Random Matrix Theory and Fund of Funds Portfolio Optimisation

TL;DR

This paper applies random matrix theory to hedge fund return correlations to identify meaningful structure, improve risk estimation, and enhance portfolio optimization in fund of funds strategies.

Contribution

It introduces a RMT-based method to clean correlation matrices, reducing noise and improving risk prediction for hedge fund portfolios.

Findings

Eigenvalues deviating from RMT spectrum reveal strategy groups.

Cleaning the correlation matrix reduces risk estimation errors.

Improved risk profile for fund of hedge funds.

Abstract

The proprietary nature of Hedge Fund investing means that it is common practise for managers to release minimal information about their returns. The construction of a Fund of Hedge Funds portfolio requires a correlation matrix which often has to be estimated using a relatively small sample of monthly returns data which induces noise. In this paper random matrix theory (RMT) is applied to a cross-correlation matrix C, constructed using hedge fund returns data. The analysis reveals a number of eigenvalues that deviate from the spectrum suggested by RMT. The components of the deviating eigenvectors are found to correspond to distinct groups of strategies that are applied by hedge fund managers. The Inverse Participation ratio is used to quantify the number of components that participate in each eigenvector. Finally, the correlation matrix is cleaned by separating the noisy part from the non-noisy part of C. This technique is found to greatly reduce the difference between the predicted and realised risk of a portfolio, leading to an improved risk profile for a fund of hedge funds.