Time-Varying Window Length for Correlation Forecasts
AbstractForecasting 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. View Full-Text
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Jeon, Y.; McCurdy, T.H. Time-Varying Window Length for Correlation Forecasts. Econometrics 2017, 5, 54.
Jeon Y, McCurdy TH. Time-Varying Window Length for Correlation Forecasts. Econometrics. 2017; 5(4):54.Chicago/Turabian Style
Jeon, Yoontae; McCurdy, Thomas H. 2017. "Time-Varying Window Length for Correlation Forecasts." Econometrics 5, no. 4: 54.
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