Hedge Fund Performance Evaluation Using the Kalman Filter

G. van Vuuren and R. Yacumakis

Studies in Economics and Econometrics, 2015, vol. 39, issue 3, 1-24

Abstract: In the capital asset pricing model, portfolio market risk is recognised through β while α summarises asset selection skill. Traditional parameter estimation techniques assume time-invariance and use rolling-window, ordinary least squares regression methods. The Kalman filter estimates dynamic αs and βs where measurement noise covariance and state noise covariance are known - or may be calibrated - in a state-space framework. These time-varying parameters result in superior predictive accuracy of fund return forecasts against ordinary least square (and other) estimates, particularly during the financial crisis of 2008/9 and are used to demonstrate increasing correlation between hedge funds and the market.

Date: 2015
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DOI: 10.1080/10800379.2015.12097283

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