CAJ | 학술논문

针对高频金融数据收益率序列的厚尾和偏斜性，建立了偏 t 误差分布假设下的R-GARCH（1，2）模型，对上证380指数5 min 频率的高频数据进行了 VaR 预测，并与经典的正态分布和 t 分布误差假设下的 R-GARCH（1，2）模型的预测精度进行了对比分析。结果表明，误差项服从偏 t 分布的 R-GARCH（1，2）模型能够有效识别上证380指数收益率序列的分布特征，并且能够精确地测量其收益风险。
침대고빈금융수거수익솔서렬적후미화편사성，건립료편 t 오차분포가설하적R-GARCH（1，2）모형，대상증380지수5 min 빈솔적고빈수거진행료 VaR 예측，병여경전적정태분포화 t 분포오차가설하적 R-GARCH（1，2）모형적예측정도진행료대비분석。결과표명，오차항복종편 t 분포적 R-GARCH（1，2）모형능구유효식별상증380지수수익솔서렬적분포특정，병차능구정학지측량기수익풍험。
We built the realized GARCH(1,2)model with skewed student’s t distribution to forecast VaR of Shanghai Stock Exchange 380 index for the high-frequency financial data with a fat tail and a-symmetry. The model with normal distribution and student’s t distribution was used for comparison. The empirical results show that the realized GARCH model with the skewed student’s t distribution can identify the characteristics of the return series of Shanghai Stock Exchange 380 index and measure the risk of Shanghai Stock Exchange 380 index more accurately.