GAS-SKST-F模型及其在高频多元波动率预测中的应用

doi:10.16381/j.cnki.issn1003-207x.2019.1105

Abstract

Abstract: Modeling conditional dependency structure of financial assets through time-varying covariance is of great significance to capital risk management, option pricing and optimal portfolio. With the advent of high-frequency data, realized multivariate volatility model is widely applied in the financial field in recent years. Among the observation-driven models for time-varying parameters, the clear advantage of the generalized autoregressive score (GAS) model is that it exploits the complete density structure and likelihood information rather than theconditional moments information.

Key words: GAS-SKST-F model, multivariate skewed student t density, multivariate volatility

CLC Number:

F830.9

LU Wan-bo, KANG Jing-hao. GAS-SKST-F Model and Its Application in High Frequency Multivariate Volatility Forecast[J]. Chinese Journal of Management Science, 2022, 30(1): 77-87.

References

［1］ Bauwens L, Laurent S, Rombouts J V K. Multivariate GARCH models: A survey［J］. Journal of Applied Econometrics,2006,21(1):79-109.
［2］ Andersen T G, Bollerslev T, Diebold F X, et al. Modeling and forecasting realized volatility［J］. Econometrica,2003,71(5): 579-625.
［3］ Engle R F, Gallo G M. A multiple indicators model for volatility using intra-daily data［J］. Journal of Econometrics,2006,131(3):3-27.
［4］ Shephard N, Sheppard K. Realising the future: forecasting with high-frequency-based volatility (HEAVY) models［J］. Journal of Applied Econometrics,2010,25(2):197-231.
［5］ Hansen P R, Huang Z, Shek H H. Realized GARCH: A joint model of returns and realized measures of volatility［J］. Journal of Applied Econometrics,2012,27(6):877-906.
［6］马锋,魏宇,黄登仕. 基于符号收益和跳跃变差的高频波动率模型［J］. 管理科学学报, 2017, 20(10):36-48.Ma Feng, Wei Yu, Huang Dengshi. Forecasting the realized volatility based on the signed return and signed jump variation［J］. Journal of Management Sciences in China,2017, 20(10):36-48.
［7］吴鑫育,李心丹,马超群. 门限已实现随机波动率模型及其实证研究［J］. 中国管理科学, 2017, 25(3): 10-19.Wu Xinyu, Li Xindan, Ma Chaoqun. Threshold realized stochastic volatility model and its empirical test［J］. Chinese Journal of Management Science,2017,25(3):10-19.
［8］ 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.
［9］ Paolo G, Hansen P R, Janus P, et al.Realized Wishart-GARCH: A score-driven multi-asset volatility model［J］. Journal of Financial Econometrics, 2019, 17(1):1-32.
［10］ Opschoor A, Janus P, Lucas A, et al. New HEAVY models for fat-tailed realized covariances and returns［J］. Journal of Business and Economic Statistics, 2018,36(4): 643-657.
［11］罗嘉雯, 陈浪南.多国股票市场的高频波动相关性研究［J］. 中国管理科学, 2018, 26(2):116-125.Luo Jiawen, Chen Langnan. The volatility co-movement of various stock markets based on high-frequency data［J］.Chinese Journal of Management Science,2018,26(2):116-125.
［12］ Creal D, Koopman S J, André Lucas. Generalized autoregressive score models with applications［J］. Journal of Applied Econometrics, 2013, 28(5):777-795.
［13］ Blasques F, Koopman S J, LucasA. Information theoretic optimality of observation driven time series models for continuous responses［J］. Biometrika, 2015, 102(2):325-343.
［14］ Lucas A, Schwaab B, Zhang X. Conditional Euro area sovereign default risk［J］. Journal of Business and Economic Statistics, 2014, 32(2):271-284.
［15］ Creal D, Schwaab B, Koopman S, et al. Observation driven mixed measurement dynamic factor models with an application to credit risk［J］. Review of Economics and Statistics, 2014,96(5):898-915.
［16］王天一,黄卓.Realized GAS-GARCH及其在VaR预测中的应用［J］. 管理科学学报，2015, 18(5):79-86.Wang Tianyi, Huang Zhuo. Realized GAS-GARCH model and its application in Value-at-Risk forecast［J］. Journal of Management Sciences in China,2015, 18(5):79-86.
［17］ Oh D H, Patton A J. Time-varying systemic risk:Evidence from a dynamic copula model of CDS spreads［J］. Journal of Business andEconomic Statistics, 2018, 36(2):181-195.
［18］沈根祥, 邹欣悦.已实现波动GAS-HEAVY模型及其实证研究［J］. 中国管理科学, 2019, 27(1):1-10.Shen Genxiang, Zou Xinyue. GAS-HEAVY model for realized measures of volatility and returns［J］. Chinese Journal of Management Science, 2019, 27(1):1-10.
［19］ Lambert P, Laurent S. Modelling financial time series using GARCH-type models with a skewed student distribution for the innovations［R］. Working Paper,Université de Liège, 2001.
［20］ Gao Chunting, Zhou Xiaohua. Forecasting VaR and ES using dynamic conditional score models and skew student distribution ［J］. Economic Modelling, 2016, 53:216-223.
［21］ Bauwens L, Laurent, S. A new class of multivariate skew densities with application to generalized autoregressive conditional heteroscedasticity models［J］.Journal of Business and Economic Statistics, 2005,23(3):346-354.
［22］ Blasques F, Koopman S J, Lucas A. Maximum likelihood estimation for score-driven models［R］. Working Paper, Tinbergen Institute, 2017.

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GAS-SKST-F Model and Its Application in High Frequency Multivariate Volatility Forecast

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