cfa institute downside risk

42 • Third Quarter 2014 ©2014 CFA Institute • cfapubs.org

Utilizing Downside Risk MeasuresMichelle McCarthy Managing Director and Head of Risk Management Nuveen InvestmentsChicago

Investment advisers and fund managers could better outperform relevant benchmarks on a risk-adjusted basis by analyzing differently their current and prospective client portfolios. This improved performance can be achieved by focusing primarily on downside risk measures and understanding whether portfolios exhibit asymmetrical return profiles with fat tails. In addition, certain classic risk management strategies are useful, such as the Sharpe ratio and the information ratio, whereas other measures, such as the Sortino ratio and semi-standard deviation, may be misleading during up-market cycles.

In this presentation, I want to first separate risk measures from performance measures because

they are often confused in people’s minds. Then I will discuss classic measures, such as the Sharpe ratio, the information ratio, and downside capture, because they are considered the key risk-adjusted performance measures to use to assess the past returns of a portfolio or fund. Next, I will discuss two conditions that are important in assessing downside risk—volatility and drawdown. Finally, I will discuss how to implement a risk management strategy for clients’ portfolios, particularly focusing on value at risk (VaR) and ex ante tracking error.

Performance vs. Risk Measures: Looking Backward and Looking ForwardMeasures that are created by analyzing the past per-formance of a portfolio can be used to assess what may happen in the future for that portfolio—unless strategies or markets change in meaningful ways. When meaningful change occurs, the backward-looking measures are not really helpful. There are significant differences between performance mea-sures and risk measures.

Performance Measures. If what is being measured has already happened, then it is per-formance, not risk. If a measure is conducted on today’s holdings, it represents a forward-looking performance measure. But if it is conducted on past returns of a fund, then it represents a backward-looking performance measure. For example, past performance of a fund is typically a good guide

to understanding the future, but it will not alert an investor when the strategy of the portfolio has changed in a meaningful way or the manager has tried something new. In those situations, past per-formance is not going to help in understanding the future. If the markets change in meaningful ways from historical patterns, then backward-looking performance measures are not strong indicators for understanding future risks. Measures applied to the past return series of a fund are what I would consider performance variability measures rather than risk measures.

The distinction between performance measures and risk measures is important for several reasons. First, performance variability measures can show portfolio manager skill, whereas risk measures do not. If managers are able to buy lower and sell higher than their peers, their portfolios will have a lower volatility and better standard deviation of returns. Second, when portfolio composition changes significantly, performance measures will reflect the change in portfolio composition slowly, whereas the risk measures will reflect the change in portfolio composition instantly. To protect equity portfolios from future downside, risk measures are more appropriate. Risk measures will also help determine whether the manager has changed the holdings in some particular way from the past. But if portfolio strategies do not change, performance and risk measures are similar.

The classic performance variability measures are standard deviation, ex post tracking error (i.e., the historical variability of performance relative to the benchmark), the Sharpe ratio, the informa-tion ratio, such variants on the Sharpe ratio as the Treynor ratio and Jensen’s alpha, downside mea-sures (e.g., the Sortino ratio and downside cap-ture), and most beta measures. Beta calculations

This presentation comes from the Wealth Management Conference held in Garden Grove, California, on 19–20 February 2014 in partner-ship with CFA Society Orange County.

Utilizing Downside Risk Measures

©2014 CFA Institute • cfapubs.org Third Quarter 2014 • 43

can represent either performance or risk mea-sures depending on how they are calculated. For example, backward-looking beta calculations based on the historical portfolio’s returns versus the market’s past returns represent performance measures. Forward-looking beta calculations based on the prior returns of the securities cur-rently held in the portfolio versus the market rep-resent risk measures.

Risk Measures. Classic names for risk mea-sures include VaR, ex ante tracking error, and return on risk-adjusted capital. VaR is a statistical technique that, when used by banks, is designed to quantify how much they could lose as a prob-able worst outcome in 1 trading day or 10 trading days. When used by asset managers, however, it can instead be used to quantify how much a port-folio could lose over, for instance, an ordinary year. Ex ante tracking error is going to be the potential underperformance of the benchmark. The return on risk-adjusted capital is defined as net income divided by the allocated risk capital and is typi-cally used by banks or leveraged market partici-pants rather than by traditional asset managers. The allocated risk capital represents a firm’s port-folio capital adjusted for a maximum potential loss based on the probability of future returns or volatility of earnings. Risk measures provide an early warning when portfolios have changed sig-nificantly and before the change is crystallized in performance.

