A neural network enhanced volatility component model
2019
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
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