Abstract
In this paper, we concentrate on the problem of stock market risk measurement, which is of great importance for the healthy development of the stock market. The main innovations of this paper lie in that 1) we introduce the GARCH model in stock market risk measuring, and 2) we utilize the Value-at-Risk (VaR) as the stock market risk measure. In order to solve the volatility prediction problem, GARCH model is developed to allow for a great more flexible lag structure and VaR is defined as the loss associated with the low percentile of the return distribution. As the performance of the GARCH model highly depends on the parameter selection, we propose an improved genetic algorithm to estimate optimal parameters for the GARCH model. Finally, to test the effectiveness of the proposed GA-GARCH algorithm, we choose Shanghai composite index and Shenzhen Compositional Index to make performance evaluation. Experimental results demonstrate that proposed GA-GARCH model can effectively cover the stock market risk.
