Gold Hedge Funds Comparative Analysis

Gold and hedge funds: a comparative analysis June 2004 Page 1 of 8

Gold and hedge funds: a comparative analysisi

by

Katharine Pulvermacher Head, Asset Allocation Research

Introduction

Using ten years of data ending in April 2004, this paper sets out to analyse the relative advantages of investing in gold bullion and hedge funds, with particular emphasis on the diversification benefits afforded during periods of under-performance in stock markets, as proxied by returns on the S&P 500. Data

Five stockmarket indices were included in the analysis. These were the Nasdaq Composite (closing price); the MSCI EAFE in USD, the MSCI World Net Return Index; the Dow Jones Industrial Average (DJIA) Total Return index and the S&P 500 Composite Total Return index.ii The “generic” hedge fund data used was the CSFB/Tremont hedge fund index and two of the style-based sectors that comprise the index. Although other hedge fund indices exist, the CSFB/Tremont data presents the attraction of being available free of charge, has a transparent methodology and, although it specifically excludes smaller funds with a net asset value below $10 million, covers 85% of assets under management.iii It is an asset-weighted index that aims to minimise survivor-bias by not removing funds in the process of liquidation, thus capturing the potential negative performance before a fund ceases operation. As of April 2004, the index included 433 funds across ten style-based sectors. The share of each sector is shown in Figure 1b, while the box below provides a brief definition of each style. Figure 1a shows the market share of fund styles in December 2000, providing an indication of just how rapidly this asset class is evolving. The style-based sectors included in the analysis were Global Macro and Managed Futures, which we identified as the two styles to have some exposure to the gold sector. The CSFB/Tremont hedge fund index and sub-indices start from December 1993. The gold hedge fund index consisted of eight gold-oriented hedge funds.iv Long-only funds that may define themselves as hedge funds are specifically excluded from this index.v One fund that forms part of this index is also a constituent of the CSFB/Tremont Managed Futures index. Ten years of monthly data, starting from the end of May 1994, are available. However, until 1999 data covers only one fund with one other added in each of 2001 and 2002 and a further three in 2003. All hedge fund data is net of fees. Finally, the gold price used was the London PM fix on the last day of each month.vi This means that intra-month variation will not be captured, but the same is true of the other series analysed. Given that daily data was not available for hedge funds, in particular, we acknowledge the possibility that end-of-month effects may be present but in this instance could not be treated using an alternative methodology.

Convertible Arbitrage: exploiting priceinefficiencies between convertiblesecurities and stock Dedicated Short Bias: equities andderivatives portfolios with net short,“bearish” focus Emerging Markets: equity and fixed-income investments in emerging marketsworldwide Equity Market-Neutral: offsetting longand short equity positions that are beta-neutral, currency-neutral or both Event-Driven: corporate strategiesfocused on distressed securities, high-yield debt, Regulation D, and riskarbitrage Fixed-Income Arbitrage: exploitingprice inefficiencies between related debtsecurities Global Macro: directionalmacroeconomic strategies Long-Short Equity: directional equityand equity derivative strategies Managed Futures (CTAs): listedfutures strategies often driven bytechnical or market analysis Multi Strategy: multiple strategies Source: CSFB/Tremont

