基于SV-SGED模型的动态VaR测度研究

中国管理科学 ›› 2013, Vol. 21 ›› Issue (6): 1-10.

• 论文 • 下一篇

基于SV-SGED模型的动态VaR测度研究

吴鑫育¹, 马宗刚², 汪寿阳³, 马超群²

1. 安徽财经大学金融学院, 安徽蚌埠 233030;
2. 湖南大学工商管理学院, 湖南长沙 410082;
3. 中国科学院数学与系统科学研究院, 北京 100190

收稿日期:2011-10-19 修回日期:2012-08-22 出版日期:2013-12-29 发布日期:2013-12-23
基金资助:
国家杰出青年科学基金资助项目（70825006）；教育部“长江学者和创新团队发展计划”项目（IRT0916）；国家自然科学基金创新研究群体科学基金项目（71221001）

Study on Dynamic VaR Measures Based on SV-SGED Model

WU Xin-yu¹, MA Zong-gang², WANG Shou-yang³, MA Chao-qun²

1. School of Finance, Anhui University of Finance & Economics, Bengbu 233030, China;
2. School of Business Administration, Hunan University, Changsha 410082, China;
3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Received:2011-10-19 Revised:2012-08-22 Online:2013-12-29 Published:2013-12-23

摘要/Abstract

摘要： 本文针对金融资产收益展现出“有偏”及“厚尾”分布特征，引入有偏广义误差分布（SGED）来描述资产收益，继而提出SV-SGED模型对资产收益波动率建模，并以此来测度动态风险值（VaR），进而采用后验测试技术对风险测度模型的精确性进行检验。同时，为了估计SV模型的参数，提出基于有效重要性抽样（EIS）技巧的极大似然（ML）估计方法。最后，给出了基于上证综合指数的实证研究。结果表明，SV-SGED模型比正态分布假定下的SV（SV-N）和广义误差分布假定下的SV（SV-GED）模型具有更好的波动率描述能力，SV-SGED模型展现出比SV-N和SV-GED模型更优越的风险测度能力。

关键词: VaR, SV模型, 有偏广义误差分布, 有效重要性抽样, 极大似然估计

Abstract: In this paper, skewed generalized error distribution (SGED) is introduced to account for skewed and heavy-tailed financial asset returns, and SV-SGED model is proposed to model asset return volatility, and then dynamic value-at-risk (VaR) can be measured. In order to test the accuracy of risk models, the back-testing technique is adopted. At the same time, a method for maximum likelihood (ML) estimation of SV models is introduced based on the efficient importance sampling (EIS) technique. Finally, an empirical study of Shanghai Stock Exchange composite index is presented. Empirical results demonstrate that the SV-SGED model can describe asset return volatility better than the SV model based on normal distribution (SV-N) and the SV model based on generalized error distribution (SV-GED), and the SV-SGED model can yield more accurate VaR estimates than the SV-N and SV-GED models.

Key words: VaR, SV models, skewed generalized error distribution, efficient importance sampling, maximum likelihood estimation

中图分类号:

F830.9

吴鑫育, 马宗刚, 汪寿阳, 马超群. 基于SV-SGED模型的动态VaR测度研究[J]. 中国管理科学, 2013, 21(6): 1-10.

WU Xin-yu, MA Zong-gang, WANG Shou-yang, MA Chao-qun. Study on Dynamic VaR Measures Based on SV-SGED Model[J]. Chinese Journal of Management Science, 2013, 21(6): 1-10.

