999) results for commodity pool trading volume. They
173
Eurodollar
Deutsche mark
Crude oil
Cotton
Corn
Copper
Coffee
FuturesMarkett−212.8(1.44)214.2*(2.84)190.5*(3.12)214.8*(2.39)712.4(0.82)1,729.7*(2.38)8,063.6(0.58)
t−1
−2.8(−0.31)20.1(0.27)251.7*(3.85)628.7*(7.03)−381.8(−0.44)−160.9(−0.22)−21,276.9(−1.52)1.4(0.15)38.9(0.52)−8.4(−0.14)230.7*(2.57)−445.0(−0.52)468.3(0.64)−6,149.2(−0.44)
t−3
t−47.3(0.82)141.1(1.87)−61.6(−1.01)63.8(0.71)2,117.2*(2.44)553.2(0.76)−25,505.1(−1.83)
Daily Price Change Lag
2.6(0.28)−24.2(−0.31)170.0*(2.62)196.4*(2.23)−29.8(−0.03)77.4(0.11)−15,490.5(−1.11)
t−5
−0.91
−144.2
−2.10
6.83
1,334.4*
−60,358.1*
3.77
542.2*
1.76
2.23
390.1*
2,667.6
0.98
t-statistic
21.3
Sum ofSlopes
Positive Feedback Regression Results for 13 Futures Markets, Large Managed Money Accounts, April 4,1994–October 6, 1994.
TABLE 8.10
0.02
0.01
0.02
0.35
0.15
0.05
−0.02
Adj.R2
174−0.2(−0.01)
−1.6(−0.08)
55.2*(2.66)190.6*(2.08)669.0(0.64)
42.2(1.92)
−135.8(−1.48)
−4,887.7*(−4.67)
Soybeans
S&P 500
68.4(0.07)
−62.7(−0.65)−1,422.8(−1.37)
−192.2*(-2.03)
83.7(−1.50)
−132.5(−0.13)
8.4(0.09)
6.1(0.28)
−533.1(−0.13)
71.3(1.27)
76.1(0.47)
t−5
−818.0
0.6
101.7
23,484.6*
541.4*
1,094.2*
Sum ofSlopes
−1.55
−0.98
1.89
2.97
4.01
2.74
t-statistic
0.14
0.04
0.05
0.14
0.11
0.07
Adj.R2
Managed money accounts are defined as large hedge funds and CTAs.The figures in parentheses are t-statistics to test the null hypothesis that a given slope parameter equals zero. The figures in the nextto last column are t-statistics to test the null hypothesis that the sum of the slope parameters for a given market equals zero.*Statistically significant at the 5 percent level.
Treasury bonds
−3,158.6(−0.79)
11,659.5*(2.92)
16,136.4*(4.05)
−619.6(−0.16)
Natural gas
64.0(1.14)
139.2*(2.48)
183.2*(3.26)
Live hogs
41.5(0.25)
405.8*(2.41)
543.2*(3.24)
27.6(0.17)
Gold
t−4
t−3
Daily Price Change Lagt−2
t−1
(continued)
Futures Market
TABLE 8.10
The Effect of Large Hedge Fund and CTA Trading on Futures Market Volatility
175
found statistically significant evidence of positive feedback trading by commodity pools in half of the futures markets studied and an average adjustedR2 across all markets of 0.12.An additional frame of reference is provided through analysis of thepositive feedback characteristics of the Commitment of Trader data asreported by the CFTC. The weekly reported open interest figures for eachweek of 1994 were used to compute noncommercial and commercialreporting traders’ estimated minimum trading volume (using the samemethodology as previously outlined for the MMA data set). Regressionmodel 8.7 was then estimated using these weekly volume estimates andweekly price changes. Tables 8.11 and 8.12, respectively, present theresults for the reporting noncommercial and commercial traders. The sumof slope coefficient estimates for noncommercial traders, presumably thegroup most closely related to MMAs, is positive for all 13 markets, and 6are statistically significant. With an average adjusted R2 of 0.27, past pricechanges explain more than three times the variation of trading volume fornoncommercial traders as compared to MMAs.8 These results indicatethat noncommercial traders in general exhibited more positive feedbacktrading tendencies than MMAs. The sum of slope coefficient estimates forcommercial traders, less obviously related to MMAs, is negative for 11 ofthe 13 markets, and 5 of the negative sums are statistically significant.Hence, negative feedback trading is generally observed for commercialfirms. The explanatory power of the regressions for commercial firms issimilar to that of noncommercial firms (average adjusted R2 of 0.26).9Profitability Tests According to Friedman (1953), in order for speculationto be destabilizing, speculators must buy when prices are above fundamental values and sell when prices are below fundamental values. This process
8Dale and Zyren (1996) report a similar level of explanatory power for positive
feedback regressions applied to noncommercial positions in crude oil, gasoline,heating oil, and treasury bond futures.9As Weiner (2002) points out, no conclusions should be drawn about price effectsof noncommercial versus commercial trading based on the results in Tables 8.11 and8.12. Since all futures markets are zero-sum games, correlations between noncommercial positions and past price movements necessarily imply just the opposite correlations between commercial positions and past price movements (assumingminimal trading volume on the part of nonreporting “small” traders). The resultsreported in Tables 8.11 and 8.12 are not sufficient to determine whether noncommercials (“speculators”) move prices and commercials (“hedgers”) follow, or viceversa. Results for both groups are reported only to provide a broader frame of reference for the hedge fund and CTA positive feedback regression results.
