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dc.contributor.authorVan Vuuren, G.
dc.contributor.authorYacumakis, R.
dc.identifier.citationVan Vuuren, G. & Yacumakis, R. 2015. Hedge fund performance evaluation using the Kalman filter. Journal for studies in economics and econometrics, 39(3):1-23. []en_US
dc.description.abstractIn 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 marketen_US
dc.publisherBureau for Economic Research and the Graduate School of Business, University of Stellenbosch.en_US
dc.titleHedge fund performance evaluation using the Kalman filteren_US
dc.contributor.researchID21277087 - Yacumakis, Ruan
dc.contributor.researchID12001333 - Van Vuuren, Gary Wayne

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