参考文献

[1] Fama E. The behavior of stock market prices[J]. Journal of Business, 1965, 38: 34-105.
[2] Baillie R T, DeGennaro R P. Stock returns and volatility[J]. Journal of Financial and Quantitative Analysis, 1990, 25(2): 203-214.
[3] Kearns P, Pagan A. Estimating the density tail index for financial times series[J]. The Review of Economics and Statistics, 1997, 79: 171-175.
[4] Cont R. Empirical properties of asset returns: stylized facts and statistical issues[J]. Quantitative Finance, 2001, 1: 223-236.
[5] Angelidis T, Benos A, Degiannakis S. The use of GARCH models in VaR estimation[J]. Statistical Methodology, 2004, 1: 105-128.
[6] Huang Yuchuan, Lin B J. Value-at-Risk analysis for Taiwan stock index futures: fat tails and conditional asymmetries in return innovations[J]. Review of Quantitative Finance and Accounting, 2004, 22: 79-95.
[7] Cheong C W. Heavy-tailed value-at-risk analysis for Malaysian stock exchange[J]. Physica A, 2008, 387: 4285-4298.
[8] Brooks C, Persand G. The effect of asymmetries on stock index return Value-at-Risk estimates[J]. Journal of Risk Finance, 2003, 4: 29-42.
[9] Lee M C, Su J B, Liu H C. Value-at-risk in US stock indices with skewed generalized error distribution[J]. Applied Financial Economics Letters, 2008, 4: 425-431.
[10] So M K P, Yu P L H. Empirical analysis of GARCH models in value at risk estimation[J]. International Financial Markets, Institutions & Money, 2006, 16: 180-197.
[11] 余素红, 张世英, 宋军. 基于GARCH模型和SV模型的VaR比较[J].管理科学学报, 2004, 7(5): 61-66.
[12] 徐炜, 黄炎龙. GARCH模型与VaR的度量研究[J]. 数量经济技术经济研究, 2008, 25(1): 120-132.
[13] Beine M, Laurent S, Lecourt C. Accounting for conditional leptokurtosis and closing days effect in FIGARCH models of daily exchange rates[J]. Applied Financial Economics, 2002, 12(8): 589-600.
[14] Wu P T, Shieh S J. Value-at-risk analysis for long-term interest rate futures: fat-tail and long memory in return innovations[J]. Journal of Empirical Finance, 2007, 14(2): 248-259.
[15] 肖智, 傅肖肖, 钟波. 基于EVT-POT-FIGARCH的动态VaR风险测度[J]. 南开管理评论, 2008, 11(4): 100-104.
[16] 肖智, 傅肖肖, 钟波. 基于EVT-BM-FIGARCH的动态VaR风险测度[J]. 中国管理科学, 2008, 16(4): 18-23.
[17] 林宇, 卫贵武, 魏宇, 等.基于Skew-t-FIAPARCH的金融市场动态风险VaR测度研究[J]. 中国管理科学, 2009, 17(6): 17-24.
[18] 王吉培, 旷志平. 偏态t分布下FIGARCH模型的动态VaR计算[J]. 统计与信息论坛, 2009, 24(5): 75-79.
[19] Kim S, Shephard N, Chib S. Stochastic volatility: likelihood inference and comparison with ARCH models[J]. Review of Economic Studies, 1998, 65: 361-393.
[20] Yu Jun. Forecasting volatility in the New Zealand stock market[J]. Applied Financial Economics, 2002, 12: 193-202.
[21] Melino A, Turnbull S M. Pricing foreign currency options with stochastic volatility[J]. Journal of Econometrics, 1990, 45: 239-265.
[22] Harvey A C, Ruiz E, Shephard N. Multivariate stochastic variance models[J]. Review of Economic Studies, 1994, 61: 247-264.
[23] Jacquier E, Polson N G, Rossi P E. Bayesian analysis of stochastic volatility models[J]. Journal of Business and Economic Statistics, 1994, 12: 371-389.
[24] Jacquier E, Polson N G, Rossi P E. Bayesian analysis of stochastic volatility models with fat-tails and correlated errors[J]. Journal of Econometrics, 2004, 122: 185-212.
[25] 孙米强, 杨忠直, 余素红, 等. 基于随机波动模型的VaR的计算[J]. 管理系统工程, 2004, 18(1): 61-63.
[26] 蒋祥林, 王春峰. 基于贝叶斯原理的随机波动率模型分析及其应用[J]. 系统工程, 2005, 23(10): 22-28.
[27] Gallant A R, Hsieh D, Tauchen G. Estimation of stochastic volatility models with diagnostics[J]. Journal of Econometrics, 1997, 81(1): 159-192.
[28] Richard J F, Zhang Wei. Efficient high-dimensional importance sampling[J]. Journal of Econometrics, 2007, 127(2): 1385-1411.
[29] Liesenfeld R, Richard J F. Estimation of dynamic bivariate mixture models: comments on Watanabe (2000)[J]. Journal of Business & Economic Statistics, 2003, 21(4): 570-576.
[30] Theodossiou P. Skewed generalized error distribution of financial assets and option pricing[R]. Working Paper, Cyprus University of Technology, 2000.
[31] Kupiec P. Techniques for verifying the accuracy of risk measurement models[J]. Journal of Derivatives, 1995, 3(2): 73-84.
[32] Koopman S J, Shephard N, Creal D. Testing the assumptions behind importance sampling[J]. Journal of Econometrics, 2009, 149: 2-11.
[33] Gordon N J, Salmond D J, Smith A F. Novel approach to nonlinear/non-Gaussian Bayesian state estimation[J]. IEE Proceedings-F, 1993, 140: 107-113.

