CFA Institute Downside Risk

42 • Third Quarter 2014 ©2014 CFA Institute • cfapubs.org
Utilizing Downside Risk Measures
Michelle McCarthy
Managing Director and Head of Risk Management
Nuveen Investments
Chicago
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
Forward
Measures 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.

©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 Measures
Now 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 rate
Standard 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 return
Standard 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

CFA Institute Conference Proceedings Quarterly
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 Drawdown
For 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
85
Time
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
Standard deviation
of return
4.2% 16.3% 5.0%
Benchmark relative
return
–0.12% 0.65%
Standard deviation
of benchmark rela-
tive return
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
Strategy
Now 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
Standard deviation
of return
4.2% 16.3% 5.0%
Maximum one-
month drawdown
–1.8% –6.1% –2.3%
Benchmark relative
return
0.65% 0.65%
Standard deviation
of benchmark rela-
tive return
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.
Conclusion
I 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.

QA: McCarthy
Question and Answer Session
Michelle 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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