A neural network enhanced volatility component model

Jia Zhai, Yi Cao, Xiaoquan Liu

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Number of pages15
JournalQuantitative Finance
Early online date19 Feb 2020
Publication statusE-pub ahead of print - 19 Feb 2020

Keywords / Materials (for Non-textual outputs)

  • wavelet analysis
  • ARMA process
  • volatility prediction
  • exchange rates


Dive into the research topics of 'A neural network enhanced volatility component model'. Together they form a unique fingerprint.

Cite this