基于SV-SGED模型的动态VaR测度研究

Study on Dynamic VaR Measures Based on SV-SGED Model

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	陈杰,邢灵博,李胃胜,陈志祥,陈崇萍. 不完备质量下带有风险厌恶的库存决策模型[J]. 中国管理科学, 2024, 32(2): 54-64.
[2]	王江涛,蔡雅,郑承利. 一类自适应的风险预测算法及其应用[J]. 中国管理科学, 2023, 31(8): 1-8.
[3]	张鹏, 崔淑琳, 李璟欣. 一致性均值-CVaR可信性投资组合优化[J]. 中国管理科学, 2023, 31(6): 111-121.
[4]	刁姝杰, 匡海波, 朱佳钰, 孟斌. 基于混合CVaR的出口海陆仓质押率与物流服务努力水平联合决策研究[J]. 中国管理科学, 2023, 31(5): 93-103.
[5]	于辉, 李鑫. 零售商运营视角下投贷联动CVaR利率决策模型[J]. 中国管理科学, 2023, 31(2): 255-265.
[6]	叶五一,李艾琳,焦守坤. 基于动态模型平均的大豆期货市场风险溢出研究[J]. 中国管理科学, 2023, 31(12): 1-10.
[7]	欧阳资生, 杨希特, 黄颖. 嵌入网络舆情指数的中国金融机构系统性风险传染效应研究[J]. 中国管理科学, 2022, 30(4): 1-12.
[8]	张小婉, 易荣华, 俞莹, 王颖. 基于滚窗VAR的沪港通波动溢出效应测度[J]. 中国管理科学, 2022, 30(11): 42-51.
[9]	吴鑫育, 李心丹, 马超群. 基于期权与高频数据信息的VaR度量研究[J]. 中国管理科学, 2021, 29(8): 13-23.
[10]	蔡光辉, 项琳. 中国铜期货市场波动率估计与风险度量——基于广义已实现测度的Realized HAR GARCH模型[J]. 中国管理科学, 2021, 29(11): 1-12.
[11]	李成刚，李峰，赵光辉. 货币政策规则对国际资本流动与人民币汇率的时变影响——基于TVP-SV-VAR模型的实证检验[J]. 中国管理科学, 2021, 29(10): 35-46.
[12]	熊一鹏, 熊正德, 姚柱. 宏观压力测试下商业银行零售信贷产品PD模型预测研究[J]. 中国管理科学, 2020, 28(7): 13-22.
[13]	徐海峰, 王晓东. 现代服务业是否有助于推动城镇化？——基于产城融合视角的PVAR模型分析[J]. 中国管理科学, 2020, 28(4): 195-206.
[14]	崔玉泉, 刘冰洁, 刘聪, 曲晶晶. 新型订单农业合作模式的优化模型分析[J]. 中国管理科学, 2020, 28(12): 140-150.
[15]	胡成春, 陈迅. 经济政策不确定性、宏观经济与资产价格波动——基于TVAR模型及溢出指数的实证分析[J]. 中国管理科学, 2020, 28(11): 61-70.