Figure 1a: Market share of fund styles, December 2000

19%6%

9%

6%

3%

48% 0%

3%

6%

Convertible Arbitrage

Dedicated Short

Emerging Markets

Equity Market Neutral

Event Driven

Fixed Income Arbitrage

Global Macro

Long/Short Equity

Managed FuturesSource: Handbook of Alternative Investments

Figure 1b: CSFB/Tremont Hedge Fund Index Weights

7%1%3%

5%

19%

8%13%

6%

6% 12%Convertible Arbitrage

Dedicated Short

Emerging Markets

Equity Market Neutral

Event Driven

Fixed Income Arbitrage

Global Macro

Long/Short Equity

Managed Futures

Multi-Strategy

Source: www.hedgeindex.com


Methodology

As mentioned, returns in this analysis are defined as the log monthly price change. The full period covered was May 1994 to April 2004, i.e. a total of 120 months. The relative performance charts shown in the Results section use indices with a base of 30th April 1994, except for the CSFB/Tremont indices which show performance from inception of the index and are therefore based to December 1993. For the correlation analysis, we calculated pairwise return correlations for the ten-year period ending 30th April 2004. We then ranked all returns using the returns on the S&P 500 as the asset of reference, and calculated pairwise return correlations for the thirty worst months of the S&P 500 and for the thirty best months of the S&P 500, i.e. the bottom and top quartiles. Although this provides some indication as to the trade-off between upside lost during the best months and the degree of protection during the worst, there are some drawbacks to this methodology. Firstly, financial time series often display autocorrelation, so drawing observations out of their time context should be done with caution as one cannot be certain that they are independently and identically distributed. By selecting data corresponding only to the best and worst 30 months of the S&P 500 we are explicitly ignoring the underlying return distributions and any lagged effects. Secondly, the best and worst month samples consist of 30 observations, which is not sufficient to draw any robust conclusions – one should be aware of small sample bias. Nonetheless, given the underlying frequency of the data, which is monthly, and the relatively short time period for which data is

available (120 months), the method used does serve to provide some insight into the impact of extreme events and the constraints on diversification benefits of the various asset types in good times and bad.

Figure 2: Stock market performance, 30 April 1994 = 100

0

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400

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600

700

Apr-94

Oct-94

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Apr-02

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Oct-03

Apr-04

DJIAMSCI EAFEMSCI WorldNasdaqS&P 500

Source data: EcoWin

Figure 3: Hedge fund performance, 31 Dec 1993 = 100

0

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450

Dec-93

Jun-9

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2

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Jun-0

3

Dec-03

CSFB/Tremont Hedge Fund Index

HEDG Global Macro

HEDG Managed Futures

Source: CSFB/Tremont

Figure 4: Precious Metals Funds - Performance, 30 April '94 = 100

0

100

200

300

400

500

600

700

800

Apr-94

Oct-94

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Apr-04

Long-onlyGold hedge fundsXAU IndexGold bullion, London PM fix

Source: Global Advisors; Bloomberg; EcoWin/LBMA


An alternative method would be to analyse the impact on the third and fourth moments (skewness and kurtosis) achieved by including different combinations of hedge funds, their subsets and gold in a portfolio of risky assets. This method has been applied by Mercer Investment Consulting, for instance, using returns relative to liabilities and without any disaggregation of the hedge fund data.vii Results

The relative performance of the three categories of data is presented in Figures 2 to 4 and in Table 1. Table 1 indicates that gold hedge funds provided the best returns over the four year period ending on 30 April 2004, although the Sharpe ratio for global macro hedge funds was far higher than for any of the other series analysed. Interestingly, gold bullion offered higher monthly average returns than those of the generic hedge fund index, although this is not the case once returns are adjusted for volatility, the Sharpe ratio for the CSFB/Tremont Hedge Fund Index being higher than all the other series with the exception of the Global Macro hedge fund index.

As one would expect, the performance figures for all the stock market indices are abysmal for the shorter period.

The longer (ten year) period also saw gold hedge fund returns rank first amongst the series analysed, although the generic hedge fund index had the highest Sharpe ratio. The longer period is dominated by the end of the long bear market in gold bullion, which reached its lowest point in 2001, with an annual average price of USD 271.04. However, for much of this period all was well with the world, with the raging bull market in equities reaching its peak in 2000, as Figure 2 reminds us.

4 years ending 30 April 2004 Average monthly returns Volatility Sharpe ratioviii

Gold Hedge Funds 1.94% 18.67% 1.39 HEDG Global Macro 1.42% 3.94% 4.66 HEDG Managed Futures 0.88% 13.77% 0.80 Gold, London PM fix 0.72% 13.58% 0.66 CSFB/Tremont Hedge Fund Index 0.62% 3.92% 1.97 DJIA Total Return 0.06% 17.12% 0.04 MSCI EAFE USD -0.35% 26.19% -0.16 S&P 500 TR -0.44% 17.11% -0.30 MSCI World USD Net Return -0.46% 16.21% -0.34 Nasdaq Composite (Close) -1.46% 34.83% -0.46

