Forecasting time-varying covariance with a range-based dynamic conditional correlation model

  • Ray Yeutien Chou
  • Chun-Chou Wu
  • Nathan Liu
Original Research


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.


CARR DCC Dynamic covariance Range Volatility 

JEL Classification

C1 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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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Institute of EconomicsAcademia SinicaTaipeiTaiwan, ROC
  2. 2.Institute of Business ManagementNational Chiao Tung UniversityHsinchuTaiwan, ROC
  3. 3.Department of FinanceNational Kaohsiung First University of Science and TechnologyKaohsiung CityTaiwan, ROC
  4. 4.Institute of FinanceNational Chiao Tung UniversityHsinchuTaiwan, ROC

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