Risk measures are often bound by the same market history as performance variability mea-sures, with the exception of a family of measures called “scenario” risk measures. Investment ana-lysts often cannot predict whether a portfolio will gain or lose, but they can show which portfolios have wider ranges of potential returns and thus a greater potential for loss. Performance measures are more readily available; risk measures are more difficult to come by but are becoming increasingly available.

Understanding Key Risk-Adjusted Performance MeasuresNow I want to discuss classic performance vari-ability measures and how to use them for manag-ing risk within client portfolios. I will focus on the Sharpe ratio and information ratio.

Sharpe Ratio. The Sharpe ratio is defined as the annual portfolio return in excess of the risk-free rate divided by its variability during the period. Its variability is quantified in terms of the standard deviation of returns measured daily, weekly, or

monthly over the same past period. The formula for the Sharpe ratio is

Portfolio return Risk-free rateStandard deviation of portf

−oolio return

.

The Sharpe ratio provides a measure of the quality of absolute performance. It is beneficial to compare with the same statistic shown for the peer benchmark. The goal is to maintain a Sharpe ratio of greater than 1.0. This measure is sometimes hard to achieve, but based on the statistics available for peer sets, greater than one is considered good.

Information Ratio. The information ratio is defined as how much a portfolio returned in excess of its benchmark divided by how much it could have underperformed (i.e., the ex post tracking error). The formula for the information ratio is

Portfolio return Benchmark returnStandard deviation of ben

−cchmark relative return

.

The information ratio provides a measure of the quality of relative performance. The goal is an infor-mation ratio of greater than 0.4–0.5. In some efficient or competitive markets—for example, large-cap US equities—a ratio greater than the 0.25–0.30 range may be excellent. For absolute return strategies, the Sharpe ratio and information ratio are equivalent. In the industry, managers typically use the 0.5 level (as opposed to 1.0) as the standard rule of thumb for a good ratio, even though their benchmark is cash.

Downside Capture. Sometimes managers pro-vide downside capture, which measures a portfo-lio’s return when market benchmark returns are less than zero. Downside capture helps quantify what percentage of down months for the benchmark are down months for the portfolio. Downside capture is calculated by dividing the cumulative return of the portfolio in the period when the corresponding benchmark return is less than zero by the cumulative return of the benchmark in periods when the return is less than zero. A less risky portfolio should have less downside capture than its benchmark. So, if a portfolio has a return of –9% and the benchmark’s return is –11%, the downside capture is 82% because the portfolio had less loss during the down periods than the benchmark.

Other Measures. The Treynor ratio, Jensen’s alpha, and semi-standard deviation focus on sub-sets of volatility, such as only downside volatility. They may also allow a manager to be more precise about which risk-free rate and which portion of volatility (systematic versus total) is used in the computation. But I am skeptical about the use of measures that focus only on the down periods. I believe that both up and down periods provide a

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more objective view, and a rapid increase in fund returns can also be indicative of risk. This view was particularly true during the period of the internet bubble in 1995–2000 when the equity markets, particularly internet stocks, rocketed upward but showed few downward movements until the end of the bubble. I would argue that semi-standard deviation was misused to understate the risk of this upward-trending market but that standard devia-tion correctly showed a high level of variability in returns during the period.

Volatility and DrawdownFor illustration purposes, Figure 1 shows two hypothetical funds’ performance relative to a benchmark. Fund A mirrors the benchmark fairly well and actually slightly exceeds the benchmark toward the end of the time horizon. Fund B has much greater volatility but ends at the same value as Fund A at the end of the time horizon. If an investor purchased Fund B with the expec-tation that it would have markedly different performance than the benchmark and have the opportunity for outperformance, then Fund B has those characteristics; it is not objectively a poor investment. But if the investment period had been shortened, Fund B could have realized a large loss relative to the benchmark. Volatility matters, particularly when investors need to withdraw

funds or sell a fund holding before the long-term expected return is achieved.

