Volatility and dynamic dependence modeling: Review, applications, and financial risk management

Mike K. P. SO, Man Ying Amanda CHU, Cliff C. Y. LO, Chun Yin IP

Research output: Contribution to journalArticlespeer-review

1 Citation (Scopus)

Abstract

Since the introduction of ARCH models close to 40 years ago, a wide range of models for volatility estimation and prediction have been developed and integrated into asset allocation, financial derivative pricing, and financial risk management. Research has also been very active in extending volatility modeling to dependence modeling and in developing our understanding of risk and uncertainty in financial systems. This paper presents a review on the statistical modeling on volatility and dynamic dependence of financial returns. In addition, we present a real data example using a time-varying copula model to estimate the dynamic dependence of stock returns. Research on volatility and dynamic dependence modeling will continue to encounter statistical and computational challenges; it is necessary to persist in dealing with the 3H (high dimension, high frequency, high complexity) paradigm in modeling. Copyright © 2021 The Authors. WIREs Computational Statistics published by Wiley Periodicals LLC.
Original languageEnglish
Article numbere1567
JournalWiley Interdisciplinary Reviews: Computational Statistics
Early online dateJun 2021
DOIs
Publication statusE-pub ahead of print - Jun 2021

Citation

So, M. K. P., Chu, A. M. Y., Lo, C. C. Y., & Ip, C. Y. (2021). Volatility and dynamic dependence modeling: Review, applications, and financial risk management. Wiley Interdisciplinary Reviews: Computational Statistics. Advance online publication. doi: 10.1002/wics.1567

Keywords

  • Copula
  • GARCH
  • High-frequency data
  • Risk management
  • Stochastic volatility
  • The 3H paradigm

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