Next Article in Journal
Recent Developments in Cointegration
Next Article in Special Issue
A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns
Previous Article in Journal
Reducing Approximation Error in the Fourier Flexible Functional Form
Previous Article in Special Issue
Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility
Open AccessArticle

Time-Varying Window Length for Correlation Forecasts

1
Ted Rogers School of Management, Ryerson University, 55 Dundas Street West, Toronto, ON M5G 2C3, Canada
2
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
Econometrics 2017, 5(4), 54; https://doi.org/10.3390/econometrics5040054
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
Show Figures

Figure 1

MDPI and ACS Style

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

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop