Semi-parametric forecasting of realized volatility
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.
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|Item Type:||Journal Article|
|Subjects:||Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > ECONOMETRICS (140300) > Time-Series Analysis (140305)|
Australian and New Zealand Standard Research Classification > COMMERCE MANAGEMENT TOURISM AND SERVICES (150000) > BANKING FINANCE AND INVESTMENT (150200) > Financial Econometrics (150202)
|Divisions:||Current > QUT Faculties and Divisions > QUT Business School|
Current > Schools > School of Economics & Finance
|Copyright Owner:||Walter de Gruyter GmbH and Co. KG|
|Copyright Statement:||The final publication is available at www.degruyter.com|
|Deposited On:||19 Jul 2012 16:30|
|Last Modified:||19 Mar 2013 09:18|
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