Post by fireopal on Dec 18, 2008 21:28:13 GMT 4
Thin-Trading Effects in Beta: Bias V. Estimation Error
Piet Sercu
Katholieke Universiteit, Leuven
Martina L. Vandebroek
Katholieke Universiteit Leuven - Faculty of Business and Economics
Tom Vinaimont
City University of Hong Kong - Department of Economics & Finance
Journal of Business Finance & Accounting, Vol. 35, Issue 9-10, pp. 1196-1219, November/December 2008
Abstract:
Two regression coefficients often used in Finance, the Scholes-Williams (1977) quasi-multiperiod thin-trading beta and the Hansen-Hodrick (1980) overlapping-periods regression coefficient, can both be written as instrumental-variables estimators. Competitors are Dimson's beta and the Hansen-Hodrick original OLS beta. We check the performance of all these estimators and the validity of the t-tests in small and medium samples, in and outside their stated assumptions, and we report their performances in a hedge-fund style portfolio-management application. In all experiments as well as in the real-data estimates, less bias comes at the cost of a higher standard error. Our hedge-portfolio experiment shows that the safest procedure even is to simply match by size and industry; any estimation just adds noise. There is a clear relation between portfolio variance and the variance of the beta estimator used in market-neutralizing the portfolio, dwarfing the beneficial effect of bias
papers.ssrn.com/sol3/papers.cfm?abstract_id=1304360
Piet Sercu
Katholieke Universiteit, Leuven
Martina L. Vandebroek
Katholieke Universiteit Leuven - Faculty of Business and Economics
Tom Vinaimont
City University of Hong Kong - Department of Economics & Finance
Journal of Business Finance & Accounting, Vol. 35, Issue 9-10, pp. 1196-1219, November/December 2008
Abstract:
Two regression coefficients often used in Finance, the Scholes-Williams (1977) quasi-multiperiod thin-trading beta and the Hansen-Hodrick (1980) overlapping-periods regression coefficient, can both be written as instrumental-variables estimators. Competitors are Dimson's beta and the Hansen-Hodrick original OLS beta. We check the performance of all these estimators and the validity of the t-tests in small and medium samples, in and outside their stated assumptions, and we report their performances in a hedge-fund style portfolio-management application. In all experiments as well as in the real-data estimates, less bias comes at the cost of a higher standard error. Our hedge-portfolio experiment shows that the safest procedure even is to simply match by size and industry; any estimation just adds noise. There is a clear relation between portfolio variance and the variance of the beta estimator used in market-neutralizing the portfolio, dwarfing the beneficial effect of bias
papers.ssrn.com/sol3/papers.cfm?abstract_id=1304360