Getting Real
IN PRINT ARCHIVE CIR Winter 2007
| Winner of the AiMA Canada 2007 Research Award Getting
real By Neil Simons, vice-president, northwater Capital management inc., and Adrian Hussey is vice-president, northwater Capital management inc. Within the past year, replication techniques have captured a great deal of media attention. In particular, the linear factor replication approach considered by Hasanhodzic and Lo and distributional replication pioneered by Kat and Palaro are frequently highlighted. At the same time, several investment banks as well as smaller investment management firms have launched products based on replication processes. All the attention is certainly not surprising given the fact that hedge fund replication has been proposed as a method for accessing hedge fund performance without the associated illiquidity, fees, necessity of performing extensive due-diligence, and exposure to single-manager risk. But can hedge fund replication really deliver? To find out, we focused our research on understanding the underlying fundamentals associated with the various replication processes. Contrary to what many say, we have determined that the underlying approaches are limited in their ability to access the performance of hedge funds. In the paper that follows, we look at both linear factor replication and distributional replication to determine the best approach to portfolio optimization. Linear
Factor Replication
Individual hedge funds possess a larger amount of idiosyncratic
risk than hedge fund indices. The idiosyncratic component of hedge
fund returns tends to diversify away as many funds are combined
into an index. Therefore, the out-of-sample linear factor replication
of individual funds is less successful than the replication of hedge
fund indices. We consider the replication of hedge find indices
as opposed to individual hedge funds. The linear factor-based replication
products that have been released thus far target well-diversified,
broad-based hedge fund indices such as the HFRI Composite. In the
examples that follow, we consider the linear factor replication
of the CS Tremont, CS Long Short Equity, CS Market Neutral Equity,
and HFRI Composite index using: S&P500, Russell 2000, USD Index,
MSCI EAFE, MSCI Emerging Markets, and 30-Day US T-Bills. The R2
from performing a 24-month rolling regression is provided in Figure
1. The R2 is a measure of the success of the linear regression, through which one determines how well the known variables (in this case the market factors such as the S&P500, Russell 2000, USD Index, MSCI EAFE, MSCI Emerging Markets, and 30-Day US T Bills) are able to explain the variable under investigation (in this case a relevant hedge fund index such as the CS Tremont, CS Long Short Equity, CS Market Neutral Equity, and HFRI Composite). The R2 ranges between 0 and 1. A value close to zero implies almost no explanatory power (i.e., the market factors are unable to explain any variation in the hedge fund return series). A value close to 1 implies that the market factors are able to explain most of the variation in the hedge fund return series. For a market-neutral hedge fund, one would expect to find that simple market factors are unable to explain the variation in hedge fund returns, and therefore a relatively low value for R2. We find that the best fit occurs for the HFRI Composite (rarely lower than 0.9); and the worst fit for the CS Market Neutral Equity index (rarely greater than 0.6). We have investigated other hedge fund indices in addition to the four indices analyzed here. There are a few strategy indices that possess R2 values similar to that of the equity market-neutral index. The remainder possesses R2 values that are distributed between the market-neutral and composite indices. The presence of a large rolling R2 indicates the returns can be modelled accurately looking backward. We have examined the out-of-sample replication of the four indices considered in Figure 1. For each index, a regression is performed using the previous 24 months of index returns as the dependent variable, with the six market factors listed in the previous section as the independent variables. The factor loadings determined from the linear regression are used as position weights. A portfolio of market factors built using the position weights is held out-of-sample over the following month. Replication is simulated by rolling the process forward in time. Using a recent 3.5-year time horizon to perform the replication (July 2003 to December 2006), we have compared the Sharpe ratios for the original four indices and their linear replicas. The comparison indicates that linear replication is capable of providing Sharpe ratios similar to the original indices for all cases, with the exception of the CS Equity Market Neutral index. This index has a low rolling R2 value and, therefore, the specific factors applied in the model are unable to accurately explain the behaviour of the index. We have also determined that out-of-sample linear factor replication is less successful over longer time horizons. The improved replication success for the broadbased indices over more recent time horizons is partially attributed to the increased rolling R2 for the long/short equity strategy indices (see Figure 1). Long/short equity strategies comprise a larger weight within the composite indices (such as the CS Tremont and the HFRI Composite) in recent years.
