How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach.

Jung-Bin Su

doi:10.3390/e23091151

Abstract

This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatility forecasting models and eight composed volatility forecasting models to explore whether the neural network approach and the settings of leverage effect and non-normal return distribution can promote the performance of volatility forecasting, and which one of the sixteen models possesses the best volatility forecasting performance. The eight parametric volatility forecasts models are composed of the generalized autoregressive conditional heteroskedasticity (GARCH) or GJR-GARCH volatility specification combining with the normal, Student’s t, skewed Student’s t, and generalized skewed Student’s t distributions. Empirical results show that, the performance for the composed volatility forecasting approach is significantly superior to that for the parametric volatility forecasting approach. Furthermore, the GJR-GARCH volatility specification has better performance than the GARCH one. In addition, the non-normal distribution does not have better forecasting performance than the normal distribution. In addition, the GJR-GARCH model combined with both the normal distribution and a neural network approach has the best performance of volatility forecasting among sixteen models. Thus, a neural network approach significantly promotes the performance of volatility forecasting. On the other hand, the setting of leverage effect can encourage the performance of volatility forecasting whereas the setting of non-normal distribution cannot.

Highlights

Volatility is a statistical measure of the dispersion of returns for a given asset
The composed volatility forecasting models are the parametric volatility forecasting models combined with a neural network approach
Among the above eight parametric volatility forecasting models, the GJR-generalized autoregressive conditional heteroskedasticity (GARCH)-SGT model is the most flexible because this model can capture most of the common features of financial assets and this model can degenerate into the other seven models under the setting of some restrictions

Summary

Introduction

Volatility is a statistical measure of the dispersion of returns for a given asset. A higher volatility means that an asset’s price can change dramatically over a short time period in either direction, and is expected to be less predictable. A lower volatility means that an asset’s price does not fluctuate dramatically, and tends to be more steady (volatility is often measured by either the standard deviation or variance between returns from that same asset). Volatility can be used to measure the amount of uncertainty or risk related to the size of changes in an asset’s price, and it obeys the criteria: ‘the higher the volatility and the riskier the asset’. As to the issue of volatility forecasting, most of literatures used the generalized autoregressive conditional heteroskedasticity (GARCH) family models, a parametric volatility forecasting approach, to predict the volatility of an asset [12,13,14,15,16,17,18,19]

Methods

Results

Conclusion