CHAPTER
10
CHAPTER 10
The Interdependence of Managed
Futures Risk Measures
Bhaswar Gupta and Manolis Chatiras
P
ractitioners today are faced with a wide choice of methods to measure
return and risk in portfolios, either in absolute or relative terms. The
Sharpe ratio, maximum drawdown, and semideviation are common exam-
ples. We classify 24 such measures into six groups and attempt to gauge
how the measures interact, by using data on five different CTA strategies.
For each measure, two groups of portfolios are created, containing CTAs
with the lowest and highest values of the measure. We find evidence of
high correlation between the measures in some of the CTA strategies,
pointing to information overlaps and suggesting that some of these meas-
ures may be redundant.
INTRODUCTION AND REVIEW OF THE LITERATURE
The managed futures industry has grown from just under $1 billion in 1985
to more than $40 billion as of June 2003. This growth has led to closer
scrutiny of the diversification properties as well as risk management of man-
aged futures. The term “managed futures” represents an industry composed
of professional money managers known as commodity trading advisors
(CTAs) who manage client assets on a discretionary basis using global
futures and options markets (CISDM 2002). The risks in managed futures
are inherently more complex than traditional investments as they undergo
rapid change over time. Hence a thorough understanding of the risks of the
different market segments CTAs trade in is essential to effectively manage
these risks. This chapter examines risk surrogates for certain CTA portfolios.
The risks in the different market segments have been explored in sev-
eral articles. Tomek and Peterson (2001) have reviewed risk management

standard deviation, downside deviation, semideviation, and maximum
drawdown. Using data from a large hedge fund of funds over the period
December 1991 to December 2000, he analyzes out-of-sample performance
to predict results in the nonoverlapping subsequent period of investment in
each hedge fund. He finds that historical standard deviation tends to be
somewhat helpful in predicting future risk. He also finds that correlation
between preinvestment standard deviation, downside deviation, and maxi-
mum drawdown is significant. Gordon concludes that standard deviation
appears to be a better predictor of future losses than downside risk measures
such as historical downside deviation and maximum drawdown. Although
this advantage is not statistically significant for some of the downside risk
measures, he notes that standard deviation should probably be favored over
all other downside risk measures because it is simple and well understood.
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In this chapter we analyze the significance of the same 24 risk measures
used in Daglioglu and Gupta (2003b) for certain CTA portfolios. The 24
measures are used as much in CTA performance reports as they are in hedge
fund reports. Our results shed greater light on the implications of these
measures for particular CTA strategies. They also provide a clearer under-
standing of the interdependence of these two measures for certain CTA
portfolios. We provide empirical evidence on the redundancy of certain risk
surrogates, to help investors determine the relevance and applicability of
these risk measures when evaluating CTA portfolios.
In the next section we describe the methodology used for this study.
Then we describe the data, present the empirical results, and conclude.
METHODOLOGY
We study the 24 risk measures that were analyzed in Daglioglu and Gupta
(2003b) to ascertain the degree of informational overlap among them. We
use correlation analysis in our study. We divide the degree of correlation

CTA managers who had complete return data for the period from January
1998 to July 2003. The CTAs covered five strategies:
1. Agriculture
2. Currencies
3. Diversified
4. Financials
5. Stocks
Using these monthly rates of return, we calculated the 24 risk measures
for the overall period, January 1998 to July 2003. These risk measures are
indicative of the wide range of risk surrogates suggested in the literature on
CTA analysis and portfolio management.
We then ranked all of the CTAs by these 24 risk measures for the five
different CTA strategies. Next, we took the first half and second half to
construct bottom 50 percent and top 50 percent portfolios for these strate-
gies. In other words, we created 48 portfolios (24 portfolios for bottom 50
percent, 24 portfolios for top 50 percent) for each CTA strategy. Tables
10.1, 10.3, 10.5, 10.7, and 10.9. present annualized returns, standard devi-
ations, and Sharpe ratios of these portfolios and Tables 10.2, 10.4, 10.6,
10.8, and 10.10 present the correlations between the portfolios.
EMPIRICAL RESULTS
Agriculture
Table 10.1 presents summary statistics for the agriculture portfolios, and
Table 10.2 presents the correlation matrix. The top 50 percent monthly
standard deviation, top 50 percent gain standard deviation, top 50 percent
loss standard deviation, and top 50 percent semideviation yield exactly the
same results as do the bottom 50 percent portfolios for the four risk meas-
ures. Similarly the top 50 percent portfolio of the up percentage ratio yields
the same results as the top 50 percent portfolio of the down percentage ratio,
and the bottom 50 percent portfolio of the up percentage ratio yields the
206 RISK AND MANAGED FUTURES INVESTING

study. Table 10.3 presents the summary statistics for the currency portfo-
lios; Table 10.4 presents the correlations among the portfolios. There were
only two instances of perfect correlations, the top and bottom 50 percent
monthly standard deviation portfolios with the top and bottom 50 percent
average monthly gain portfolios, and the top and bottom 50 percent semi-
deviation portfolios with the top and bottom 50 percent loss standard
deviation portfolios. There were several instances of high, moderate, and
low correlations. Of the 27 funds, three indicated that their trades had a
short-term time horizon; four indicated that their trades had short-,
medium-, and long-term horizons. Eight of the funds indicated that their
trades had a medium-term horizon; four indicated that they had a long-
term horizon. Two indicated that they traded intraday. Seven of the funds
were classified as discretionary, 15 as systematic, 2 as trend-based, and
3 as trend-identifier.
There is considerable variety even within the strategies. For example, a
certain fund that was classified as systematic and short term had a correla-
The Interdependence of Managed Futures Risk Measures 209
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tion of only 0.19 with another fund that was classified as systematic and
medium term for the time period studied. Another pair where both were
classified as systematic and medium term had a correlation of 0.25. Sys-
tematic funds can be either trend followers or contrarian; in this case one
was a systematic trend follower and the other was a systematic non-trend
210 RISK AND MANAGED FUTURES INVESTING
TABLE 10.3 Summary Statistics for Currency Portfolios
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TABLE 10.4 Correlations for Currency Portfolios
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follower. However, a pair where both funds were classified as systematic

based, and 3 were trend identifiers. Clearly the systematic or trend-based
funds dominated the portfolios. The return patterns of these portfolios sug-
gest that they have similar risk characteristics.
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The Interdependence of Managed Futures Risk Measures 213
TABLE 10.5 Summary Statistics for Diversified Portfolios
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TABLE 10.6 Correlations for Diversified Portfolios
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The Interdependence of Managed Futures Risk Measures 215
TABLE 10.7 Summary Statistics for Financial Portfolios
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TABLE 10.8 Correlations for Financial Portfolios
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The Interdependence of Managed Futures Risk Measures 217
TABLE 10.9 Summary Statistics for Stock Portfolios
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TABLE 10.10 Correlations for Stock Portfolios
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Stock Portfolios
Table 10.9 presents the summary characteristics of the stock portfolios;
Table 10.10 presents the correlations. Several portfolios were perfectly
correlated. For example, the top and bottom 50 percent gain standard
deviation portfolios were perfectly correlated with the top and bottom
50 percent average monthly gain portfolios, and the top and bottom 50
percent information ratio portfolios were perfectly correlated with the