| dc.contributor.author | Van Vuuren, G. | |
| dc.contributor.author | Yacumakis, R. | |
| dc.date.accessioned | 2016-08-31T12:24:08Z | |
| dc.date.available | 2016-08-31T12:24:08Z | |
| dc.date.issued | 2015 | |
| dc.identifier.citation | Van Vuuren, G. & Yacumakis, R. 2015. Hedge fund performance evaluation using the Kalman filter. Journal for studies in economics and econometrics, 39(3):1-23. [https://www.ber.ac.za/Research/S-E-E/] | en_US |
| dc.identifier.issn | 0379-6205 | |
| dc.identifier.uri | http://hdl.handle.net/10394/18489 | |
| dc.identifier.uri | http://reference.sabinet.co.za/webx/access/electronic_journals/bersee/bersee_v39_n3_a1.pdf | |
| dc.description.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 | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Bureau for Economic Research and the Graduate School of Business, University of Stellenbosch. | en_US |
| dc.title | Hedge fund performance evaluation using the Kalman filter | en_US |
| dc.type | Article | en_US |
| dc.contributor.researchID | 21277087 - Yacumakis, Ruan | |
| dc.contributor.researchID | 12001333 - Van Vuuren, Gary Wayne | |
