A Novel Multivariate Volatility Modeling for Risk Management in Stock Markets
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 mo...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Chinese Fuzzy Systems Association
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/67540 |
| _version_ | 1848761592398741504 |
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| author | Wei, Z. Yiu, K. Wong, H. Chan, Kit Yan |
| author_facet | Wei, Z. Yiu, K. Wong, H. Chan, Kit Yan |
| author_sort | Wei, Z. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T10:34:07Z |
| format | Journal Article |
| id | curtin-20.500.11937-67540 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:34:07Z |
| publishDate | 2018 |
| publisher | Chinese Fuzzy Systems Association |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-675402018-11-29T01:54:29Z A Novel Multivariate Volatility Modeling for Risk Management in Stock Markets Wei, Z. Yiu, K. Wong, H. Chan, Kit Yan 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. 2018 Journal Article http://hdl.handle.net/20.500.11937/67540 10.1007/s40815-017-0298-x Chinese Fuzzy Systems Association restricted |
| spellingShingle | Wei, Z. Yiu, K. Wong, H. Chan, Kit Yan A Novel Multivariate Volatility Modeling for Risk Management in Stock Markets |
| title | A Novel Multivariate Volatility Modeling for Risk Management in Stock Markets |
| title_full | A Novel Multivariate Volatility Modeling for Risk Management in Stock Markets |
| title_fullStr | A Novel Multivariate Volatility Modeling for Risk Management in Stock Markets |
| title_full_unstemmed | A Novel Multivariate Volatility Modeling for Risk Management in Stock Markets |
| title_short | A Novel Multivariate Volatility Modeling for Risk Management in Stock Markets |
| title_sort | novel multivariate volatility modeling for risk management in stock markets |
| url | http://hdl.handle.net/20.500.11937/67540 |