摘要
本文首次将百度指数引入HAR波动建模框架，基于跳跃、好坏波动率与百度指数提出HAR改进模型，实证研究揭示股指期货波动运行规律，并通过MCS检验分析预测模型优劣．HAR建模考察连续一跳跃波动、好一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，基于序列相关法仿真统计最优抽样频率，利用已实现核修正的ADS检测识别跳跃，进一步修正好坏波动率与符号跳跃．基于沪深300股指期货的样本内外预测表明：连续波动比跳跃波动对未来已实现波动的预测贡献更大；好坏波动率具有不对称的波动冲击，好（坏）波动率抑制（加剧）未来波动性；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；百度指数能显著提升HAR波动建模的样本内外预测能力；MCS检验证实，考虑符号跳跃与百度指数的HAR—RV—SJ—BI模型表现最佳．研究结论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. In this paper, we establish 4 HAR-type models involving jumps, realized semivariances, signed jumps and Baidu Index, to forecast the realized volatility of CSI 300 index futures. Based on the framework of HAR modeling, four novel HAR-type models are proposed by adding Baldu Index as independent variable. 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, optimal sampling frequency for calculating realized volatilities is determined by sequential correlation approach, the statistic Zmed 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 volatility; 3） Decomposition of upside and downside realized semivariances outperforms that of continuous and jump variances; 4） Baidu index can significantly improve the forecasting performances of HAR-type models both in-sample and out-of-sample testing; 5） Signed jumps bear valuable information of both market volatility and directions, and HAR-RV-SJ-BI is the best model among all forecast models specified in our paper. Our findings have import implications for investors and polieymakers of Chinese stock index futures.
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2018年第2期299-316,共18页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(71171065,71440006)~~
