Hedge fund performance evaluation using the Kalman filter

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.