Semi-parametric forecasting of realized volatility


Autoria(s): Becker, Ralf; Clements, Adam; Hurn, Stan
Data(s)

2011

Resumo

Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/52382/

Publicador

Walter de Gruyter GmbH and Co. KG

Relação

http://eprints.qut.edu.au/52382/1/52383.pdf

DOI:10.2202/1558-3708.1814

Becker, Ralf, Clements, Adam, & Hurn, Stan (2011) Semi-parametric forecasting of realized volatility. Studies in Nonlinear Dynamics and Econometrics, 15(3), pp. 1-21.

Direitos

Walter de Gruyter GmbH and Co. KG

The final publication is available at www.degruyter.com

Fonte

QUT Business School; School of Economics & Finance

Palavras-Chave #140305 Time-Series Analysis #150202 Financial Econometrics
Tipo

Journal Article