10 years ending 30 April 2004

Average monthly returns Volatility Sharpe ratio Gold Hedge Funds 1.53% 21.12% 0.95 HEDG Global Macro 1.26% 11.64% 1.39 DJIA Total Return 1.02% 16.13% 0.80 CSFB/Tremont Hedge Fund Index 0.97% 8.10% 1.52 S&P 500 TR 0.90% 15.83% 0.71 Nasdaq Composite (Close) 0.80% 29.18% 0.34 MSCI World USD Net Return 0.55% 14.60% 0.47 HEDG Managed Futures 0.53% 12.43% 0.53 MSCI EAFE USD 0.32% 19.73% 0.20 Gold, London PM fix 0.03% 12.55% 0.03

Table 1

Note: The XAU index is an index of shares of gold and silver mining companies listed on the Philadelphia Stock Exchange. It includes a total of twelve producers who may be hedged or unhedged.


The results of the correlation analysis are reported in Figures 5 to 7 and Tables 2 to 4. Over the entire reference period, returns on managed futures, gold bullion and gold hedge funds managed futures were least correlated with returns on the S&P 500. Of these, managed futures offered the best diversification, with a correlation coefficient of (-0.209). However, it is not significantly different from zero at a 5% level of significance.ix The only other series that had a negatively signed correlation coefficient (r) was gold bullion, with r = (-0.067). The return correlation for gold hedge funds and the S&P 500 was 0.013, i.e. effectively zero. It is perhaps surprising that returns on the CSFB/Tremont Hedge Fund index were positively correlated with those on the S&P 500 over the full period; however this statistic should be viewed in the context of the low volatility (8.10%) and high Sharpe ratio (1.52) reported in Table 1, the implication being that the basis for a generic allocation given these results would be the risk-adjusted returns rather than the pure diversification benefits with respect to equities. As one might expect, returns on the other stock market indices are highly correlated with those on the S&P 500. The results for the 30 worst months of the S&P 500 are reported in Table 3 and Figure 6. Once again, returns on managed futures had the lowest correlation with those on the reference index; this was the only series that was negatively correlated with returns on equities (using either a one-tailed or a two-tailed test)8. Gold hedge funds and global macro hedge funds took second and third place respectively, followed by gold bullion.

Figure 5: Correlation with S&P 500 TR, May '94 to April '04

-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00

MSCI World USD Net Return

DJIA Total Return

Nasdaq Composite (Close)

MSCI EAFE USD

CSFB/Tremont Hedge FundIndex

HEDG Global Macro

Gold Hedge Funds

Gold, London PM fix


Figure 6: Correlation with S&P 500 TR, May '94 to April '0430 worst months

-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00


DJIA Total Return


MSCI EAFE USD


Gold, London PM fix

HEDG Global Macro

Gold Hedge Funds


Figure 7: Correlation with S&P 500 TR, May '94 to April '0430 best months

-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00

DJIA Total Return


MSCI EAFE USD


Gold Hedge Funds


Gold, London PM fix

HEDG Global Macro



None of these correlation coefficients was significantly different from zero. However, this lack of correlation also applied to the generic CSFB/Tremont hedge fund index and the MSCI EAFE. It is worth reiterating that the small sample size requires that these results be treated with caution, particularly where they are marginal, i.e. where test statistics were close to critical values.

Examining the correlation of returns for the 30 best months provides some indication as to the trade-off that might be required between protecting a portfolio against downside risk without unduly compromising overall returns when stock markets are performing well. Figure 7 and Table 4 show that returns on the CSFB/Tremont Hedge Fund index had the lowest correlation with respect to the S&P 500, followed by global macro hedge funds, gold bullion and then gold hedge funds. However, although all of these correlation coefficients had negative signs, none were found to be significantly different from zero at the 5% level of significance.

May 94-Apr 04

CSF

B/T

rem

ont

Hed

ge F

und

Inde

x

HED

G G

loba

l Mac

ro

HED

G M

anag

ed

Futu

res

Gol

d, L

ondo

n PM

fix

DJI

A T

otal

Ret

urn

MSC

I EA

FE U

SD

MSC

I Wor

ld U

SD N

et

Ret

urn

Nas

daq

Com

posi

te

(Clo

se)

