.00017βGexp 0.00039 -0.00031 -0.00041 -0.00005 0.00014βBexp 0.00119 0.00185∗∗∗ 0.00248∗∗∗ 0.00108∗∗∗ 0.00026βGrec 0.00167 0.00338∗ 0.00271 0.00065 -0.00011βBrec 0.00303 -0.00082 -0.00138 0.00006 0.00108∗∗

NFP βGexp -0.00339∗∗∗ -0.00253∗∗∗ -0.00156∗∗∗ -0.00068∗∗∗ -0.00052∗∗∗

βBexp -0.00634∗∗∗ -0.00452∗∗∗ -0.00390∗∗∗ -0.00161∗∗∗ -0.00076∗∗∗

βGrec -0.00319 -0.00275∗∗ -0.00239∗∗∗ -0.00117∗∗∗ -0.00056∗∗

βBrec 0.00035 0.00110 0.00014 -0.00097 -0.00054R2 0.189 0.233 0.334 0.339 0.178

PPI α 0.00054 0.00046 0.00044 0.00002 -0.00001βGexp -0.00266∗∗ -0.00131∗∗ -0.00030 -0.00021 -0.00046∗∗

βBexp -0.00166 -0.00032 -0.00008 0.00033 -0.00042βGrec 0.00081 0.00102 0.00114∗∗ 0.00042∗∗ 0.00005βBrec 0.00217 0.00111 0.00072 0.00015 0.00022R2 0.040 0.019 0.016 0.023 0.048

∗ ∗ ∗, ∗∗, and ∗ denote statistical significance at the one, five, and 10 percent levels,respectively.

28

Table 4: Estimates of the State-Dependent Jump Model

This table shows estimates for daily returns on the 30-year Treasury bond futures (US), the 10-year Treasury note futures (TY), the 5-year Treasury note futures (FV), the 2-year Treasury notefutures (TU), and the 90-day Treasury bill futures (TB) of the following model:

²1,t+1 = σt+1zt+1, zt+1 ∼ N[0, 1], ²2,t+1 =nt+1∑

k=1

Yt+1,k − θt+1λt+1

σ2t+1 = ω +(α + αjE

[nt

∣∣Φt])

²2t + βσ2t , µt+1 = µ

expt+1(1−XRIt+1) + µrect+1XRIt+1

λt+1 = λ0 +∑

k=cpi,er,ppi

λkDk,t+1

θt+1 =(1−XRIt+1

)(θexp0 +

∑

k=cpi,cur,nfp,ppi

θexpk Sk,t+1

)+XRIt+1

(θrec0 +

∑

k=cpi,cur,nfp,ppi

θreck Sk,t+1

)

US TY FV TU TB

ω × 106 0.9810∗∗∗ 4.4483∗∗∗ 0.0021∗∗∗ 0.5273∗∗∗ 14.6029∗∗∗α 0.0737∗∗∗ 0.0878∗∗∗ 0.0701∗∗∗ 0.0411∗∗∗ 0.1387∗∗∗

αj -0.0561∗∗∗ -0.0640∗∗∗ -0.0531∗∗∗ -0.0352∗∗∗ -0.1239∗∗∗

β 0.9370∗∗∗ 0.9188∗∗∗ 0.9321∗∗∗ 0.9571∗∗∗ 0.8756∗∗∗

µexp × 104 1.5124∗ 0.8729∗ 0.4063 0.2004 0.0325µrec × 104 5.2735∗∗ 4.7239∗∗∗ 3.1109∗∗∗ 1.8189∗∗∗ 0.9623∗∗∗

λ0 0.3429∗∗∗ 0.3161∗∗∗ 0.3213∗∗∗ 0.1641∗∗∗ 0.1320∗∗∗

λcpi 0.3501∗∗∗ 0.3957∗∗∗ 0.4650∗∗∗ 0.1989∗∗∗ 0.0635λer 1.0574∗∗∗ 1.2604∗∗∗ 1.0151∗∗∗ 0.5447∗∗∗ 0.9802∗∗∗

λppi 0.6823∗∗∗ 0.6944∗∗∗ 0.5370∗∗∗ 0.1565∗∗∗ 0.1548∗∗∗

θexp0 × 104 -5.3039∗ -4.7729∗∗∗ -2.4921∗ -4.4213∗∗∗ -0.42493θexpcpi × 104 -29.2192∗∗∗ 0.5324 8.4604 9.6003∗∗ 3.2602∗∗θexpcur × 104 4.9841 -3.7622 -0.9755 -3.5334 -0.1045θexpnfp × 104 -33.1023∗∗∗ -21.3446∗∗∗ -22.2312∗∗∗ -4.1504 -0.8882∗θexpppi × 104 15.1112 4.8902 1.4131 5.1144 1.7234θrec0 × 104 -29.8123∗∗∗ -24.3031∗∗∗ -14.1431∗∗∗ -3.1804 4.4326∗∗∗θreccpi × 104 57.6348∗∗ 34.6237∗ 15.5484 8.1934 5.3218θreccur × 104 -30.5386 -26.1379 28.8390∗∗ 4.1004 3.7019∗θrecnfp × 104 43.4328∗∗ 21.1036∗∗ 19.7344∗∗ 18.4301∗∗∗ 6.8400∗∗∗θrecppi × 104 -22.4381 -18.2351 -18.7093∗∗ -14.7223∗∗∗ -2.7254δ 0.0055∗∗∗ 0.0039∗∗∗ 0.0026∗∗∗ 0.0022∗∗∗ 0.0010∗∗∗

logL 21,766.60 22,306.00 18,195.90 18,870.40 35,506.50Obs 6054 5469 3968 3526 5965

∗ ∗ ∗, ∗∗, and ∗ denote statistical significance at the one, five, and 10 percent levels,respectively.