It is possible to use more complex factors in order to improve the in-sample R2, and therefore potentially the out-of-sample correlation. For example, Fung and Hsieh, and Foerster have investigated factors that are useful in the explanation of CTA/Managed-Futures and Equity Market-Neutral strategies, respectively. While the refinement of the factor basket will help to improve replication success, the behaviour exhibited within Figure 2 remains. A continuum of strategies exists where some are more market-neutral than others. We conclude that successful linear factor replication requires the underlying funds or indices to possess significant exposures to market factors. The replication process provides access to the beta component of hedge fund returns. Due to its inherent idiosyncratic nature, the alpha component of hedge fund returns, which investors generally seek, is not accessible through linear factor replication. Distributional
Replication Findings Distributional replication can be separated into two subcomponents—distributional adjustments and correlation targeting. Distributional adjustments transform the distribution of a market factor or portfolio of market factors (referred to as the reserve portfolio), into a more desirable distribution. The process can scale volatility, implement a performance floor, or increase the skewness of an underlying market factor. Implementation of a distributional adjustment is therefore similar to dynamic option replication using delta hedging. The idea of correlation targeting is an appealing one. It creates a replica with a desired correlation with respect to one specific target portfolio, and one desirable attribute of hedge funds is a low correlation to traditional market factors. One expects that a replica with a low correlation to an investor’s portfolio will improve portfolio performance when combined with the original investor’s portfolio. However, correlation targeting represents a synthetic approach to achieving the correlation target. Lower desired levels of correlation are achieved through a larger short position in the investor’s portfolio within the replication process. This synthetic approach to achieving correlation becomes redundant when considered within a mean-variance portfolio optimization. Volatility and correlation targeting can be achieved using a monthly trading process derived through mean-variance portfolio optimization with additional constraints. We have compared the results obtained from our monthly volatility and correlation targeting process to those achieved from the process applied by Kat and Palaro. The comparison utilizes the same underlying hedge fund indices (10 Edhec Hedge Fund indices), the same time period (March 1999 to September 2006), and the same reserve and target portfolios as those considered by Kat and Palaro. The monthly process is capable of providing correlation, volatility, and mean excess returns very similar to those produced by the Kat and Palaro process. For each index, the correlation and volatility of the replicas are similar to the targeted hedge fund indices, indicating the success of the replication processes. Volatility and correlation targeting can be achieved using a monthly trading process derived through mean-variance portfolio optimization with additional constraints.
To a large extent, the excess return earned from distributional replication is obtained from the contents of the reserve portfolio. The Sharpe ratio of the reserve portfolio serves as an estimate for the Sharpe ratio of the replicas. Since the contents of the reserve portfolio consists of a long position in traditional market factors (to satisfy the constraint of using liquid instruments), expectations for the long-term performance of distributional replication will be equivalent to that of a portfolio of traditional market factors. Volatility and correlation targeting is robust with respect to changes in the reserve portfolio. However, mean returns are not robust to changes in the reserve portfolio. Optimal
Portfolio Construction In addition, we have created optimal portfolios combining the monthly replicas with the original target portfolio. For each of the 10 Edhec replicas, without exception, the optimal portfolios lie along the Tobin frontier. Therefore, for each of the 10 replicas, the optimal combination of replica and original target is equivalent to the optimal portfolio created by combining the reserve asset and the target asset. In each of the 10 cases, it was unnecessary to follow the monthly volatility and correlation targeting process, and then subsequently use the replica to build an optimal portfolio. The end result is the same as that which could have been achieved through the creation of an optimal combination of the reserve and target portfolios. The process of replication essentially adds new assets (in the form of the reserve portfolio) into an investor’s existing portfolio. In the examples provided, this entails adding commodities, small capitalization stocks, and bonds with different maturities through the distributional replication process. The addition of these new asset classes represents a change to the original 50/50 S&P500 and Long Bond policy mix. Verdict