S&P

500

TR

Gol

d H

edge

Fun

ds

CSFB/Tremont Hedge Fund Index 1.000 0.849 0.118 0.099 0.389 0.356 0.478 0.536 0.473 0.161HEDG Global Macro 0.849 1.000 0.262 -0.013 0.180 0.092 0.175 0.172 0.210 0.166HEDG Managed Futures 0.118 0.262 1.000 0.149 -0.213 -0.073 -0.170 -0.235 -0.209 0.488Gold, London PM fix 0.099 -0.013 0.149 1.000 -0.077 0.050 0.021 0.009 -0.067 0.428DJIA Total Return 0.389 0.180 -0.213 -0.077 1.000 0.626 0.893 0.650 0.927 -0.035MSCI EAFE USD 0.356 0.092 -0.073 0.050 0.626 1.000 0.749 0.544 0.612 0.079MSCI World USD Net Return 0.478 0.175 -0.170 0.021 0.893 0.749 1.000 0.793 0.945 0.070Nasdaq Composite (Close) 0.536 0.172 -0.235 0.009 0.650 0.544 0.793 1.000 0.806 0.059S&P 500 TR 0.473 0.210 -0.209 -0.067 0.927 0.612 0.945 0.806 1.000 0.013Gold Hedge Funds 0.161 0.166 0.488 0.428 -0.035 0.079 0.070 0.059 0.013 1.000

Table 2

30 worst months

CSF

B/T

rem

ont H

edge

Fu

nd In

dex

HED

G G

loba

l Mac

ro

HED

G M

anag

ed

Futu

res

Gol

d, L

ondo

n PM

fix

DJI

A T

otal

Ret

urn

MSC

I EA

FE U

SD

MSC

I Wor

ld U

SD N

et

Ret

urn

Nas

daq

Com

posi

te

(Clo

se)

S&P

500

TR

Gol

d H

edge

Fun

ds

CSFB/Tremont Hedge Fund Index 1.000 0.711 -0.139 0.304 0.280 0.215 0.293 0.348 0.322 -0.107HEDG Global Macro 0.711 1.000 0.288 0.055 -0.024 -0.058 -0.147 -0.186 -0.112 -0.031HEDG Managed Futures -0.139 0.288 1.000 0.125 -0.453 -0.143 -0.388 -0.354 -0.496 0.475Gold, London PM fix 0.304 0.055 0.125 1.000 -0.001 0.032 0.193 0.109 0.100 0.432DJIA Total Return 0.280 -0.024 -0.453 -0.001 1.000 0.504 0.820 0.369 0.833 -0.347MSCI EAFE USD 0.215 -0.058 -0.143 0.032 0.504 1.000 0.561 0.301 0.383 -0.013MSCI World USD Net Return 0.293 -0.147 -0.388 0.193 0.820 0.561 1.000 0.621 0.915 -0.084Nasdaq Composite (Close) 0.348 -0.186 -0.354 0.109 0.369 0.301 0.621 1.000 0.687 -0.050S&P 500 TR 0.322 -0.112 -0.496 0.100 0.833 0.383 0.915 0.687 1.000 -0.256Gold Hedge Funds -0.107 -0.031 0.475 0.432 -0.347 -0.013 -0.084 -0.050 -0.256 1.000

Table 3


30 best months

CSF

B/T

rem

ont H

edge

Fu

nd In

dex

HED

G G

loba

l Mac

ro

HED

G M

anag

ed

Futu

res

Gol

d, L

ondo

n PM

fix

DJI

A T

otal

Ret

urn

MSC

I EA

FE U

SD

MSC

I Wor

ld U

SD N

et

Ret

urn

Nas

daq

Com

posi

te

(Clo

se)

S&P

500

TR

Gol

d H

edge

Fun

ds

CSFB/Tremont Hedge Fund Index 1.000 0.938 0.324 -0.144 -0.295 -0.114 -0.193 0.235 -0.306 0.128HEDG Global Macro 0.938 1.000 0.219 -0.218 -0.245 -0.274 -0.330 0.050 -0.281 0.066HEDG Managed Futures 0.324 0.219 1.000 0.187 -0.261 -0.055 -0.117 -0.001 -0.109 0.653Gold, London PM fix -0.144 -0.218 0.187 1.000 -0.150 0.222 0.132 0.257 -0.251 0.280DJIA Total Return -0.295 -0.245 -0.261 -0.150 1.000 0.264 0.463 0.049 0.646 -0.251MSCI EAFE USD -0.114 -0.274 -0.055 0.222 0.264 1.000 0.901 0.194 0.224 -0.255MSCI World USD Net Return -0.193 -0.330 -0.117 0.132 0.463 0.901 1.000 0.295 0.505 -0.258Nasdaq Composite (Close) 0.235 0.050 -0.001 0.257 0.049 0.194 0.295 1.000 0.208 0.226S&P 500 TR -0.306 -0.281 -0.109 -0.251 0.646 0.224 0.505 0.208 1.000 -0.072Gold Hedge Funds 0.128 0.066 0.653 0.280 -0.251 -0.255 -0.258 0.226 -0.072 1.000

