基于时变高阶矩模型的贵金属市场风险测度研究  

Risk Measurements on Precious Metal Market Given the Context of Time-varying High Order Moments

作　　者：吕永健[1] 王鹏[1] 

机构地区：[1]西南财经大学中国金融研究中心,成都611130

出　　处：《管理科学》2015年第1期133-143,共11页Journal of Management Science

基　　金：国家自然科学基金(71101119;71473200);四川省教育厅创新团队建设项目(JBK130401)~~

摘　　要：以黄金为代表的贵金属及其金融衍生品的交易量不断增长,逐渐成为与股票和债券平行的投资和避险工具,但关于贵金属市场风险测度的研究却比较缺乏。以上海和伦敦市场的黄金和白银交易价格为样本,基于常数高阶矩模型和时变高阶矩模型建立风险测度模型,计算出不同模型的风险价值和预期损失;采用严谨的后验分析方法,在多头和空头两种头寸共10种分位数水平下对不同模型的风险测度精确性进行后验分析。研究结果表明,在测度风险价值时,时变高阶矩模型的风险测度精确性略优于常数高阶矩模型,带有杠杠效应的时变高阶矩模型优于不带杠杆效应的时变高阶矩模型;综合对比分析不同风险测度模型的后验分析结果可知,对于准确测度贵金属市场的风险,GJR-GARCHSK模型是一个相对合理的选择。In the previous study of VaR（ value at risk） , the risk measurements models mainly focus on the second order moments of returns distribution （variance） ,and GARCH models have been widely used in risk measurement researches. But in the frame- work of conventional GARCH family models, the time-varying three order moments （skewness） and the time-varying four order moments（kurtosis） are not included, so the GARCH model family belongs to the ＂constant higher order moments volatility mod- el＂. However, the distribution of financial asset return does not obey standard normal distribution, but is Leptokurtic and fat tailed distribution. More and more researches have begun to explore the role of the third order moments of the return distribution （skewness） and fourth order moments of the return distribution （kurtosis） in risk management, asset pricing and option pricing, where especially in the area of risk management, the influence of third order moments and fourth order moments are very intui- tive. For example, if a portfolio returns distribution has a larger kurtosis value, the probability of extreme loss occurs will be lar- So if we set kurtosis coefficient as constant in the risk measurement models, the models will underestimate the impact of ex- treme events, and the asset management based on that risk measurement models may be facing a great loss in case of an acci- dent. Therefore, the time-varying higher order moments is considered to be as an alternative for an accurate and reliable risk measurement method. The paper takes the spot price of gold and silver of Shanghai and London market as a sample. Since the trading volume of precious metals and their derivatives is continuously growing, the precious metals are gradually parallel with stock and bond as financial investment and hedging tools. However, the researches done on risk measurements of precious metals market have been relatively less. Hence, this paper uses both ＂constant higher order moments volatility model＂ （ GARCH m

关 键 词：贵金属市场 风险价值 预期损失 时变高阶矩 后验分析 

分 类 号：F830.9[经济管理—金融学]