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Title: A novel multivariate volatility modeling for risk management in stock markets
Authors: Wei, ZK 
Yiu, KFC 
Wong, H 
Chan, KY
Keywords: Generalized autoregressive conditional heteroscedastic modeling
Model averaging techniques
Multivariate volatility models
Risk management to future markets
Issue Date: 2018
Publisher: Springer
Source: International journal of fuzzy systems, 2018, v. 20, no. 1, p. 116-127 How to cite?
Journal: International journal of fuzzy systems 
Abstract: 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.
ISSN: 1562-2479
EISSN: 2199-3211
DOI: 10.1007/s40815-017-0298-x
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