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    Hedge fund performance evaluation using the Kalman filter

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    Date
    2015
    Author
    Van Vuuren, G.
    Yacumakis, R.
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    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
    URI
    http://hdl.handle.net/10394/18489
    http://reference.sabinet.co.za/webx/access/electronic_journals/bersee/bersee_v39_n3_a1.pdf
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    • Faculty of Natural and Agricultural Sciences [4311]

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