The Multivariate GARCH Model and its Application to East Asian Financial Market Integration

Yoshihiko Tsukuda, Junji Shimada and Tatsuyoshi Miyakoshi

Chapter 123 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 4209-4254 from World Scientific Publishing Co. Pte. Ltd.

Abstract: We review briefly multivariate GARCH models in contrast with univariate GARCH models, and clarify the statistical perspective of the DCC-GARCH model introduced by Engel (2002). This model ingeniously compromises two contrary requirements for constructing a model: sufficiently flexible to catch the behaviors of actually observed data process, and sufficiently parsimonious for statistical analysis in practice. Then, we illustrate practical usefulness of the DCC-GARCH through its application to the bond and stock markets in the emerging East Asian countries. The DCC-GARCH can evaluate the comovements of different financial assets by use of dynamic variance decomposition (volatility spillover) in addition to the DCCs. Empirical investigation of this paper clarifies that the bond market integration is still limited in terms of both DCCs and volatility spillover, while the stock markets are highly integrated both regionally and globally.

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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