29

Table

5:

Model-Im

plied

Jum

pand

Vola

tility

Dynam

ics

Thi

sta

ble

show

ske

yfe

atur

esim

plie

dby

the

esti

mat

edju

mp

mod

elfo

rda

ilyre

turn

son

the

30-y

ear

Tre

asur

ybo

ndfu

ture

s(U

S),

the

10-y

ear

Tre

asur

yno

tefu

ture

s(T

Y),

the

5-ye

arTre

asur

yno

tefu

ture

s(F

V),

the

2-ye

arTre

asur

yno

tefu

ture

s(T

U),

and

the

90-d

ayTre

asur

ybi

llfu

ture

s(T

B).

Pan

elA

show

sth

eex

pect

ednu

mbe

rof

jum

ps(λ

t),th

em

ean

ofth

eju

mp

size

(θt)

,an

dth

epr

opor

tion

ofth

eco

ndit

iona

lvar

ianc

eth

atis

expl

aine

dby

the

jum

pco

mpo

nent

(i.e

.,va

r[ ²2,t+

1

∣ ∣ Φt]/

var[ r

t+1

∣ ∣ Φt] )

.T

here

port

edva

lues

are

sam

ple

med

ians

abov

ean

dm

ean

abso

lute

devi

atio

nsfr

omth

em

edia

nsbe

low

.Pan

elB

show

sth

ein

ferr

ednu

mbe

rof

jum

psfo

rth

efu

llsa

mpl

e,th

eav

erag

enu

mbe

rof

jum

pspe

rye

ar,th

enu

mbe

rof

anno

unce

men

tda

ysin

the

sam

ple,

the

infe

rred

num

ber

ofju

mps

occu

rrin

gon

anno

unce

men

tda

ys,t

hepr

opor

tion

ofju

mps

occu

rrin

gon

anno

unce

men

tda

ys,a

ndth

epr

opor

tion

ofan

noun

cem

ent

days

that

resu

ltin

jum

ps.

Pan

elA

US

TY

FV

TU

TB

all

exp

rec

all

exp

rec

all

exp

rec

all

exp

rec

all

exp

rec

λt

0.34

290.

3429

0.34

290.

3161

0.31

610.

3161

0.32

130.

3213

0.32

130.

1641

0.16

410.

1641

0.13

200.

1320

0.13

200.

0970

0.09

730.

0955

0.10

940.

1094

0.10

990.

0933

0.09

330.

0930

0.04

060.

0406

0.04

090.

0530

0.05

310.

0520

θ t-6

.04e

-4-5

.79e

-4-2

.64e

-3-5

.16e

-4-5

.16e

-4-2

.16e

-3-2

.84e

-4-2

.72e

-4-1

.07e

-3-4

.29e

-4-4

.38e

-4-1

.02e

-4-2

.80e

-5-3

.30e

-53.

75e-

45.

38e-

42.

64e-

46.

52e-

43.

10e-

41.

40e-

44.

08e-

42.

24e-

41.

27e-

43.

17e-

41.

02e-

45.

50e-

51.

80e-

48.

60e-

53.

30e-

58.

80e-

5ju

mp-

indu

ced

0.31

870.

3264

0.26

090.

3697

0.36

970.

3727

0.43

060.

4332

0.41

580.

5551

0.56

120.

5255

0.41

960.

4597

0.17

52va

rian

ce0.

1261

0.12

350.

1342

0.13

110.

1317

0.12

610.

1190

0.12

150.

1000

0.10

460.

1060

0.09

170.

2458

0.23

760.

2232

30

Pan

elB

US

TY

FV

TU

TB

all

exp

rec

all

exp

rec

all

exp

rec

all

exp

rec

all

exp

rec

N.ju

mps

+10

0286

913

392

482

698

682

607

7532

228

735

473

418

55N

.ju

mps

per

year

++

41.3

842

.25

36.4

242

.24

42.7

438

.34

42.9

743

.41

39.6

422

.83

23.3

819

.10

19.8

220

.69

15.0

3N

.an

noun

cem

ent

days

839

718

121

759

670

8954

648

165

473

411

6283

171

012

1N

.ju

mps

onan

noun

cem

ent

days

612

534

7853

948

554

348

314

3411

010

010

245

209

36ju

mps

onan

n.da

ysov

eral

lju

mps

0.61

0.61

0.59

0.58

0.59

0.55

0.51

0.52

0.45

0.34

0.35

0.29

0.52

0.50

0.65

jum

pson

ann.

days

over

allan

n.da

ys0.

730.

740.

640.

710.

720.

610.

640.

650.

520.

230.

240.

160.

290.

290.

30ju

mps

onC

PI

ann.

days

9685

1179

736

7970

922

193

1412

2ju

mps

onC

PI

over

jum

pson

ann.

days

0.16

0.16

0.14

0.15

0.15

0.11

0.23

0.22

0.26