一类金融贝叶斯分位数GARCH模型及其在我国汇率市场中的应用研究  

A Kind of Financial Bayesian Quantile GARCH Model and Its Applicationin China’s Exchange Rate Market

作　　者：姚萍[1] 杨爱军[1] 林金官 YAO Ping;YANG Ai-jun;LIN Jin-guan(College of Economics and Management,Nanjing Forestry University,Nanjing 210037,China;School of Statistics and Mathematics,Nanjing Audit University,Nanjing 211815,China)

机构地区：[1]南京林业大学经济管理学院,江苏南京210037 [2]南京审计大学统计与数学学院,江苏南京211815

出　　处：《数学的实践与认识》2021年第20期10-16,共7页Mathematics in Practice and Theory

基　　金：国家自然科学基金(11971235);江苏省青蓝工程项目(2020)。

摘　　要：现有GARCH模型依赖于参数条件分布形式假设,依然不能有效刻画金融资产收益偏态厚尾特性,分位数回归能给条件分布提供更加全面的描述.在分位数回归和GJR-GARCH模型基础上建立分位数GJR-GARCH模型,并在贝叶斯框架下对模型进行分析;同时利用中国金融市场数据检验分位数GJR-GARCH模型在风险价值预测方面的实际效果.The emprical results show that GARCH model with one kindparametric distribution still can’t capture the characteristics of returns,quantile regression is able to more fully describe these facts.This paper aims to combine GJR-GARCH model and quantile regression to build one type quantile GJR-GARCH model,and uses Bayesian method to analyze this model;we also use the China financial market data to check the detailed ability of quantile GJR-GARCH model in forecasting the value at risk.

关 键 词：GJR-GARCH模型 分位数回归 贝叶斯分析 风险度量 

分 类 号：F832.6[经济管理—金融学] F224