A neural network enhanced volatility component model
Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a n...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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2020
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| Online Access: | https://eprints.nottingham.ac.uk/60139/ |
| _version_ | 1848799733499297792 |
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| author | Zhai, Jia Cao, Yi Liu, Xiaoquan |
| author_facet | Zhai, Jia Cao, Yi Liu, Xiaoquan |
| author_sort | Zhai, Jia |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a novel two-component volatility model. The realized volatility is decomposed by a nonparametric filter into long- and short-run components, which are modeled by an artificial neural network and an ARMA process, respectively. We use intraday data on four major exchange rates and a Chinese stock index to construct daily realized volatility and perform out-of-sample evaluation of volatility forecasts generated by our model and well-established alternatives. Empirical results show that our model outperforms alternative models across all statistical metrics and over different forecasting horizons. Furthermore, volatility forecasts from our model offer economic gain to a mean-variance utility investor with higher portfolio returns and Sharpe ratio. |
| first_indexed | 2025-11-14T20:40:21Z |
| format | Article |
| id | nottingham-60139 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:40:21Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-601392020-03-23T01:22:58Z https://eprints.nottingham.ac.uk/60139/ A neural network enhanced volatility component model Zhai, Jia Cao, Yi Liu, Xiaoquan Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a novel two-component volatility model. The realized volatility is decomposed by a nonparametric filter into long- and short-run components, which are modeled by an artificial neural network and an ARMA process, respectively. We use intraday data on four major exchange rates and a Chinese stock index to construct daily realized volatility and perform out-of-sample evaluation of volatility forecasts generated by our model and well-established alternatives. Empirical results show that our model outperforms alternative models across all statistical metrics and over different forecasting horizons. Furthermore, volatility forecasts from our model offer economic gain to a mean-variance utility investor with higher portfolio returns and Sharpe ratio. 2020-02-19 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/60139/1/Xiaoquan-merged.pdf Zhai, Jia, Cao, Yi and Liu, Xiaoquan (2020) A neural network enhanced volatility component model. Quantitative Finance . pp. 1-15. ISSN 1469-7688 Wavelet analysis; ARMA process; Volatility prediction; Exchange rates http://dx.doi.org/10.1080/14697688.2019.1711148 doi:10.1080/14697688.2019.1711148 doi:10.1080/14697688.2019.1711148 |
| spellingShingle | Wavelet analysis; ARMA process; Volatility prediction; Exchange rates Zhai, Jia Cao, Yi Liu, Xiaoquan A neural network enhanced volatility component model |
| title | A neural network enhanced volatility component model |
| title_full | A neural network enhanced volatility component model |
| title_fullStr | A neural network enhanced volatility component model |
| title_full_unstemmed | A neural network enhanced volatility component model |
| title_short | A neural network enhanced volatility component model |
| title_sort | neural network enhanced volatility component model |
| topic | Wavelet analysis; ARMA process; Volatility prediction; Exchange rates |
| url | https://eprints.nottingham.ac.uk/60139/ https://eprints.nottingham.ac.uk/60139/ https://eprints.nottingham.ac.uk/60139/ |