Forecasting the Chinese Gold Futures Market Volatility Using Markov-Switching Regime and Mixed Data Sampling Model
郭杨莉 1马锋1
扫码查看
点击上方二维码区域，可以放大扫码查看
作者信息
1. 西南交通大学经济管理学院,四川 成都 610031
折叠
摘要
利用马尔科夫机制转换(Markov-switching regime,MS)和混频数据(mixed data sampling,MIDAS)模型,构建新的马尔科夫机制混频模型(MS-MIDAS),并以此模型对中国黄金期货市场波动率建模和预测.运用样本外滚动时间窗(rolling time windows)预测技术和模型信度集(model confidence set,MCS)检验发现:(1)总体上,引入马尔科夫机制转换的混频模型(MS-MIDAS)相比于MIDAS模型本身,从统计上展现出更高的预测精度;(2)含有跳跃的马尔科夫机制混频模型(MS-MIDAS-CJ)的预测精度最高;(3)对不同的预测窗口和不同的滞后阶数(kmax),上述实证结果都是稳健的.
Abstract
In this study,a new Markov-switching regime(MS-MIDAS)is constructed using Markov-switching regime(MS)and mixed data sampling(MIDAS)models,and the volatility of the Chinese gold futures market is modeled and predicted.Using the out-of-sample rolling window prediction method and the Model Confidence Set(MCS)test,it is found that:(1)In general,higher prediction accuracy is demonstrated by the mixed data sampling models with Markov-switching regime(MS-MIDAS)compared to the MIDAS model;(2)The mixed data sampling model of Markov-switching regime with jumps(MS-MIDAS-CJ)exhibits the highest prediction accuracy;(3)The empirical results remain robust for different prediction windows and different lag orders(kmax).
维普
万方数据