Table 1 gives the annual return, standard deviation of return, and Sharpe ratio for the two funds and benchmark shown in Figure 1. Fund A, which tracked closely with the benchmark, outperformed the benchmark annual return and had a higher Sharpe ratio of 1.37 compared with the benchmark’s Sharpe ratio of 0.99. Fund A was actually more moderate than the benchmark, as its lower standard deviation of return shows, but still managed to outperform it. Fund B had more than three times the volatility of the benchmark but outperformed the benchmark annual return. Fund B’s Sharpe ratio was 0.35 compared with the

Figure 1. Performance of Hypothetical Funds and a Benchmark

Fund A Fund B Benchmark C

Value

125

120

115

110

105

100

95

90

85Time

Table 1. Return and Volatility Information for Hypothetical Funds and a Benchmark


Annual return 6.0% 6.0% 5.2%

Standard deviation of return

4.2% 16.3% 5.0%

Sharpe ratio 1.37 0.35 0.99

Benchmark relative return

0.65% 0.65%

Standard deviation of benchmark rela-tive return

3.1% 16.0%

Information ratio 0.21 0.04



benchmark’s ratio of 0.99. So, both funds outper-formed by 65 bps relative to the benchmark, but their standard deviations relative to the benchmark are very different. The ex post tracking error, which is the standard deviation of the benchmark relative return, equals 3.1% for Fund A and 16% for Fund B. In other words, Fund B had the volatility to either outperform or underperform the benchmark by 16%. Their information ratios are also both posi-tive: Fund A is 0.21 and Fund B is 0.04.

To illustrate the importance of the endpoint, I changed the hypothetical example only slightly so that Fund A’s performance goes down to slightly below the benchmark right at the end of the time series. Table 2 shows the updated information. Despite mostly tracking the benchmark during the period, Fund A underperformed by 12 bps over the full period. So, in the final month, Fund A forfeits a small amount of its benchmark relative perfor-mance and thus underperforms for the full period, and its information ratio changes to negative.

It is important to remember that these risk measures are highly dependent on the endpoint and best paired with the full series for the great-est insight. In addition, smaller Sharpe and infor-mation ratios have poor discriminatory power. If these ratios are close to the significant levels mentioned earlier (e.g., a Sharpe ratio of 1.0 or an information ratio of 0.4–0.5), then they should reasonably indicate strong performance, but lev-els around zero may represent more noise rather than provide an accurate signal. Another issue for analysts is to beware of measures that aver-age extreme periods, such as 2008–2009, with dull periods. The results are often misleading because of offsetting returns within the periods. For exam-ple, risk calculations that include the financial cri-sis period during 2008 will likely give an extreme statistic, especially if it includes both 2008 and 2009. Analysts may not realize that the downside

and upside were so extreme because they offset one another.

Finally, as small numbers converge toward zero, they have less explanatory power for client portfolio calculations. Small numbers are indicat-ing less and are more likely representing noise in the statistics. But a strong and large positive num-ber has more explanatory power than small num-bers that vary around zero. For example, the small negative information ratio for Fund A in Table 2 does not tell the whole story.

Volatility is meaningful, but it is not the only metric for assessing past performance. When assets have a symmetrical return distribution, a normal distribution can be used to understand the possible range of their returns. To estimate the worst-case scenarios and to be prepared for the effects of a bad day, a manager can multiply the standard devia-tion by a known Z-score. But when an asset has an asymmetrical return profile, also called “fat tails,” managers cannot estimate its potential losses by knowing its standard deviation alone. Thus, it is important to understand which assets have asym-metrical return distributions.

Asymmetrical Returns. Asymmetrical returns come from such elements that can increase down-side risk as credit default risk, selling (writing) options, long–short or hedging strategies that rely on constant correlation, illiquid markets, over-concentrated positions, and positions too large for the market liquidity.

