Abstract and Keywords
The hedge fund replication refers to the process of replicating, or cloning, the returns of hedge funds through a statistical model or algorithmic trading strategy. There are three approaches that are employed for hedge fund replication. The first method, factor replication, is a top-down approach, which tries to estimate asset exposures of hedge funds with various statistical methods. The second is a bottom-up approach, which is referred here to as rule-based replication. The article aims to isolate broad and fundamental characteristics of hedge fund strategies and implement these with automated trading algorithms. The third approach aims to replicate desirable distributional properties of hedge fund returns with dynamic trading techniques. The underlying assumption of factor replication method is that major parts of hedge fund returns can be captured by a set of common risk factors. The dynamic trading approach aims to match the distributional properties such as mean, volatility, and correlation of hedge fund return time-series relative to a portfolio of common assets. The return time-series of hedge funds are not expected to be replicated on a monthly basis. The implementation of dynamic trading involves three steps. Firstly, the investor's portfolio is used to functionally define desirable dependence structures relative to hedge fund returns. The second step is to derive the payoff function, where the reserve asset is the dependent variable, which will give the dependence structure relative to the investor's portfolio. The final and third step is to derive the dynamic trading strategy, which replicates the payoff function.
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