What is it about?
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.
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Why is it important?
Volatility is usually used in asset allocation, option pricing , risk management and hedge strategy. Thus, how to accurately predict the volatility of an asset is a very important issue in the actual investment process in the financial field.
Perspectives
From the empirical results, I propose the following important policy implications for investors and fund managers. First, the investors should use the asymmetric GARCH model to precisely forecast the volatility of stock indices. Second, the investors should utilize the composed volatility forecasting models to accurately forecast the volatility of stock indices because the neural net-works approach can handle the complex, non-linear univariate and multivariate relationships that would be difficult to fit using other techniques, and therefore it can significantly promote the performance of volatility forecasting.
Dr Jung-Bin Su
Qilu University of Technology
Read the Original
This page is a summary of: How to Promote the Performance of Parametric Volatility Forecasts in the Stock Market? A Neural Networks Approach, Entropy, September 2021, MDPI AG,
DOI: 10.3390/e23091151.
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