Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model
This paper endeavors to enhance the prediction of volatility in financial markets by developing a novel hybrid model that integrates generalized autoregressive conditional heteroskedasticity (GARCH) models and long short-term memory (LSTM) neural networks. Using high-frequency data, we first estimat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/18/3937 |
| _version_ | 1827725393495326720 |
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| author | Weibin Wang Yao Wu |
| author_facet | Weibin Wang Yao Wu |
| author_sort | Weibin Wang |
| collection | DOAJ |
| description | This paper endeavors to enhance the prediction of volatility in financial markets by developing a novel hybrid model that integrates generalized autoregressive conditional heteroskedasticity (GARCH) models and long short-term memory (LSTM) neural networks. Using high-frequency data, we first estimate realized volatility as a robust measure of volatility. We then feed the outputs of multiple GARCH models into an LSTM network, creating a hybrid model that leverages the strengths of both approaches. The predicted volatility from the hybrid model is used to generate trading strategy signals, which are subsequently used to build an investment strategy. Empirical analysis using the China Securities Index 300 (CSI300) dataset demonstrates that the hybrid model significantly improves value-at-risk (VaR) prediction performance compared to traditional GARCH models. This study’s findings have broad implications for risk management in financial markets, suggesting that hybrid models incorporating mathematical models and economic mechanisms can enhance derivative pricing, portfolio risk management, hedging transactions, and systemic risk early-warning systems. |
| first_indexed | 2024-03-10T22:29:42Z |
| format | Article |
| id | doaj.art-23d26bd282304929a8f5f0d2edcb462e |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T22:29:42Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-23d26bd282304929a8f5f0d2edcb462e2023-11-19T11:49:39ZengMDPI AGMathematics2227-73902023-09-011118393710.3390/math11183937Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction ModelWeibin Wang0Yao Wu1School of Economics and Management, Sanming University, Sanming 365004, ChinaSchool of International Economics and Management, Beijing Technology and Business University, Beijing 100048, ChinaThis paper endeavors to enhance the prediction of volatility in financial markets by developing a novel hybrid model that integrates generalized autoregressive conditional heteroskedasticity (GARCH) models and long short-term memory (LSTM) neural networks. Using high-frequency data, we first estimate realized volatility as a robust measure of volatility. We then feed the outputs of multiple GARCH models into an LSTM network, creating a hybrid model that leverages the strengths of both approaches. The predicted volatility from the hybrid model is used to generate trading strategy signals, which are subsequently used to build an investment strategy. Empirical analysis using the China Securities Index 300 (CSI300) dataset demonstrates that the hybrid model significantly improves value-at-risk (VaR) prediction performance compared to traditional GARCH models. This study’s findings have broad implications for risk management in financial markets, suggesting that hybrid models incorporating mathematical models and economic mechanisms can enhance derivative pricing, portfolio risk management, hedging transactions, and systemic risk early-warning systems.https://www.mdpi.com/2227-7390/11/18/3937financial marketvolatility predictionhybrid modelrisk management |
| spellingShingle | Weibin Wang Yao Wu Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model Mathematics financial market volatility prediction hybrid model risk management |
| title | Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model |
| title_full | Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model |
| title_fullStr | Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model |
| title_full_unstemmed | Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model |
| title_short | Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model |
| title_sort | risk analysis of the chinese financial market with the application of a novel hybrid volatility prediction model |
| topic | financial market volatility prediction hybrid model risk management |
| url | https://www.mdpi.com/2227-7390/11/18/3937 |
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