Credit default risk increases the returns on many fixed-income funds, but it also causes a fat left tail and makes return distributions asymmetri-cal. Selling options increases the left side of the tail returns (losses) and does not increase the right side of positive return distributions (gains) beyond the premium income earned for writing the options. Long–short or hedging strategies that rely on con-stant correlation do not mathematically create a bigger left tail the way sold options do. These strat-egies combine pairs of trades or one hedge after another, and if the correlations between the pairs change, it can create unusual losses or gains. For example, hedging strategies when the underlying asset is not hedged effectively can result in larger realized losses.

Illiquid markets and over-concentrated posi-tions that are too large for the current market liquid-ity can be difficult to identify. For some assets, the size of the position relative to daily average trading volume is used as an indicator. For example, if a large fund purchases so much of a popular stock such that its holding represents 90% of the daily average trading volume, the historical standard deviation of the stock could be measured. But if the

Table 2. Return and Volatility Information for Hypothetical Funds and a Benchmark




4.2% 16.3% 5.0%



–0.12% 0.65%


3.2% 16.0%

Information ratio –0.04 0.04



fund sold that entire stock position in one day, it would not experience that historical standard devi-ation. It would create a loss because of its outsized position. On the equity side, the rule of thumb is that market participants do not like to represent more than 10% of the daily average trading vol-ume. It is important to know whether a position is too large relative to the market liquidity because it will affect the downside risk when it is time to sell.

When liquidity is materially damaged, as it was during the financial crisis, the normal statis-tics do not apply, and liquidity is the hardest to track. Those in risk management spend a lot of time trying to obtain good metrics about the num-ber of market makers, bid–offer spreads, and the daily average trading volume. This information is sought for learning purposes going into the next period of history.

Liquidity is like a sold option. Poor liquidity during relatively healthy markets will mean very bad liquidity during a liquidity crisis. If investors purchase an asset that is not liquid, or they are over-concentrated in one that is not liquid, the asset performance will never be better than historical measures of risk because the measures were based on whatever holding sizes are normal for that mar-ket, but the asset performance can be a lot worse.

Thus, for strategies with asymmetry or fat tails, a metric beyond standard deviation is needed to fully understand downside risk and to describe past returns to investors.

Pairing Maximum Drawdown with Volatility. When advisers hear about a strategy that combines low standard deviation and high returns, then it is time to look for the elements of asymmetrical risk just described to determine whether they are in acceptable proportions or whether the propor-tions are distorted. A good metric for uncovering the downside risk elements in a fund is to look at the maximum drawdown.

Unlike standard deviation, which averages performance over a given period, maximum drawdown highlights the worst period (e.g., day, month, or quarter) of performance for a strategy in a given period. The length of period selected should be whatever is relevant for a manager’s client. To fully understand the worst-case sce-nario for a fund or portfolio, pair volatility (e.g., standard deviation) with an additional maximum drawdown statistic.

Using the same hypothetical funds and bench-mark from Figure 1 and Table 1, Table 3 shows the maximum one-month drawdown for each. The more volatile strategy, Fund B, has a dramatic down month that should not happen if it was supposed to be a safe fixed-income-type holding. Similarly,

relative to the benchmark, it markedly underper-formed. Managers and fund families understand drawdown because they know that it is somewhat disingenuous to only disclose standard deviation. Investors should ask, “What was the worst single month (or whatever period the investor prefers) for the strategy in the backtests?”

The maximum drawdown statistic is more revealing for strategies that appear to have a low standard deviation, high returns, and asymmetrical return profiles, which the funds in Table 3 did not contain. Long-trending bubbles (e.g., the internet stock boom from 1995 to 2000 or the housing bub-ble from 2003 to 2007) can still defy this measure until they collapse. For asymmetrical strategies, it is good practice to pair a maximum drawdown sta-tistic with standard deviation when describing past performance and simulating future performance.

Implementing a Risk Management StrategyNow I will turn from using performance variability numbers to using forward-looking risk measures. VaR and ex ante tracking error are two key forward-looking risk measures that can be used, along with consideration of client objectives, to implement a risk management strategy in client portfolios.

