"A New Hedge Fund Replication Method with the Dynamic Optimal Portfolio"

This paper provides a new hedge fund replication method, which extends Kat and Palaro (2005) and Papageorgiou, Remillard and Hocquard (2008) to multiple trading assets with both long and short positions. The method generates a target payoff distribution by the cheapest dynamic portfolio. It is regarded as an extension of Dybvig (1988) to continuous-time framework and dynamic portfolio optimization where the dynamic trading strategy is derived analytically by applying Malliavin calculus. It is shown that the cost minimization is equivalent to maximization of a certain class of von Neumann-Morgenstern utility functions. The method is applied to the replication of a CTA/Managed Futures Index in practice.

Generating a Target Payoff Distribution with the Cheapest Dynamic Portfolio: an Application to Hedge Fund Replication

This paper provides a new method to construct a dynamic optimal portfolio for asset management in a complete market. The method generates a target payoff distribution by the cheapest dynamic trading strategy. It is regarded as an extension of Dybvig (1988a) to continuous-time framework and dynamic portfolio optimization where the dynamic trading strategy is derived analytically by applying Malliavin calculus. As a practical example, the method is applied to hedge fund replication, which extends Kat and Palaro (2005) and Papageorgiou, Remillard and Hocquard (2008) to multiple trading assets with both long and short positions.

"Hedge Fund Replication"

This chapter provides a comprehensive explanation of hedge fund replication. This chapter first reviews the characteristics of hedge fund returns. Then, the emergence of hedge fund replication products is discussed. Hedge fund replication methods are classified into three categories: Rule-based, Factor-based, and Distribution replicating approaches. These approaches attempt to capture different aspects of hedge fund returns. This chapter explains the three methods.

Hedge Fund Replication ?Revised in November 2008, forthcoming in The Recent Trend of Hedge Fund Strategies)

This chapter provides a comprehensive explanation of hedge fund replication. This chapter first reviews the characteristics of hedge fund returns. Then, the emergence of hedge fund replication products is discussed. Hedge fund replication methods are classified into three categories: Rule-based, Factor-based, and Distribution replicating approaches. These approaches attempt to capture dierent aspects of hedge fund returns. This chapter explains the three methods.

A New Hedge Fund Replication Method With The Dynamic Optimal Portfolio

This paper provides a new hedge fund replication method, which extends Kat and Palaro (2005) and Papageorgiou, Remillard and Hocquard (2008) to multiple trading assets with both long and short positions. The method generates a target payoff distribution by the cheapest dynamic portfolio. It is regarded as an extension of Dybvig (1988) to continuous-time framework and dynamic portfolio optimization where the dynamic trading strategy is derived analytically by applying Malliavin calculus. It is shown that the cost minimization is equivalent to maximization of a certain class of von Neumann-Morgenstern utility functions. The method is applied to the replication of a CTA/Managed Futures Index in practice.

"Generating a Target Payoff Distribution with the Cheapest Dynamic Portfolio: An Application to Hedge Fund Replication"

This paper provides a new method to construct a dynamic optimal portfolio for asset management. This method generates a target payoff distribution using the cheapest dynamic trading strategy. As a practical example, the method is applied to hedge fund replication. This dynamic portfolio strategy is regarded as an extension of a hedge fund replication methodology that was developed by Kat and Palaro (2005a, b) and Papageorgiou, Remillard and Hocquard (2008) to address multiple trading assets with both long and short positions. Empirical analyses show that such an extension significantly improves the performance of replication in practice.