Many asset managers have recently produced products that are described as hedge fund replication product. Now there are a few methodologies that have been used in replication. Factor model replication, return distribution replication and position tracking. The most successful or at least the most widely utilized are factor models. Factor models are attractive as they are relatively easy to utilize and understand, and also unlike return distribution replication (Harry Kat) the successfulness of the product can be evaluated monthly.
As a very quick primer on factor model replication:-
These products work by evaluating the beta exposures to a number of factors. Running, normally an ordinary least squares regression between the index that is to be replicated and an array of alternative betas leads to the weightings of each market factor.
Those factor weighting are usually reevaluated each month and new hedge fund data becomes available. That’s basically it, however most companies have some slight variation.
Index selection / creation
One of the questions to be asked is “what should be replicated”? A standard hedge fund index, a fund of fund index or a customized index. Some companies create their own index, the argument being that this allows them to remove funds that they do not want exposure to, or to overweight exposure to fund that are “easier” to replicate.
Potential factor exposures
Generally a basket of factors is monitored. Now, one of the advantages of replication is the transparency and the increased liquidity, (as well as the lower fees). This means that even if it is clear that Ugandan equity is a significant factor, it is not going to be possible to provide a product with such an exposure. This is important, as many hedge fund replication products do not include exposure to factors such as credit, volatility, convertibles or some emerging market.
Weightings
The classic methodology is an ordinary least squares regression on rolling window. Normally something between 24 and 36 months. Some replication products utilize techniques such a kalman filter to be able to adapt quickly to any structural change in market structure. The argument being that a rolling window technique will take time to fully adjust to any significant change in weights.
These products have had a degree of success. However, there are a number of problems. Firstly, what are the advantages of these products? Well the two main headline attributes are liquidity and transparency. Both of these are attractive attributs to some degree, however one has to ask what level of liquidity and transparency investors really require. Investing in a hedge fund replication product has generally been considered in a core satellite approach. The replication product providing the strategic core whilst the investor can allocate tactically to specific hedge funds that they believe will outperform or provide alpha. Now, if an investor has chosen to invest like this, the additional liquidity is of little advantage (unless of cause the investor changes strategic allocations intra-month). The liquidity offered by most fund of hedge funds would be adequate to provide investor with the liquidity needed in a core satellite approach. Transparency is indeed important; however there are some important questions.
1) What do you do with this transparency? Full transparency of a Fund of hedge fund portfolio down to hedge funds underlying security positions is quite daunting. Most investors would use a factor model to break the returns down in order to find out where the risk lies. This can be done without knowledge of the underlying positions.
2) What makes an investor think that they are in any way better informed at evaluating the risks in the underlying positions? This is done by the hedge fund who will (such have) intimate knowledge of the position and the fund of hedge fund.
The disadvantage of these product revolve around the fact that because of there aims of providing liquidity, they are unable to provide exposure to (or replicate) all hedge fund strategies. None of the academic papers have been able to show a significant out performance over the index that they are trying to replicate. Most of these products are trying to be correlated to the index after fees, so by the time the replication products fees are included; the returns are not particularly attractive.
One of the way that I want to look utilizing these know market risk premiums is not to replicate hedge funds, uses these risk premiums to create an efficient portfolio.
Friday 12 March 2010
Factor models
One of the motivations behind this blog is the work conducted by William Fung and David A. Hsieh in their paper Asset-based Hedge-fund Styles and Portfolio Diversification. In the paper the authors demonstrate that asset-based style factors link returns of hedge-fund strategies to observed market prices. From their earlier work it was shown that the returns from trend-following strategies can be replicated by a dynamically managed option-based strategy known as a lookback option. The lookback option model can be used to potential compute managers’ alpha if it exists as well as making the underlying mechanism of this class of strategies transparent to investors. The return of this option-based replication strategy has been shown to have a good degree of explanatory power for funds that utilize a trend-following style. This fact shows that the return distribution of trend-following funds is a systematic consequence of a broad class of trend-following strategies.
The key question in factor model analysis in the hedge fund context is: “Can we discern the return characteristics of hedge-fund strategies by looking at how hedge-fund returns are statistically clustered together?” The results from the work on lookback options show that there are systematic reasons why hedge fund strategies offer the returns distribution characteristics they do.
Now just jumping ahead a few pages, we look at a simple multi-factor model. Just for reference the classic Sharpe formula:-
The key question in factor model analysis in the hedge fund context is: “Can we discern the return characteristics of hedge-fund strategies by looking at how hedge-fund returns are statistically clustered together?” The results from the work on lookback options show that there are systematic reasons why hedge fund strategies offer the returns distribution characteristics they do.
Now just jumping ahead a few pages, we look at a simple multi-factor model. Just for reference the classic Sharpe formula:-
Is simply expanded:-
Now, taking some well documented “classic” asset based style factor we can build a model. The values of the Betas are derived from a simple ordinary least squares regression against a fund of hedge fund index, in this case the HFRI Fund of Funds Composite Index. A separate discussion can be had on survivorship bias and why a fund of fund index mitigates this to some extent.
Market risk, taking the excess returns over says the S&P 100 or S&P 500 Index
Small and large cap spread, using the spread between the Russell 2000 and the S&P 500 Indices
Yield Spread, Change in 10-year US Treasury yields
Credit Spread, Change in the spread between 10-year Treasury Bonds and Moody’s BBa bonds
Trend following, taking the excess returns of the Barclays CTA index*
*In the paper Hedge Fund Benchmarks: A Risk-Based Approach. Financial Analysts Journal Volume 60 Number 5 ©2004, CFA Institute. The authors use three separate factors to model trend following behavior. A basket of look back options on bonds, commodities and finally FX.