176
Eurodollar
Deutsche mark
Crude oil
Cotton
Corn
Copper
Coffee
FuturesMarket
t−1
30.4(1.26)358.6*(3.38)3,957.6*(3.46)736.2*(4.49)11,002.2*(7.94)8,500.7*(6.61)15,759.6(0.95)
1994–December 31, 1994
4.8(0.21)218.4*(2.06)7.5(0.01)686.9*(4.01)−1,612.3(−1.15)959.1(0.75)−17,710.7(−1.09)
t−2
t−4−1.9(−0.08)−230.8*(−2.23)1,876.6(1.64)−20.2(−0.12)−759.4(−0.55)−1,756.0(−1.37)17,072.4(1.03)
t−3−27.9(−1.16)102.1(0.98)113.7(0.10)101.6(0.62)−295.6(−0.21)−900.5(−0.70)1,094.9(0.06)
Weekly Price Change Lag
1.5(0.06)−173.1(−1.67)−701.3(−0.61)−232.4(−1.37)1,121.7(0.77)−844.3(−0.66)−5,494.9(−0.32)
t−5
3.642.86
1,272.1*9,456.5*
10,721.4
0.23
1.71
2.38
5,254.2*
5,959.0
1.12
0.16
t-statistic
275.3
6.9
Sum ofSlopes
TABLE 8.11 Positive Feedback Regression Results for 13 Futures Markets, Noncommercial Traders, January 3,
−0.01
0.49
0.60
0.40
0.18
0.34
−0.05
Adj.R2
177
2,395.0*(11.05)65.5(−0.40)20,531.1*(3.33)1,224.7*(5.68)325.6*(3.07)3,292.3*(2.15)
t−1861.2*(3.97)297.5(1.87)7,204.2(1.23)593.5*(2.88)61.1(0.56)−940.6(−0.59)
t−2317.2(1.52)136.4(0.87)2,533.5(0.44)31.7(0.16)105.5(0.95)−197.8(−0.12)
t−3108.5(0.52)52.0(0.33)99.0(0.02)273.6(1.33)−111.1(−1.00)−403.9(−0.25)
t−4
Weekly Price Change Lag
92.1(0.45)101.3(0.64)2,197.5(0.41)192.0(0.89)−217.41*(−2.06)1,620.7(1.06)
t−5
3,370.8
0.04
−0.03
0.450.19
4.38
2,315.5*
0.12
0.00
0.74
Adj.R2
0.55
2.15
32,565.3*
381.1
1.90
6.65
t-statistic
652.8
3,773.9*
Sum ofSlopes
Managed money accounts are defined as large hedge funds and CTAs.The figures in parentheses are t-statistics to test the null hypothesis that a given slope parameter equals zero. The figures in thenext-to-last column are t-statistics to test the null hypothesis that the sum of the slope parameters for a given market equals zero.*Statistically significant at the 5 percent level.
