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.
Highlights
Volatility modeling and prediction play a crucial role in asset allocation, portfolio construction and risk management, as accurate volatility forecasts are of huge importance to traders, fund managers, and regulators
The autoregressive neural network (ARNN) has been applied to the time series modeling and shown to outperform traditional models such as the GARCH, EGARCH, and Autoregressive Fractionally Integrated Moving Average (ARFIMA) in volatility forecasting in the computer science literature (Kristjanpoller et al, 2014; Kristjanpoller and Minutolo, 2016), especially after the data are deseasonalized (Kristjanpoller and Minutolo, 2015)
When the investor is assumed to have a relatively low level of risk aversion at γ=3, portfolios are able to achieve an annual return of 6.63% when volatility forecasts are generated by the Hybrid model for EUR/USD, and the Sharpe ratio is 0.37, while the certainty equivalent return is slightly higher than 3%
Summary
Volatility modeling and prediction play a crucial role in asset allocation, portfolio construction and risk management, as accurate volatility forecasts are of huge importance to traders, fund managers, and regulators. In the baseline forecasting exercises, our neural network enhanced volatility component model consistently dominates the competing models in producing more accurate volatility forecasts with smaller root mean square forecasting error (RMSFE), is always the preferred model in the Diebold and Mariano (1995) pairwise comparison, shows superior predictive ability in the Hansen (2005) test, and offers better interval forecasts in the likelihood ratio tests of Christoffersen (1998). This is the case over all forecasting horizons from one day up to 16 months ahead..
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