on Distributional Replication Next, correlation targeting represents a partial match of the correlation structure of the original hedge fund within the capital markets. A replica can be constructed to achieve a desired correlation to a specific target portfolio; however, a factor analysis would indicate a potentially large R2 with correlations to market factors within the reserve/target portfolios. These factors and relative value trades may represent entirely new exposures for an investor, and are not likely to be present within the original targeted hedge fund. Finally, we have investigated the construction of optimal portfolios using the distributional replicas and an investor’s original portfolio. For each distributional replica considered, the result is equivalent to a portfolio created using traditional mean-variance portfolio construction, without using replication. This demonstrates a redundancy associated with correlation targeting. Conclusions While hedge fund replication may be limited in its potential, such analysis leads to an improved understanding of hedge fund returns (although not necessarily useful as a replication exercise). Some of the more complex factors contained in hedge fund returns are sometimes referred to as Alternative Betas. These factors may represent trading strategies that have been profitable in the past (but perhaps won’t be profitable in the future), and may also represent alternative risk premia that are expected to be profitable over longer time horizons. This has two implications for building efficient portfolios. First, these alternative risk premia are useful for inclusion within a portfolio although, not necessarily through a replication process. Second, as these risk premia and/or trading strategies become widely recognized, they are no longer classified as alpha. The formerly alpha-rich strategies begin to resemble complex betas, implying the search for alpha is a continuous process. References |
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The
out-of-sample correlation of the replica and the underlying index
provides an indication of replication success. If the replication
process has successfully reproduced the time series behaviour of
the original index, a correlation close to 1 is expected. The relationship
between the out-of-sample correlation achieved by the replication
process and the average R2 observed over the replication time period
is provided in Figure 2. We have expanded the set of indices considered
and each point within the figure corresponds to a particular index.
We find a positive dependence between the achieved out-of-sample
correlation and the average R2 with a y-intercept less than 0. The
positive sloped relationship is intuitively obvious: replication
success depends on the extent of the factor contribution to the
strategy returns. However, the negative y intercept implies that
a threshold exists for the average R2 before the replica resembles
the original index, and therefore acts as a guideline for selecting
indices or funds suitable for linear replication.
With
respect to risk-adjusted return, our analysis indicates that the
Sharpe ratios of the replicas are sometimes greater than the original
Edhec indices and therefore one might conclude that the performance
is due to the replication process. However, we demonstrate that
the performance is due to the market factors selected for use within
the replication process. The results provided in Figure 3 demonstrate
the impact of using a different mix of market factors within the
reserve portfolio. The replication of 10 Edhec Indices is performed
using three different reserve portfolios (see x axis). For each
reserve portfolio, the correlation and volatility targets are successfully
met, but mean returns are significantly different. As demonstrated
by the minimum, maximum and average replica Sharpe ratio, the replicas
have performance similar to that of the reserve portfolio in each
case. Reserve Portfolio 1 consists of the same set of market factors
as Kat and Palaro (S&P500, GSCI, Russell 2000, Long US Treasury
Bonds, Medium Term US Treasury Bonds, and Eurodollar Futures). Reserve
Portfolio 2 contains a modification to the original reserve portfolio.
The S&P500 Information Technology and Telecommunication Services
sectors (with poor performance over the time period considered)
are included within the reserve portfolio, and the Russell 2000
and GSCI (good performance over the time period) are excluded. The
replica Sharpe ratios achieved from using Reserve Portfolio 2 are
poor in comparison to those achieved from using Reserve Portfolio
1. Reserve Portfolio 3 has been modified in an attempt to achieve
superior performance to the original reserve portfolio. In this
case, S&P500 Aerospace & Defense, and the S&P500 Homebuilding
sub-industry groups; and short positions in the S&P500 Information
Technology and S&P500 Telecommunication Services sectors have
been added.