Treasury bonds
S&P 500
Soybeans
Natural gas
Live hogs
Gold
FuturesMarket
TABLE 8.11 (continued)
178
Eurodollar
Deutsche mark
Crude oil
Cotton
Corn
Copper
Coffee
Futures Market
t−2−20.5(−0.83)−163.6(−1.15)−330.1(−0.26)−813.73*(−3.93)−939.6(−0.39)−1,510.3(−0.89)23,183.3(0.91)
−23.2(−0.90)−397.8*(−2.80)−5,531.6*(−4.29)−872.87*(−4.41)−17,143.6*(−7.27)−12,334.1*(−7.26)−21,846.8(−0.84)29.8(1.16)−151.3(−1.09)109.3(0.08)−77.9(−0.39)1,739.0(0.73)415.2(0.24)18,208.1(0.68)
t−34.0(0.16)340.9*(2.46)−2,022.8(−1.57)−23.9(−0.12)2,338.5(1.00)1,821.7(1.08)2,174.2(0.08)
t−4
Weekly Price Change Lag
t−1
1994–December 31, 1994
7.5(0.29)187.8(1.35)506.6(0.39)277.1(1.35)935.3(0.38)1,260.6(0.74)25,372.7(0.95)
t−5
0.65
−2.25
−10,347.0*47,091.6
−2.32
−13,070.4*
−2.92
−7,268.6*
3.58
−0.56
−184.0
175.3*
−0.07
t-statistic
−2.4
Sum ofSlopes
TABLE 8.12 Positive Feedback Regression Results for 13 Futures Markets, Commercial Traders, January 1,
−0.03
0.54
0.55
0.40
0.26
0.27
−0.04
Adj.R2
179
t−2−971.21*(−3.49)−125.5(−1.18)−8,098.4(−1.25)−625.50*(−3.19)−125.9(−0.95)726.3(0.38)
t−1
−3,287.3*(−11.81)173.2(−1.60)−22,158.8*(−3.25)−1,360.1*(−6.62)−115.5(−0.90)−2,370.3(−1.31)
−470.8(−1.76)−123.4(−1.18)1,068.1(0.17)81.7(0.43)−235.0(−1.75)1,025.1(0.54)
t−3−189.8(−0.71)13.2(0.13)1,002.6(0.16)−553.50*(−2.82)29.6(0.22)−377.0(−0.20)
t−4
Weekly Price Change Lag
83.3(0.32)−155.3(−1.47)−2,214.4(−0.38)−169.8(−0.83)110.3(0.86)−1,338.8(−0.74)
t−5
−6.64−0.95−1.82−5.22−0.94−0.47
−217.8−30,400.9−1,448.1*−336.5−2,334.6
t-statistic
−3,864.6*
Sum ofSlopes
−0.05
0.00
0.53
0.12
0.04
0.76
Adj.R2
Managed money accounts are defined as large hedge funds and CTAs.The figures in parentheses are t-statistics to test the null hypothesis that a given slope parameter equals zero. The figures in thenext-to-last column are t-statistics to test the null hypothesis that the sum of the slope parameters for a given market equals zero.*Statistically significant at the 5 percent level.
Treasury bonds
S&P 500
Soybeans
Natural gas
Live hogs
Gold
Futures Market
TABLE 8.12 (continued)
180
RISK AND MANAGED FUTURES INVESTING
creates excessive volatility by driving prices past their fundamental values.Rational speculators, however, recognize the deviation from fundamentalsand take the opposite position, bringing prices back to a level reflecting theunderlying fundamentals. Rational speculators, therefore, make a profitwhile destabilizing speculators lose money. The following analysis of MMAestimated profits is based on this theoretical argument.The estimates of profits by MMAs during the period from April 4through October 6, 1994, are based on the mark-to-market technique usedby Hartzmark (1987) and Leuthold, Garcia, and Lu (1994). The pricechange (based on the close-to-close difference) on day t is multiplied by thenet open interest position held by MMAs at the end of day t − 1. The dailyprofit/loss figures are then aggregated across all days within a market foreach month to compute total monthly profit or loss for each market. Table8.13 presents the profit/loss estimates for each month and market. Notsurprisingly, results vary considerably through time and across markets.Total profits and losses by month range from a high of $785.1 million(September) to a low of −$539.2 million (August). Total profits and lossesby market for the entire six-month period