Forecasting time-varying covariance with a range-based dynamic conditional correlation model
This paper proposes a range-based dynamic conditional correlation (DCC) model combined by the return-based DCC model and the conditional autoregressive range (CARR) model. The substantial gain in efficiency of volatility estimation can boost the accuracy for estimating time-varying covariances. As to the empirical study, we use the S&P 500 stock index and the 10-year treasury bond futures to examine both in-sample and out-of-sample results for six models, including MA100, EWMA, CCC, BEKK, return-based DCC, and range-based DCC. Of all the models considered, the range-based DCC model is largely supported in estimating and forecasting the covariance matrices.
KeywordsCARR DCC Dynamic covariance Range Volatility
JEL ClassificationC1 C5 G11
We are greatful indebted to Cheng F. Lee, the editor and the anonymous referees for many insightful comments. The first author, R. Y. Chou was supported by project NSC 97-41-H-001-014 for part of the research in this article. The corresponding author, C.-C. Wu also gratefully acknowledge the financial support from the National Science Council (NSC: 96-2416-H-003-MY2).
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