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

被引：33

作者：

Chou R.Y. ^{[1
,2
]}

Wu C.-C. ^{[3
]}

Liu N. ^{[4
]}

机构：

[1] Institute of Economics, Academia Sinica, Taipei

[2] Institute of Business Management, National Chiao Tung University, Hsinchu

[3] Department of Finance, National Kaohsiung First University of Science and Technology, Kaohsiung City 811, 2 Jhuoyue Rd, Nanzih

[4] Institute of Finance, National Chiao Tung University, Hsinchu

来源：

Review of Quantitative Finance and Accounting | 2009年 / 33卷 / 4期

关键词：

CARR; DCC; Dynamic covariance; Range; Volatility;

D O I：

10.1007/s11156-009-0113-3

中图分类号：

学科分类号：

摘要：

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. © Springer Science+Business Media, LLC 2009.

引用

页码：327 / 345

页数：18

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