range from a high of $430.7 million (coffee) to a low of −$234.5 million (S&P 500).Although the analysis is based on a relatively short time period, aggregating across all months and markets nonetheless provides additional statistical power. Under the assumption of independent price changes across the13 markets (which is probably not true for some of the markets, such as cornand soybeans), this analysis is similar to using 78 months of data for onemarket (6 months multiplied by 13 markets). The average profit across allmonths and markets is $30.6 million per month. The t-statistic to test thenull hypothesis of zero profits per month is 2.45 and statistically significantat the 5 percent level. The aggregate total profit across all months and markets is $397.6 million. While the statistical significance of average (or total)profits can be debated due to the lack of independence across some markets,the economic significance of the profits seems more apparent. A profit ofalmost $400 million in six months is an economically nontrivial amount.As noted, under the assumption of market efficiency, for speculativeactivity to be destabilizing, speculators must buy when prices are high and sellwhen prices are low. Trading in this manner should lead to trading losses asthe market price returns to its underlying fundamental value. The profit estimates reported here suggest that MMA trading is not destabilizing, butinstead is based on valuable private information. Of course, the economic significance of the profit results must be tempered to some degree by acknowledging the relatively brief time period over which the profits were earned. Itis well known that the returns of MMAs vary widely over time (e.g.,Schneeweis, Savanayana, and McCarthy 1991; Ackermann, McEnally, andRavenscraft 1999). In addition, it is theoretically possible for noise traders to
181
The Effect of Large Hedge Fund and CTA Trading on Futures Market Volatility
TABLE 8.13
Estimated Gross Profits in Millions for Large Managed MoneyAccounts in 13 Futures Markets, April 4, 1994–October 6, 1994FuturesMarket
April
May
June
July
Average TotalProfit/ Profit/August SeptemberLossLoss
Coffee
23.8
187.9
190.6
64.0
−22.3
−13.2
71.8
Copper
6.0
53.4
13.0
6.6
−1.6
−4.4
12.2
73.0
Corn
−0.6
−1.4
−22.8
10.8
−1.3
0.4
−2.5
−14.9
430.7
Cotton
12.3
−3.3
−39.2
−10.7
−1.6
−9.4
−8.6
−51.9
Crude oil
20.0
46.6
47.1
42.9
−64.6
−0.5
15.2
91.5
Deutschemark
13.0
3.1
40.1
−3.1
−17.4
14.5
8.4
50.2
Eurodollar
54.0
127.5
40.1 −168.3
−17.5
231.6
44.6
267.4
Gold
−6.9
−29.2
−20.4
−14.6
−10.2
−13.6
−81.7
Live hogs
4.9
11.5
5.9
−5.6
10.3
15.0
7.0
41.9
Natural gas −10.3
16.0
−27.2
−1.5
63.4
22.8
10.5
63.2
−0.6
S&P 500
−213.4
−79.9
251.0 −325.8 −388.1
521.7
−39.1
−234.5
Soybeans
−12.7
−17.7
−37.9
12.4
−3.1
14.6
−7.4
−44.3
−21.7
−40.3
−10.4
−42.1
−80.8
2.3
−32.2
−193.0
−10.1
21.1
33.1
−32.4
−41.5
60.4
—
30.6
TotalProfit/Loss −131.7
274.2
429.9 −420.8 −539.2
785.1
—
397.6
TreasurybondsAverageProfit/Loss
Managed money accounts are defined as large hedge funds and CTAs. Profits andlosses for the first four business days of October are included in the monthly totalsfor September.
survive, and even profit, in the long run if they are numerous enough andthe arbitrage capacity of rational traders is limited (e.g., De Long, Schleifer,Summers, and Waldman 1990).
