中国股市多分形波动率建模及预测研究

Multifractal volatility forecast of Chinese stock market

本文考虑股市高频数据的日内效应和已实现波动率的测量误差修正了现有多分形波动率指标的构建方法,以HAR模型为基础构建新的多分形波动率预测模型.利用Diebold-Mariano检验和"模型信度设定"检验等方法综合评价了各种模型对我国沪深300股指的预测能力.结果表明:1)在相同模型范式下,赋权调整已实现波动率的样本外预测能力要优于已实现波动率,而本文提出的新的多分形波动率模型要显著优于其他模型;2)在相同波动率测度指标下,引入股价波动的跳跃成分和杠杆效应能进一步改善波动率模型的短期预测效果;3)最优和次优模型均是基于新多分形波动率方法构建的模型.

In this paper,we propose a modified multifractal volatility measure and construct multifractal volatility models based on HAR-type models including jumps and leverage effect.We apply Diebold-Mariano test and model confidence set test to compare the empirical performance of these models.The empirical results show that,1)Based on the same paradigm,weighted adjusted realized volatility is better than realized volatility and our new multifractal volatility outperforms the other methods.2)Based on the same volatility method,these models perform better when including jumps and leverage effect.3)By the comparison among models,LHAR-MVWA-CJ model and LHAR-MVWA model outperform the other models.