A neural network enhanced volatility component model

Zhai, J ORCID: https://orcid.org/0000-0002-2746-7749, Cao, Y and Liu, X 2020, 'A neural network enhanced volatility component model' , Quantitative Finance .

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Abstract

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
Schools: Schools > Salford Business School
Journal or Publication Title: Quantitative Finance
Publisher: Taylor & Francis
ISSN: 1469-7688
Related URLs:
Depositing User: J Zhai
Date Deposited: 02 Jan 2020 15:49
Last Modified: 25 Feb 2020 10:15
URI: http://usir.salford.ac.uk/id/eprint/56099

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