CONCLUSIONThe first part of the chapter analyzed the relationship between hedge fundand CTA trading and market volatility. Regression models of marketvolatility were specified as a function of: (a) trading volume and open interest for large hedge funds and CTAs; (b) trading volume and open interestfor the rest of the market; and (c) day-of-the week effects. The regression
182
RISK AND MANAGED FUTURES INVESTING
results showed a small but positive relationship between the trading volumeof large hedge funds and CTAs and market volatility. However, a positiverelationship between hedge fund and CTA trading volume and marketvolatility is consistent with either a private information (e.g., Clark 1973) ornoise trader hypothesis (e.g., DeLong, Schleifer, Summers, and Waldman 1990).The second part of the chapter conducted tests to distinguish betweenthe private information hypothesis and the noise trader hypothesis. The firsttest consisted of identifying the noise component exhibited in return variances over different holding periods. The efficient market hypothesis impliesthat a q day holding period return variance should be equal to q times thedaily return variance. Only two of 78 estimated test statistics were significant during the six-month sample period, suggesting that a statistically identifiable noise component exists only in one market. Even in this market,however, the noise component was not significant for holding period returnsgreater than three days. Therefore, the variance ratio test results provide little support for the noise trader hypothesis during this six-month period.The second test examined whether positive feedback trading characterizes large hedge fund and CTA trading behavior. Statistically significant evidence of positive feedback trading was found in about half of the marketsstudied. However, because positive feedback terms explained just 9 percentof the variation in large hedge fund and CTA trading volume, it can be concluded that their trading decisions are influenced only in small part by pastprice changes. Furthermore, additional tests showed that noncommercialtraders as a group exhibit substantially more positive feedback tradingeffects than large hedge funds and CTAs.The third test consisted of estimating the profits and losses associatedwith the positions of large hedge funds and CTAs. For speculative tradingto be destabilizing, speculators must buy when prices are above fundamental values and sell when prices are below fundamental values. The implication of this hypothesis is that destabilizing speculators lose money and aredriven from the market, having no negative effects on market efficiency.Across all 13 markets, profits for large hedge funds and CTAs were estimated to be just under $400 million, a nontrivial amount. This fact suggeststhat the trading decisions of large hedge funds and CTAs are likely basedon valuable private information.Overall, the evidence presented in this study indicates that trading bylarge hedge funds and CTAs is most likely based on private fundamentalinformation. Futures return variances exhibited a significant noise component in only one market. In addition, large hedge funds and CTAs generatednearly $400 million in gross trading profits across the 13 markets. Thesefindings imply that large hedge funds and CTAs likely enhance market efficiency by bringing valuable fundamental information to the market throughtheir trading.
CHAPTER
9
CHAPTER 9
Measuring the Long VolatilityStrategies of Managed FuturesMark Anson and Ho Ho
ertain hedge fund strategies create investment positions that resemble along put option. Specifically, managed futures or commodity tradingadvisors have significant exposure to volatility events. This exposure is positively related to volatility much like a long option position. We identify andmeasure this long volatility exposure, which may not always be transparentfrom the trading positions of a commodity trading advisor. We also examineways to apply these long volatility strategies to improve risk management.
C
INTRODUCTIONThe managed futures industry has come full circle in its application over thelast 15 years. In the early 1990s, global macro funds were the predominantform of the hedge fund industry. These funds were primarily managedfutures funds run by commodity trading advisors (CTAs). As the 1990s progressed, other types of hedge fund strategies came to the forefront, such asrelative value arbitrage, event driven, merger arbitrage, and equity long/short.As these strategies grew, managed futures became a smaller part of thehedge fund industry.Now, however, managed futures have achieved a renewed interestbecause of their risk reducing properties relative to other hedge fund strategies. Specifically, most CTA strategies employ some form of trend-followingstrategy. These trend-following strategies pursue both up- and down-marketmovements in futures markets. These strategies also may be called momentum strategies because they follow the momentum of the market and thenliquidate their positions (or reverse them) when they detect that the momentum is changing or about to change.
183
184
RISK AND MANAGED FUTURES INVESTING
Whether we call managed futures trend-following or momentum strategies, they have one important characteristic: They capitalize on the volatilityin the futures market. Trend-following strategies tend to be “long-volatility”strategies; that is, they profit during volatile markets. Long-volatility strategies can be useful risk management tools for other active trading strategiesthat tend to be short volatility.We begin with a brief overview of the managed futures industry. Wethen measure the long-volatility exposure captured these strategies. Nextwe apply Monte Carlo simulation to estimate the value at risk for longvolatility strategies. Last, we demonstrate some practical risk managementstrategies that may be employed with managed futures.
BRIEF REVIEW OF THE MANAGED FUTURES INDUSTRYManaged futures is often referred to as an absolute return strategy becausetheir return expectations are not driven by broad market indices, such asthe Standard & Poor’s (S&P) 500, but instead by the specialized tradingstrategy of the commodity trading advisor. More specifically, their returnexpectations are an absolute level of return sufficient to compensate themfor the risk associated with trading in the futures markets. This absolutelevel is established independently of the return on the stock market.The managed futures industry is another skill