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.

«Performance analysis of niche alternatives and hedge fund strategies»

The interest of institutional investors in hedge funds as alternative investments has grown substantially over the last decade. The key reason for adding alternative investments to a well-diversified institutional portfolio is the risk-return profile, which is achieved by reducing the risk through diversification and enhancing the returns through alpha. In addition to the well-known hedge fund investment strategies, the Swiss investment company Progressive Capital Partners Ltd. offers its own specialized niche alternative assets consisting of music royalties, appraisal and litigation rights. Due to their performance characteristics, the alternative investments are intended to provide an opportunity for pension fund portfolios. The purpose of this master thesis is to analyze the monthly returns of twelve hedge fund strategies, and niche alternatives of Progressive Capital. In addition, the performance of a self-created representative Swiss pension fund portfolio is examined quantitatively with niche alternatives as an alternative asset class. The methodology for the analysis is based on a combination of principal component analysis with three different multi-factor models to explain the returns of hedge fund strategies. An extensive aggregated hedge fund database and a universe of 25 risk factors are employed for the full sample period from August 2007 to December 2018. Furthermore, a portfolio optimization analysis is used on the Swiss pension fund portfolio to evaluate the niche alternatives and other traditional alternative assets based on pension fund investment restrictions. The results showed small differences in the alphas resulting from the three different multi-factor models. The average monthly alpha is highest 0.22 % for the Fung and Hsieh eight-factor model, 0.19 % for the stepwise regression model and lowest with 0.16 % for Fung and Hsieh seven-factor model over all thirteen hedge fund strategies including the niche alternatives. According to these results, Progressive Capital performs better in all three models than the average alphas do. The highest alpha of 0.47 % was gained by the stepwise regression, followed by 0.44 % in the Fung and Hsieh eight-factor model, and 0.37 % in the Fung and Hsieh seven-factor model

Hedge fund replication with a genetic algorithm: breeding a usable mousetrap

This study tests the performance of 14 hedge fund index clones created using parsimonious outof- sample replication portfolios consisting solely of easily accessible assets. We employ a genetic algorithm to integrate two traditional hedge fund replication methods, the factor-based and payoff distribution replication methods, and evaluate over 4500 commonly held stocks, bonds and mutual funds as replicating portfolio components. In-sample performance indicates that hedge funds have return series similar to portfolios of commonly held assets, and out-of-sample results provide evidence that the in-sample relationships can hold with infrequent rebalancing. This hedge fund replication attempt rates well relatively to prior efforts as 11 replicating portfolios have out-of-sample correlation values of at least 60%. Overall, these results show promise for using a genetic algorithm technique to replicate hedge fund returns

Managing Capital Market Risk for Retirement

We offer an overview of solutions available to pension plans to manage capital market risk in order to meet their obligations. We outline the main drivers behind the evolution of asset-liability management (ALM) for pension plans and the emergence of liability-driven investment (LDI) in the last decade. We look at some of the most popular pension de-risking tools and at recent innovations prompted by the Global Financial Crisis. We offer examples based on the rise of cross-asset correlation, the use of hybrid products to mitigate tail risk, and the increasing relevance of counterparty risk mitigation tools such as collateralization. We conclude by outlining some of the main challenges ahead, including developments in pension regulation, centralized clearing of over-the-counter (OTC) instruments, and risk taking incentives in delegated asset management for long term retirement obligations