Time-Varying Window Length for Correlation Forecasts
46 Pages Posted: 20 Feb 2016 Last revised: 26 Nov 2017
Date Written: November 24, 2017
Forecasting correlations between stocks and commodities is important for diversification across asset classes and other risk management decisions. Correlations forecasts are affected by model uncertainty, sources of which can include uncertainty about changing fundamentals and associated parameters (model instability), structural breaks and non-linearities 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
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