Cross-Commodity Analysis and Applications to Risk management.

The understanding of joint asset return distributions is an important ingredient for managing risks of portfolios. While this is a well-discussed issue in fixed income and equity markets, it is a challenge for energy commodities. In this paper we are concerned with describing the joint return distribution of energy related commodities futures, namely power, oil, gas, coal and carbon. The objective of the paper is threefold. First, we conduct a careful analysis of empirical returns and show how the class of multivariate generalized hyperbolic distributions performs in this context. Second, we present how risk measures can be computed for commodity portfolios based on generalized hyperbolic assumptions. And finally, we discuss the implications of our findings for risk management analyzing the exposure of power plants which represent typical energy portfolios. Our main findings are that risk estimates based on a normal distribution in the context of energy commodities can be statistically improved using generalized hyperbolic distributions. Those distributions are flexible enough to incorporate many characteristics of commodity returns and yield more accurate risk estimates. Our analysis of the market suggests that carbon allowances can be a helpful tool for controlling the risk exposure of a typical energy portfolio representing a power plantCommodities; Risk;

"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.

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.

Cross-commodity analysis and applications to risk management.

The understanding of joint asset return distributions is an important ingredient for managing risks of portfolios. Although this is a well-discussed issue in fixed income and equity markets, it is a challenge for energy commodities. In this study we are concerned with describing the joint return distribution of energy-related commodities futures, namely power, oil, gas, coal, and carbon. The objective of the study is threefold. First, we conduct a careful analysis of empirical returns and show how the class of multivariate generalized hyperbolic distributions performs in this context. Second, we present how risk measures can be computed for commodity portfolios based on generalized hyperbolic assumptions. And finally,we discuss the implications of our findings for risk management analyzing the exposure of power plants, which represent typical energy portfolios. Our main findings are that risk estimates based on a normal distribution in the context of energy commodities can be statistically improved using generalized hyperbolic distributions. Those distributions are flexible enough to incorporate many characteristics of commodity returns and yield more accurate risk estimates. Our analysis of the market suggests that carbon allowances can be a helpful tool for controlling the risk exposure of a typical energy portfolio representing a power plantCommodities; Risk;

"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.