Value at Risk. VaR is the potential loss in net asset value for a given holding period (e.g., 1 day, 10 days, or 1 year), at some confidence interval (e.g., 99%, 95%, 1 downside standard deviation, or 84.15%). For example, banks use short hold-ing periods and high confidence intervals (e.g., 10 days at 99%), whereas asset managers tend to use longer holding periods and lower confidence

Table 3. Return, Maximum Drawdown, and Volatility Information for Hypothetical Funds and a Benchmark




4.2% 16.3% 5.0%

Maximum one-month drawdown

–1.8% –6.1% –2.3%



0.65% 0.65%


3.1% 16.0%

Maximum one-month underperformance

–1.9% –7.5%

Information ratio 0.21 0.04



intervals (e.g., 1 year at 84.15% confidence). So, in one use of the VaR number, banks measure how much they could lose in 10 days with 99% confi-dence and have several multiples of that amount set aside, as mandated by the Basel regime. In asset management, funds are usually provided upfront capital by investors; VaR does not need to be used to measure how much capital is needed. The asset manager’s main concern is quantifying potential future losses for a given strategy. Some given number of days of history is used to mea-sure the volatility of market risk factors and the correlations between them; sometimes exponen-tial smoothing is applied.

VaR is a flexible number, and it can provide a good forecast of how much a fund could lose in an ordinary year, month, or week. The one-year, 84.15% measure, which is equal to one downside standard deviation, indicates the kinds of losses that are possible at the center of the probability distribution. Expected income can be added to this loss measure to estimate how much a portfolio could lose in an ordinary year; for example, if the strategy could lose 10% in market value according to the VaR measure but is regularly expected to earn 3% in income, the total potential loss in an ordinary year is about 7%. Managers can also approximately double the measure (multiplying a one standard deviation measure by a Z-score of 2.3268) to esti-mate a 99% worst-case year—except if the strategy is asymmetrical.

Once the VaR is calculated, the next step is to compare it with other portfolios to determine whether the risk is relatively high. Managers can compare the VaR with• the same measure for other portfolios of a simi-

lar strategy, if available;• a portfolio’s realized standard deviation of past

returns;• a peer set’s standard deviation of past returns;

or• past losses for this or similar portfolios that

were deemed unacceptable.For example, how does the VaR calculated

based on a one-year, 84.15% confidence interval differ from the standard deviation of realized performance? This measure uses the risk factors in the current portfolio (new portfolio, old mar-kets) rather than the historical portfolio’s realized returns (old portfolio, old markets). Knowing what is possible in an ordinary year helps advisers begin to understand potential downside and place their clients in investments that have an accept-able range of risk of loss.

Ex Ante Tracking Error. Ex ante tracking error is also referred to as relative VaR. It is defined as the potential underperformance of a given port-folio strategy compared with the benchmark, and it is usually calculated based on a one-year period and one standard deviation confidence interval. Ex ante tracking error stands alone as a measure of potential underperformance. Unlike VaR, for which expected portfolio income is subtracted from the measure, there is no regularly expected amount of benchmark outperformance that is sub-tracted from ex ante tracking error. To determine whether ex ante tracking error is relatively high or whether active risk is relatively low, managers compare the measure with• the same measure for comparable portfolios, if

available;• any standard for the portfolio—for example, an

unacceptable level of underperformance;• the portfolio’s ex post realized tracking error; or• a peer set’s ex post realized tracking error.

Ex ante tracking error is different from ex post tracking error in that it uses the risk factors in the current portfolio rather than the realized returns of the historical portfolio.

Implementing a Risk Management Strategy. To implement a risk management strategy for client portfolios, the most important step is to understand the liabilities or the objectives of each client. The liabilities or objectives of the client should be understood relative to the timing of the required cash flows and what market factors can increase those liabilities. Then it is important to understand a client’s tolerance for both annual volatility and shorter-term drawdowns. After these steps are completed, construct a portfolio in which investment values increase when liabilities increase and stay within the volatility and draw-down tolerances.

In terms of the clients’ expectations, ask what they need money for and when they need it. For pension funds, advisers should look at actuarial studies of the timing and magnitude of their liabilities and how variable the outcomes can be. Advisers model their clients’ liabilities (e.g., liquid-ity requirements) in terms of timing and match those timing requirements with assets. The sooner the liabilities are required, the less variability the portfolio can tolerate, and the assets required to meet those liabilities will likely be short-term high-quality fixed-income securities.

