A neural network enhanced volatility component model

Zhai, Jia, Cao, Yi and Liu, Xiaoquan (2020) A neural network enhanced volatility component model. Quantitative Finance . pp. 1-15. ISSN 1469-7688

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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.

Item Type: Article
Keywords: Wavelet analysis; ARMA process; Volatility prediction; Exchange rates
Schools/Departments: University of Nottingham Ningbo China > Faculty of Business > Nottingham University Business School China
Identification Number: https://doi.org/10.1080/14697688.2019.1711148
Depositing User: Zhou, Elsie
Date Deposited: 23 Mar 2020 01:22
Last Modified: 23 Mar 2020 01:22
URI: https://eprints.nottingham.ac.uk/id/eprint/60139

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