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Hedge Fund Performance Evaluation Using the Kalman Filter

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  • G. van Vuuren
  • R. Yacumakis

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.

Suggested Citation

  • G. van Vuuren & R. Yacumakis, 2015. "Hedge Fund Performance Evaluation Using the Kalman Filter," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 39(3), pages 1-24, December.
    • Handle: RePEc:taf:rseexx:v:39:y:2015:i:3:p:1-24
    DOI: 10.1080/10800379.2015.12097283
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