Time-Varying Window Length for Correlation Forecasts

Yoontae Jeon and Thomas McCurdy
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Yoontae Jeon: Ted Rogers School of Management, Ryerson University, 55 Dundas Street West, Toronto, ON M5G 2C3, Canada

Econometrics, 2017, vol. 5, issue 4, 1-29

Abstract: Forecasting correlations between stocks and commodities is important for diversification across asset classes and other risk management decisions. Correlation forecasts are affected by model uncertainty, the sources of which can include uncertainty about changing fundamentals and associated parameters (model instability), structural breaks and nonlinearities due, for example, to regime switching. We use approaches that weight historical data according to their predictive content. Specifically, we estimate two alternative models, ‘time-varying weights’ and ‘time-varying window’, in order to maximize the value of past data for forecasting. Our empirical analyses reveal that these approaches provide superior forecasts to several benchmark models for forecasting correlations.

Keywords: model uncertainty; variance and correlation forecasts; time-varying window length (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2017
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