No here comes the incredible fact, this simple model captures around 70% of the risk in a diversified portfolio of hedge funds. Yes just five factors, 70% of the risk! If we use the 3 trend following factors instead of the CTA proxy the risk captured increases to 80%!
All of the factors are significant at a 99% confidence level.
Large cap: 0.15 – 0.19
Small – Large cap: 0.05 – 0.09
Credit spread: -3.0 – -4.0
Yield spread: -0.9 – -1.1
Trend following: 0.16 – 0.20
Now, interesting the alpha of this model is around 0.15% per month or approx 1.8% per annum, but is not statistically significant. The T-stat is around 1.7. Depending on the time frame selected, this value does fluctuate into being significant at times. However, the point of this is not to argue if hedge funds produce alpha or not, but to demonstrate the fact that a lot of hedge fund returns are from exposure to “simple” risk premiums.
The model was run in sample; normally this causes statisticians a degree of discomfort. However, this is not a problem here as I simply wish to show that these factors are utilized. The predictive power of such models is clearly of concern when trying to use these types of model in a financial product, such as a hedge fund replication product.
Whist this very simple model captures c70% of the risk there is still 30% out there, and potentially 2% of unexplained returns. It is important to note that we have not taken into account the hedge fund fees.
Large cap: 0.15 – 0.19
Small – Large cap: 0.05 – 0.09
Credit spread: -3.0 – -4.0
Yield spread: -0.9 – -1.1
Trend following: 0.16 – 0.20
Now, interesting the alpha of this model is around 0.15% per month or approx 1.8% per annum, but is not statistically significant. The T-stat is around 1.7. Depending on the time frame selected, this value does fluctuate into being significant at times. However, the point of this is not to argue if hedge funds produce alpha or not, but to demonstrate the fact that a lot of hedge fund returns are from exposure to “simple” risk premiums.
The model was run in sample; normally this causes statisticians a degree of discomfort. However, this is not a problem here as I simply wish to show that these factors are utilized. The predictive power of such models is clearly of concern when trying to use these types of model in a financial product, such as a hedge fund replication product.
Whist this very simple model captures c70% of the risk there is still 30% out there, and potentially 2% of unexplained returns. It is important to note that we have not taken into account the hedge fund fees.
Tuesday 2 February 2010
Objective
I am always amazed at how investors look at and analyze hedge funds. Alot of investors have the misguided notion that hedge funds returns are "Alpha" in the truest sense of the word, (that little alpha in the CAPM model). As a result investors are sometimes unable to fully understand the risks that funds are taking and so unable to accurately compare funds versus each other and versus their strategic objectives.
Hedge fund managers can take advantage of many markets and strategies. However, investors often only compare returns to that of a broad market index, or other such buy and hold strategy, an industry specific index for example. This is incorrect, as this analysis does not fully capture all of the risks premiums involved. Hedge fund returns can be view as the return generated from exposure to a number of traditional betas, a number of alternative beta and a number of alphas over each of these betas / risk premiums.
Traditional Beta, this would include the usual risk premium that we are all used to. Broad market returns, bond returns, commodity returns. For all these there are a plethora of market indices that capture the relevant risk premium.
Alternative betas are generally the types of risk exposures that are not available to the normal buy and hold investor. These Alternative betas are generated from exposures to risk such as merger spreads, momentum, illiquidity, carry trades, pairs trades, volatility trades..... As well as market timing to all these betas.
Alpha (skill) can be generated over each of these betas. Without fully understanding the all of the beta exposure it is basically impossible to measure the alpha generated. Without understanding all of the various risk exposures, investors cannot full understand all of the risks that they are exposured to.
In this blog we will attempt to understand and develop strategies that capture the full extent of all the risk premium that hedge funds can exploit. Ultimately, we will try and create an efficient portfolio from these strategies. We shall also be looking at other hedge fund replication strategies on the way.
Hedge fund managers can take advantage of many markets and strategies. However, investors often only compare returns to that of a broad market index, or other such buy and hold strategy, an industry specific index for example. This is incorrect, as this analysis does not fully capture all of the risks premiums involved. Hedge fund returns can be view as the return generated from exposure to a number of traditional betas, a number of alternative beta and a number of alphas over each of these betas / risk premiums.
Traditional Beta, this would include the usual risk premium that we are all used to. Broad market returns, bond returns, commodity returns. For all these there are a plethora of market indices that capture the relevant risk premium.
Alternative betas are generally the types of risk exposures that are not available to the normal buy and hold investor. These Alternative betas are generated from exposures to risk such as merger spreads, momentum, illiquidity, carry trades, pairs trades, volatility trades..... As well as market timing to all these betas.
Alpha (skill) can be generated over each of these betas. Without fully understanding the all of the beta exposure it is basically impossible to measure the alpha generated. Without understanding all of the various risk exposures, investors cannot full understand all of the risks that they are exposured to.
In this blog we will attempt to understand and develop strategies that capture the full extent of all the risk premium that hedge funds can exploit. Ultimately, we will try and create an efficient portfolio from these strategies. We shall also be looking at other hedge fund replication strategies on the way.
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