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

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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

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

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Publisher
Springer US
Copyright
Copyright © 2009 by Springer Science+Business Media, LLC
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-009-0113-3
Publisher site
See Article on Publisher Site

References

  • Asymmetric dynamics in the correlations of global equity and bond returns
    Cappiello, L; Engle, R; Sheppard, K
  • Risk and volatility: econometric models and financial practice
    Engle, R

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