Table 4 Table 5 reports the test-statistics for all three sets of correlation coefficients with respect to returns on the S&P 500, providing some indication as to whether the diversification benefit gained during the worst months was greater than that “lost” during the best. Ideally, one would want the test statistic for the worst months to be equal to

or less than that for the entire reference period, the same applies, somewhat more strongly, for the best months.

Test statistics May 94 - April 04 30 worst months 30 best months

Critical values (α = 0.05, two-tails) |1.980| |2.042| |2.042| CSFB/Tremont Hedge Fund Index 5.838 1.801 -1.703 HEDG Global Macro 2.334 -0.599 -1.550 HEDG Managed Futures -2.326 -3.023 -0.582 Gold, London PM fix -0.732 0.531 -1.373 DJIA Total Return 26.883 7.956 4.475 MSCI EAFE USD 8.407 2.196 1.219 MSCI World USD Net Return 31.438 12.028 3.092 Nasdaq Composite (Close) 14.805 4.997 1.127 Gold Hedge Funds 0.140 -1.402 -0.383

Note: test-statistics reported in red are not significantly different from zero. Table 5

Series by series, the results reported in Table 5 indicate that out of all the series, only the returns HEDG Managed Futures and Gold Hedge Funds were less correlated with returns on the S&P 500 during the 30 worst months than they were during the 30 best months. However, taking the statistical significance of the results into account, gold bullion, global macro funds and the generic hedge fund index also provided evidence of effective diversification in both environments. Discussion The results of this analysis indicate that the exposure to the generic hedge fund universe on an asset-weighted basis would have provided investors with the highest risk-adjusted returns over the

ten year period ending in April 2004, with macro hedge funds running at a close second and gold hedge funds ranking third. For the four years ending in April 2004, which cover the recent bear market in equities, these three categories still provided the best risk-adjusted returns, but macro hedge funds outperformed the generic index.


Investors who prioritised protection against downside risk over higher returns would have benefited most from exposure to managed futures, gold hedge funds, global macro funds and gold bullion, ranked in order of the return correlations with the 30 worst months of the S&P 500. During periods when stock markets have under-performed, and assuming that the investor had access to hedge funds, it would appear that he/she would have been better off implementing a style-based strategy rather than simply seeking balanced exposure to the entire hedge fund universe. However, implementation raises a number of issues. Notably, we identify four constraints that exist with respect to hedge funds that do not apply to gold. These concern transparency; size and liquidity constraints; minimum investment requirements; and moral hazard. A final constraint – which concerns the application of portfolio modelling techniques - applies to a greater or lesser degree to range of alternative investments. Recent estimates indicate that by May 2004, more than 7000 hedge funds were operating;x the vast majority of these are not included in the CSFB/Tremont Index because they do not meet the criteria – they may be too small or they may not meet the Index reporting requirements.xi A lack of transparency is a very real problem for analysts and asset allocators trying to evaluate hedge funds; however given that successful implementation of hedge fund strategy requires keeping one’s cards close to one’s chest and running ahead of the pack, it seems unlikely that this particular problem will be resolved. With respect to size, it would seem that (relatively) small really is beautiful when it comes to a hedge fund’s assets under management: in order to exploit market inefficiencies, managers need to be able to execute trades without impacting on price. The downside of small is that it may prove difficult for many investors, especially large investors, to access hedge funds directly. This is perplexing since the minimum required investment for a fund of hedge funds – which is, arguably, the “retail” end of the market - is said to be around £50,000. Assuming that 5% of a portfolio is to be allocated to hedge funds as an asset class, an assumption based on views generally reported in the trade press, this rules out portfolios under £1 million. The skewness of the size distribution of

