中文摘要：

考虑股市波动结构转换的特性和参数模型会产生误设的情况,提出具有马尔可夫结构转换的非参数GARCH模型,并利用非参数估计技术估计波动率.将沪深股市的波动变化分为下跌、盘整和上涨3个状态,分别采用基于马尔可夫结构转换参数与非参数GARCH（MRS-GARCH）模型对我国沪深股市的波动率进行估计和预测,运用MSE1、MSE2和QLIKE对估计和预测出的波动率进行评价.结果表明误差分布服从正态分布的参数和非参数MRS-GARCH模型的估计和预测更准确.在此基础上对沪深股市收益率的动态VaR值进行估计,然后运用Kupiec检验法对这两类模型在预测实际损失的表现进行评价.估计和检验结果表明,基于参数和非参数的MRS-GARCH模型都能较好地估计中国沪深股市VaR,且基于非参数MRS-GARCH模型的VaR估计效果更好.

英文摘要：

Considering the characteristics of volatility with regime switching of the stock market and the mis- specification of the parametric model, the paper proposes a nonparametric GARCH model with Markov regime switching, and uses nonparametric estimating technique to estimate the volatility. We classify the volatility of China＇ s stock market into three states： the fall, the consolidation and the rise. We estimate and forecast the volatility of both Shanghai and Shenzhen stock markets using parametric and nonparametric GARCH models with Markov regime switching, and then we evaluate the performances of these models using MSE~, MSE2 and QLIKE. The result shows that the parametric and nonparametric MRS-GARCH model in which the error term follows a normal distribution is more accurate. Based on these models, we estimate the dynamic VaR of the return of Shanghai composite index and Shenzhen component index of China＇ s stock market. Then, using Ku- piec back testing, we evaluate the performances of these models to predict the market risk. It is concluded that the parametric and nonparametric MRS-GARCH models can both estimate the VaR of China＇ s stock market well, and what is more, the performance of the nonparametric MRS-GARCH model is better than that of the parametric MRS-GARCH model.