Cholesky Realized Stochastic Volatility Model

Shinichiro Shirota, Yasuhiro Omori (), Hedibert. F. Lopes and Haixiang Piao
Additional contact information
Shinichiro Shirota: Department of Statistical Science, Duke University
Hedibert. F. Lopes: Insper Institute of Education and Research
Haixiang Piao: Nippon Life Insurance Company

No CIRJE-F-979, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: Multivariate stochastic volatility models are expected to play important roles in financial applications such as asset allocation and risk management. However, these models suffer from two major difficulties: (1) there are too many parameters to estimate using only daily asset returns and (2) estimated covariance matrices are not guaranteed to be positive de nite. Our approach takes advantage of realized covariances to attain the efficient estimation of parameters by incorporating additional information for the co-volatilities, and considers Cholesky decomposition to guarantee the positive definiteness of the covariance matrices. In this framework, we propose a exible modeling for stylized facts of financial markets such as dynamic correlations and leverage effects among volatilities. Taking a Bayesian approach, we describe Markov Chain Monte Carlo implementation with a simple but efficient sampling scheme. Our model is applied to nine U.S. stock returns data, and the model comparison is conducted based on portfolio performances. --

Pages: 28pages
Date: 2015-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Citations: View citations in EconPapers (2)

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Journal Article: Cholesky realized stochastic volatility model (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2015cf979

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