The assets required for funding the time-specific liquidity requirements can include the entire spectrum of asset classes. More common assets will be fixed-income securities and equi-ties. Liabilities will represent actual cash flow



requirements to be met with the asset classes that the advisers are investing in. An important factor is a high correlation between the investments and the stream of expected payments to ensure that there is a high probability of meeting the client’s objectives. For example, if the liability is short term, then it will correlate well with short-term fixed-income investments. But if the liability is long term, it will require the adviser to consider each asset class separately relative to the risk tolerance of the client, as well as to construct a portfolio of investments that can maintain real value. For long-term liabilities, equities, long-term fixed-income securities, and real assets are common asset classes to use.

ConclusionI hope I have convinced you to consider using different downside risk measures as part of a risk management strategy. Critically analyze current and prospective portfolios for asymmetrical return profiles that exhibit fat tails, and then show both standard deviation and maximum drawdown mea-sures when describing past performance. Also, be sure to understand the limitations of the Sortino ratio and semi-standard deviation when applied during up-market cycles. These practices will enable investment managers to better minimize future losses and optimize risk-adjusted returns.

This article qualifies for 0.5 CE credit.


Q&A: McCarthy

Question and Answer SessionMichelle McCarthy

Question: Can you provide more detail about implementing a risk management strategy? How useful are derivative overlays?

McCarthy: If there is too much risk for the client in the cur-rent portfolio, the first step is to assess the asset allocation, which drives most downside risk. There are times when derivative overlays are perfect for portfolio rebalancing. If a long-term fixed-income manager is concerned about interest rates rising and does not want to sell investment positions in the portfolio, then it is possible to use derivatives to hedge the interest rate exposure (e.g., reduce the duration).

Sometimes clients or manag-ers prefer to use a derivative overlay to change a portfolio’s risk profile without having to sell assets and generate capital gains. To accomplish this, manag-ers may end up having to use large derivative positions, and the overlay will show a lot of profit or loss in those cases. For example, if rates rise, certain derivative contracts pay off, but if rates decrease, then derivative positions will result in a realized loss.

Although these fluctuations will usually be offset in the port-folio being hedged, the tax treat-ment can be different, and that is something to consider. There are times when hedging is optimal, but the principle is to determine whether the client’s liabilities or liquidity requirements are being managed correctly.

Question: Does Bloomberg or some other third-party ven-dor have the toolkit to enable an adviser to do the analysis efficiently?

McCarthy: Bloomberg has many series to compute historical volatility, which is similar to the standard deviation measures I discussed. The COMP function compares the investment with its relevant benchmark, and the PORT function provides many risk statistics.

FactSet and other vendors also provide standard deviations and the information ratios for various funds. Bloomberg pro-vides historical datasets on many different funds, but if they do not have the datasets available, then customers can request additional coverage.

Question: How do you measure the risk for VIX-based strategies?

McCarthy: The VIX (Volatility Index) is a contract on implied volatility. The near-term VIX is an indicator of the stock market’s expected movement (up or down) over the next 30 days. It represents equity market volatility and increases when the options that are used to buy or sell equities, and the level of volatility implied in their prices, increases. VIX contracts are used by managers to hedge the vari-ability in investment portfolios or for diversification purposes. Managers quantify risk for VIX-based strategies using the methods I discussed for portfo-lios with asymmetrical return profiles.

Question: Can you explain why long–short strategies cause asym-metrical return distributions?

McCarthy: Long–short strategies by themselves might not lead to asymmetrical returns. Strategies that rely on tight hedging, or long–short strategies, involve buying undervalued stocks and

selling overvalued stocks. These strategies might appear to have low risk, but if the correlation between the long side and the short side changes in ways not recently seen in history, it can generate surprising performance. Certain funds use hedging to actively manage duration risk. But funds that are purchasing high-yield bonds and using hedging contracts based on government securities could incur higher realized losses if the correlations diverge.

For example, during 2008, many high-yield funds incurred realized losses as market partici-pants demanded higher-quality risk-free investments. Although the bundle of investments in high-yield funds that have been hedged to reduce interest rate risk may appear to have low risk, the slightest decoupling of corre-lation can cause losses if the mar-ket suddenly demands higher quality and if yields are rising for low-quality assets while they are falling for the high-quality assets that are the basis of the hedge. In these cases, both parts of the portfolio can show losses instead of hedging one another.

