No abstract

No abstract

No 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.

No abstract

This paper examines the impact of investor preferences on the optimal futures hedging strategy and associated hedging performance. Explicit risk aversion levels are often overlooked in hedging analysis. Applying a mean-variance hedging objective, the optimal futures hedging ratio is determined for a range of investor preferences on risk aversion, hedging horizon and expected returns. Wavelet analysis is applied to illustrate how investor time horizon shapes hedging strategy. Empirical results reveal substantial variation of the optimal hedge ratio for distinct investor preferences and are supportive of the hedging policies of real firms.Hedging performance is then shown to be strongly dependent on underlying preferences. In particular, investors with high levels of risk aversion and a short horizon reduce the risk of the hedge portfolio but achieve inferior utility in comparison to those with low risk aversion.

No abstract

This study investigates the hedging effectiveness of a dynamic moving‐window OLS hedging model, formed using wavelet decomposed time‐series. The wavelet transform is applied to calculate the appropriate dynamic minimum‐variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dynamic multiscale hedging strategy is then tested, both in‐ and out‐of‐sample, using standard variance reduction and expanded to include a downside risk metric, the scale‐dependent Value‐at‐Risk. Measured using variance reduction, the effectiveness converges to one at longer scales, while a measure of VaR reduction indicates a portion of residual risk remains at all scales. Analysis of the hedge portfolio distributions indicate that this unhedged tail risk is related to excess portfolio kurtosis found at all scales.