国际原油市场价格风险研究——基于股票市场的不对称溢出效应
Crude oil market price risk measurement study-asymmetric spillover effects based on stock markets
李明芳 1徐弋然 1赵鲁涛2
作者信息
- 1. 北京科技大学数理学院,北京 100083
- 2. 北京科技大学数理学院,北京 100083;北京理工大学管理与经济学院能源与环境政策研究中心,北京 100081
- 折叠
摘要
对国际原油市场价格风险进行精确度量,有利于规避风险,稳定经济.股票市场的不对称波动会对原油市场产生不同的溢出效应,因此对广义自回归条件异方差模型(GARCH)和门限GARCH模型(TGARCH)进行改进,分析股票市场对原油市场的不对称溢出效应并度量原油市场风险.首先,提出不对称波动提取方法用以确定4种波动.其次,建立时变波动GARCH模型(BVGA)和时变波动TGARCH模型(BVTGA),分析股票市场对原油市场的不对称溢出效应.最后,计算风险价值(VaR),度量原油市场风险.结果表明:原油市场波动具有长记忆性和杠杆效应,且外部市场对原油价格波动具有较强的持续性的影响.股票价格的缓慢波动会加剧原油价格波动,且上行影响大于下行影响.本研究可以为稳定原油市场和风险管理提供政策启示.
Abstract
A precise measure of risk in the crude oil market is beneficial for risk aversion and economic stabilization.Asymmetric fluctuations in the stock market have different spillover effects on the crude oil market,so we improve the generalized autoregressive conditional heteroskedasticity model(GARCH)and threshold GARCH model(TGARCH)to analyze the asymmetric spillover effects of the stock market on the crude oil market and to measure the crude oil market risk.First,an asymmetric volatility extraction method is proposed to identify four types of volatility.Second,a time-varying volatility GARCH model(BVGA)and a time-varying volatility TGARCH model(BVTGA)are developed to analyze the asymmetric spillover effects of the stock market on the crude oil market.Finally,the value-at-risk(VaR)is calculated to measure the crude oil market risk.The results show that crude oil market volatility has a long memory and leverage effect,and the external market has a strong and persistent influence on crude oil price volatility.Slow fluctuations in stock prices exacerbate crude oil price volatility,and the upside effect is greater than the downside effect.This study can provide policy implications for stabilizing the crude oil market and risk management.
关键词
原油收益/价格风险/股票市场/广义自回归条件异方差/不对称溢出/风险价值Key words
crude oil returns/price risk/stock market/generalized autoregressive conditional heteroskedasticity/asymmetric spillovers/value at risk引用本文复制引用
出版年
2024
NETL
NSTL
万方数据