摘要
股指期货波动率建模与预测是揭示其波动运行规律和市场风险是重要途径。本文基于跳跃、好坏波动率与符号跳跃建立四组HAR模型,提出单级纠偏HARQ类模型和多级纠偏HARQF类模型,实证研究揭示股指期货波动运行规律,并采用MCS检验来评估模型优劣。HAR建模考察连续与跳跃波动、好与坏波动率的两种已实现波动分解。为了降低波动率估计偏差,基于最小化MSE准则确定最优抽样频率,利用已实现核修正的ADS检测法识别跳跃,采用已实现核估计修正好坏波动率与符号跳跃。基于沪深300股指期货的实证研究表明：连续波动比跳跃波动对未来已实现波动贡献更大;好坏波动率具有不对称波动冲击,而符号跳跃对未来波动具有负向冲击;好坏波动率分解优于连续与跳跃波动分解;中位数已实现四次幂差能够显著提升HAR类模型的样本内外预测能力;与样本内预测相反,样本外预测中单级纠偏HARQ类模型优于多级纠偏HARQF类模型;MCS检验得出HARQ-RV-SJ模型表现最佳。研究结论与启示对认识股指期货波动规律和市场风险具有意义。
Chinese stock index futures experienced an unusual bull and bear markets around 2015, but its volatility dynamic is a mystery for investors and regulators. Modeling and forecasting volatility is a feasible way to reveal volatility transmission process and track market risk. In this paper, 4 HAR-type models involving jumps, realized semivariances and signed jumps are established to forecast the realized volatility of CSI 300 index futures. Based on 4 basic HAR-type models, HARQ-type models and HARQF-type models are proposed by adding correction term of median realized quarticity （MedRQ）. During the modeling process, two decompositions of realized volatility including continuous and jump variances, upside and downside realized semivariances are considered. To reduce the robustness of market microstructure noise, the optimal sampling frequency for calculating realized volatilities is determined by the minimum MSE criterion, the statistic Z med of ADS jump test, realized semivariances and signed jump are revised based on realized kernel estimator. The newly MCS test is employed to evaluate the out-of-sample forecast performances. In-sample and out-of-sample analysis of forecast models are carried out on CSI 300 index futures, which shows important conclusions： 1）Most of the predictable variation in realized volatility stems from continuous volatility rather than jump variance, and future realized volatility is more related to historical downside semivariances （bad volatility） than upside semivariances （good volatility） ; 2） Good volatility and bad volatility exhibit asymmetric impact effect that good （bad） volatility generate negative （positive） impact on future realized vola- tility; 3）Decomposition of upside and downside realized semivariances outperforms that of continuous and jump variances; 4） MedRQ can significantly enhance the forecast ability of HAR-type models, HARQF models outperform HARQ models on in-sample performances, while HARQ models achieve better out-of-sample forecast
出处
《中国管理科学》
CSSCI
CSCD
北大核心
2018年第1期57-71,共15页
Chinese Journal of Management Science
基金
国家自然科学基金资助项目(71171065,71440006)
黑龙江博士后研究基金项目(2501050103)
