Properties of a robust asymmetric GARCH model and its application in evaluating the Chinese stock market

Some properties of a robust asymmetric GARCH model and its application in evaluating risks in the Chinese stock market are investigated in this paper. The main aim of the study is to effectively measure stock market risks to maintain the healthy development of the stock market by using the GARCH model. The GARCH and Value-at-Risk models are employed to assess and measure stock market risks, respectively. To promote the quality of parameter selection for the GARCH model, an improved genetic algorithm (GA) to estimate optimal parameters is proposed. Experimental results prove that our proposed GA-GARCH model can measure stock market risks with high accuracy.