科研之友
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摘要
The accurate forecasting of stock market volatility is of great theoretical and practical significance for investors to predict stock market trend, optimize asset allocation and avoid risks, and for regulators to warn risks and stabilize market order. In this paper, on the basis of HAR model based on high-frequency trading data, Lasso and random forest method in machine learning are combined to conduct model feature selection, and the nonlinear characteristics among variables are depicted by neural network method, so as to construct several new realized volatility models based on machine learning. Then, the performance of various models in forecasting the realized volatility of Shanghai stock index is evaluated and compared. The empirical results show that, the introduction of the jump component can improve the out-of-sample forecasting accuracy of realized volatility in the stock market. The HAR extended models based on Lasso and random forest for feature selection have significantly better out-of-sample prediction performance than the traditional HAR models and GARCH models. Using the neural network method to describe the nonlinear characteristics of volatility can further improve the out-of-sample prediction accuracy of the model. The Lasso-NN-J model has the best in-sample and out-sample prediction performance among all the investigated forecasting models, and the prediction performance of the model is quite robust under the simulation tests of different rolling window widths, different high-frequency data of individual stocks and random sampling. ? 2024 Chinese Chemical Society.
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