Although long–short equity funds may claim a beta of 0, a lack of correlation can cause their beta to depart from 0. For the most part, long–short equity funds are not meant to be hedged perfectly but to have risk on both the long and short sides. When both sides move to the same degree, they can seem perfectly hedged, but part of the strategy is that both sides should move somewhat differently, ide-ally generating alpha but some-times leading to risk.



Question: Have you found Monte Carlo simulation to be use-ful in downside risk measures?

McCarthy: When using risk measures, such as VaR, manag-ers also need simulation-based models to test outcomes with strategies that use options. Options are embedded in certain fixed-income instruments, such as call and put options and prepayment options relevant for mortgage-backed securities. To determine the downside of these instruments, it is best to re-price them at various market points. Models based on Monte Carlo simulation are good to use in a VaR calculation because they re-price all the factors in the portfo-lio for various market conditions and show at what point options move in and out of the money.

Question: Do you believe the liquidity risks related to risk parity strategies are similar to the portfolio insurance strategies of the 1980s?

McCarthy: Risk parity strategies invest in a range of uncorrelated assets to keep risk levels or dol-lar exposure constant. Portfolio insurance strategies rely on sell-ing as the market decreases and buying as the market increases, which is what banks do when they write an option. That option has negative convexity or negative gamma. If the market gaps, the bank loses more than expected because it is not able to instantly transact in little incre-ments as the market increases or decreases.

Large market movements up and down may trigger losses for a portfolio insurance strategy, which is intended to create returns in stable markets, although it may generate higher losses during volatile market conditions. The portfolio insur-ance strategy ingredients exac-erbate gap risk and exhibit more risk during a financial crisis.

Question: What is your view of the efficiency of concentrated positions in publicly traded stocks, and are there hidden risks? For example, how would an investor with $100 million of a stock put a collar on that position?

McCarthy: One approach is to sell covered calls on the position and cap the upside; it will earn a good premium as part of that. The problem is if it really goes up, assets have to be liquidated to pay out under that option. If the goal is to not lose control of the stock, then selling covered calls will not work if the mar-ket moves up and other assets cannot be liquidated to pay the unrealized gains of the stock. Other liquid assets have to be available to cover the entire position if selling the stock is not an option.

Question: Why do some inves-tors in certain strategies with asymmetrical risk remove the upside volatility while retaining the downside risk to the extent that the loss would exceed the premium?

McCarthy: There are a number of covered call funds available for investors, and generally, investors who buy options tend to slightly overpay for them; selling options is usually a better deal over the long term. Covered call funds normally have lower volatility and higher returns than other funds they might be benchmarked to.

So, it is not too ridiculous to consider covered calls as a risk management strategy. But it is important to remember that it is not insurance. I think of it this way: If I had a bus, I could buy an insurance contract that costs $1,000, or I could decide that the 10 cents people pay me to ride the bus will be sufficient over time to pay for any bus crashes I might have. That second option is not an insurance strategy,

and that is what selling calls does. Investors get a little bit of premium, and might even earn a little bit more than the eventual payoff of the options, but it is not insurance.

Insurance is buying a put for the downside that pays off if the market crashes; it is expensive but actually protects you in a downturn. Selling a call provides a small amount of extra income that helps investors a very small amount when the markets are careening down and stops them from enjoying the upside. Over the long term, though, the options pay off less (and prevent the enjoyment of market upside less) than the investor is getting paid for them, and that is why it is a popular strategy.

Question: As structured prod-ucts have become more main-stream, how would you screen the wide variety of products that do not present on term sheets as being a product with an asym-metrical return?

McCarthy: Providers of these instruments are obligated to disclose downside risk, behavior in various market environments, upside risk, and the relevant benchmarks. Some investors may buy these instruments because they have strategies that align with these instruments that have asymmetrical returns.

Managers need to ask various questions: Do my clients need this structure in their portfolio? Is there some reason why my clients require an option embedded in a bond? Could I simply have sold put options on my own and replicated the embedded derivative?

It is important to understand the distinct components that are combined in the structured product. Few investments in the business have zero risk, so always think about the client’s risk tolerance.

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