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Time-Varying Window Length for Correlation Forecasts

Ted Rogers School of Management, Ryerson University, 55 Dundas Street West, Toronto, ON M5G 2C3, Canada
Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, ON M5S 3E6, Canada
Author to whom correspondence should be addressed.
Academic Editors: Deniz Erdemlioglu, Olivier Scaillet and Kamil Yilmaz
Received: 7 September 2017 / Revised: 20 November 2017 / Accepted: 24 November 2017 / Published: 11 December 2017
(This article belongs to the Special Issue Volatility Modeling)
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. View Full-Text
Keywords: model uncertainty; variance and correlation forecasts; time-varying window length model uncertainty; variance and correlation forecasts; time-varying window length
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MDPI and ACS Style

Jeon, Y.; McCurdy, T.H. Time-Varying Window Length for Correlation Forecasts. Econometrics 2017, 5, 54.

AMA Style

Jeon Y, McCurdy TH. Time-Varying Window Length for Correlation Forecasts. Econometrics. 2017; 5(4):54.

Chicago/Turabian Style

Jeon, Yoontae, and Thomas H. McCurdy. 2017. "Time-Varying Window Length for Correlation Forecasts" Econometrics 5, no. 4: 54.

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