UK final salary schemes illustrated in Figure 8 is probably understates the case as the underlying data did not include funds with a capital value below £10 million; it nonetheless serves to make the point that many potential institutional investors may find minimum investment requirements a serious constraint. Moral hazard is a problem at two levels. When selecting individual hedge funds, how can the investor be sure that “it does what it says on the tin”? Is one paying active management fees for passive management performance? There is no doubt that the hedge fund community counts some of the most talented investment managers in the industry. However, historically it has been a close network of relationships that have typically provided the source of hedge fund capital, and it is hard to see how it will be possible to package the asset class for the mass market without at the same time increasing the likelihood of a market for “lemons”. An obvious solution to this problem has been for investors to access hedge funds via funds of hedge funds, which resolves certain access problems but still relies heavily on the skill of the fund of funds manager. The results of this analysis have indicated that there little to be gained by simply implementing an asset-weighted strategy based on the market share of each style-based sector, given that the generic index (constructed on that basis) was found on the whole to be positively correlated with returns on equities (as proxied by the S&P 500) – a correlation which actually increased during equity market under-performance and decreased

Figure 8: Size distribution of reporting UK DB Pension Funds

833

14276

45 29 17 9 5 3 30

100

200

300

400

500

600

700

800

900

1 2 3 4 5 6 7 8 9 10

Deciles

No.

of f

unds

in d

ecile

Source data: AP/IPE, July 2003


during the top quartile of performance. Whichever way one looks at the issue, a degree of trust is a prerequisite, and this may sit less comfortably with institutional investors and their compliance departments than it does with ultra high net worth individuals. When it comes to portfolio modelling and strategic asset allocation, is that, although the first hedge funds were set up in the late 1940s, most hedge fund indices do not start before 1990. Data for bonds, equities and precious metals is available going back at least 100 years, facilitating the task of forming a judgement with respect to long run average returns and volatility through a variety of market conditions.

A challenge that confronts asset allocators analysing many alternative investments, including hedge funds, private equity and gold, is that the most widely used portfolio modelling techniques require some estimate of a discounted cash flow. Absolute return strategies are not designed to fit conveniently into this approach and require a higher degree of confidence in the manager’s skill. Whilst it is true that other modelling methods exist, and that many managers may place greater weight on a less technical quantitative evaluation of the medium term investment environment, the reality is that institutional investors are more likely to place greater emphasis on technical quantitative analysis and changing the way that analysis is done – if this is appropriate – may take a little time. Conclusions The results of our analysis indicate that gold hedge funds, managed futures and gold bullion offered superior diversification over the period covered. However, taking entry barriers and liquidity constraints into account, it may have proved difficult or even impossible for many investors to access these types of funds other than via funds of hedge funds. Exposure to the returns on gold bullion, whether via direct exposure to the underlying metal or via the universe of long-only funds, could have provided such investors with a viable and attractive alternative.

Acknowledgements: Many thanks to Baker Steel Capital, GAIM and Global Advisors for the range of background information and data they provided for this research. i Please read the disclaimer on the final page of this report ii Data source: EcoWin/Global Insight iii See www.hedgeindex.com iv This data was kindly provided by Global Advisors, based on Bloomberg data. v The universe of gold-oriented funds (whether “hedge” or not) consists of 39 funds worldwide. vi This data is published by the London Bullion Market Association (see www.lbma.org.uk) and is widely available from a number of sources. For more information about the London gold fixing, see XXX. vii See A Review of the Strategic Case for Gold in UK Pension Funds, Mercer Investment Consulting, November 2003. Available on request from the World Gold Council. viii Sharpe ratio here is not calculated with reference to excess returns but is simply the ratio of the annualised average monthly returns to annualised volatility over the reference period. ix Using the same test statistic as Smith (2001), the critical values for N=120 and N=30 are |0.219| and |0.425| respectively, with α= 0.05 and a null hypothesis that the test statistic is equal to zero, i.e. a two-tailed test. For a one-tailed test, i.e. testing the hypothesis that the test statistic is negative, also using a significance level of 5%, the critical values are (-0.197) for 120 observations and (-0.385) for 30 observations. x Merrill Lynch Investment Managers/Pensions Week supplement, 3 May 2004. xi These are described in full on the CSFB/Tremont website, www.hedgeindex.com


Disclaimer

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