我国股指期货与现货市场的波动溢出效应研究——基于HAR-CAW模型

doi:10.12005/orms.2018.0023

运筹与管理 ›› 2018, Vol. 27 ›› Issue (1): 153-159.DOI: 10.12005/orms.2018.0023

我国股指期货与现货市场的波动溢出效应研究——基于HAR-CAW模型

赵树然^1,2, 袁东¹, 任培民³

1.中国海洋大学经济学院,山东青岛 266100;
2.教育部人文社会科学重点研究基地中国海洋大学海洋发展研究院,山东青岛 266100;
3.青岛大学经济学院金融系,山东青岛 266071

收稿日期:2015-11-05 出版日期:2018-01-25
作者简介:赵树然(1978-),女,湖南人,教授,博士。研究方向为金融计量与金融风险管理。
基金资助:
山东省自然科学基金(ZR2017MG005);国家自科基金 (71201147);国家社科基金重大项目(15ZDB171);山东省社科基金(17CJRJ07)

Volatility Spillover Effects between Our Country’s Index Futures and Spot Market ——Based on HAR-CAW Model

ZHAO Shu-ran^1,2, YUAN Dong¹, REN Pei-min³

1.School of Economics, Ocean University of China, Qingdao 266100, China;
2.Institute of Ocean Development at Ocean University of China, Qingdao266100, China;
3.School of Economics,Qingdao University, Qingdao 266071, China

Received:2015-11-05 Online:2018-01-25

摘要/Abstract

摘要： 沪深300股指期货与现货波动溢出问题的研究对于风险管理具有重要的理论和现实意义。本文旨在基于高频数据,利用异质金融市场驱动的HAR-CAW模型研究我国股指期货和现货市场之间及其自身的短期、中期和长期波动溢出问题。研究结果表明,沪深300股指期货与现货市场之间整体上存在着双向波动溢出效应,但是溢出效应不对称,期货对现货的溢出效应占主导地位;在相互间各期溢出研究上,两市场间的各期溢出表现各不相同;在自身溢出效应上,各期整体而言现货市场存在溢出,而期货市场不存在。

关键词: 波动溢出, 股指期货, 高频数据 HAR-CAW模型

Abstract: Volatility spillover effects between Shanghai and Shenzhen 300 index futures and spot market is of great significance for investors of risk management. This article is based on high frequency data, using heterogeneous financial market driven HAR-CAW model to study short-term, medium-term and long-term volatility spillovers between future market and spot market and of their own. The results show that there are two-way volatility spillover effects, but the effects are not asymmetric: the futures market dominates the volatility spillovers. On the study of two-way volatility spillover effects, the two markets have different performances during short-term, medium-term and long-term; on the study of its own spillover effects, overall speaking, volatility spillovers exist in each phase of the spot market, but not in the futures market.

Key words: volatility spillovers, index futures, high-frequency data, HAR-CAW model

中图分类号:

F832.5

赵树然, 袁东, 任培民. 我国股指期货与现货市场的波动溢出效应研究——基于HAR-CAW模型[J]. 运筹与管理, 2018, 27(1): 153-159.

ZHAO Shu-ran, YUAN Dong, REN Pei-min. Volatility Spillover Effects between Our Country’s Index Futures and Spot Market ——Based on HAR-CAW Model[J]. Operations Research and Management Science, 2018, 27(1): 153-159.

参考文献

[1] Gannon G. Simultaneous volatility transmissions and spillover effects: US and Hong Kong stock and futures markets[J]. International Review of Financial Analysis, 2005, 14(3): 326-336.
[2] Zhou Z, Dong H, Wang S. Intraday volatility spillovers between index futures and spot market: evidence from China[J]. Procedia Computer Science, 2014, 31: 721-730.
[3] Antonakakis N, Floros C, Kizys R. Dynamic spillover effects in futures markets: UK and US evidence[J]. International Review of Financial Analysis, doi:10.1016/j.irfa.2015.03.008.
[4] Corredor P, Ferrer E, Santamaria R. Sentiment-prone investors and volatility dynamics between spot and futures markets[J]. International Review of Economics & Finance, 2015, 35(C): 180-196.
[5] 左浩苗,刘振涛,曾海为.基于高频数据的股指期货与现货市场波动溢出和信息传导研究[J].金融研究,2012,4:140-155.
[6] Rittler D. Price discovery and volatility spillovers in the european union emissions trading scheme: a high-frequency analysis[J]. Journal of Banking & Finance, 2012, 36(3): 774-785.
[7] Kang S H, Cheong C, Yoon S M. Intraday volatility spillovers between spot and futures indices: evidence from the korean stock market[J]. Physica A Statistical Mechanics & Its Applications, 2013, 392(8): 1795-1802.
[8] 严敏,巴曙松,吴博.我国股指期货市场的价格发现与波动溢出效应[J].系统工程,2009,10:32-38.
[9] 柴尚蕾,郭崇慧,苏木亚.基于ICA模型的国际股指期货及股票市场对我国股市波动溢出研究[J].中国管理科学,2011,19(3):11-18.
[10] 项歌德,沈开艳.股指期货市场的价格发现与风险波动溢出效应实证研究——以中国沪深300股指期货市场为例[J].上海金融,2012,6:67-73.
[11] 刘晓彬,李瑜,罗洎.股指期货与现货市场间波动溢出效应探究[J].宏观经济研究,2012,7:80-86.
[12] 刘庆富,华仁海.中国股指期货与股票现货市场之间的风险传递效应研究[J].统计研究,2011,28(11):84-90.
[13] Zhang L. Estimating covariation: epps effect, microstructure noise[J]. Journal of Econometrics, 2011, 160(1): 33-47.
[14] Barndorff-Nielsen O F, Hansenc P R, Lunde A, et al. Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading[J]. Journal of Econometrics, 2011, 162: 149-169.
[15] Christensen K, Kinnebrock S, Podolskij M. Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data[J]. Social Science Electronic Publishing, 2010, 159(1): 116-133.
[16] 赵树然,姜亚萍,任培民.高频波动率矩阵估计的比较分析——基于有噪非同步的金融数据[J].中国管理科学,2015,23(10):19-29.
[17] Golosnoy V, Gribisch B, Liesenfeld R. The conditional autoregressive wishart model for multivariate stock market volatility[J]. Journal of Econometrics, 2012, 167(1): 211-223.
[18] Corsi F. A simple approximate long-memory model of realized-volatility[J]. Journal of Financial Econometrics, 2009, 7: 174-196.

我国股指期货与现货市场的波动溢出效应研究——基于HAR-CAW模型

Volatility Spillover Effects between Our Country’s Index Futures and Spot Market ——Based on HAR-CAW Model

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