A Novel Multivariate Volatility Modeling for Risk Management in Stock Markets

Zi Kai Wei, Ka Fai Cedric Yiu, Heung Wong, Kit Yan Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Volatility modeling is crucial for risk management and asset allocation; this is an influential area in financial econometrics. The central requirement of volatility modeling is to be able to forecast volatility accurately. The literature review of volatility modeling shows that the approaches of model averaging estimation are commonly used to reduce model uncertainty in order to achieve a satisfactory forecasting reliability. However, those approaches attempt to forecast more reliable volatilities by integrating all forecasting outcomes equally from several volatility models. Forecasting patterns generated by each model may be similar. This may cause redundant computation without improving forecasting reliability. The proposed multivariate volatility modeling method which is called the fuzzy-method-involving multivariate volatility model (abbreviated as FMVM) classifies the individual models into smaller scale clusters and selects the most representative model in each cluster. Hence, repetitive but unnecessary computational burden can be reduced, and forecasting patterns from representative models can be integrated. The proposed FMVM is benchmarked against existing multivariate volatility models on forecasting volatilities of Hong Kong Hang Seng Index constituent stocks. Numerical results show that it can obtain relatively lower forecasting errors with less model complexity.

Original languageEnglish
Pages (from-to)116-127
Number of pages12
JournalInternational Journal of Fuzzy Systems
Issue number1
Publication statusPublished - Jan 2018


  • Generalized autoregressive conditional heteroscedastic modeling
  • Model averaging techniques
  • Multivariate volatility models
  • Risk management to future markets

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence


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