本文選題：尾部扭曲風險 + 極端風險　； 參考：《系統(tǒng)管理學報》2017年05期

【摘要】：使用隨機波動率模型修正滬深300股指期貨收益率序列的波動聚集效應,并在殘差服從正態(tài)分布和極值分布的假設下,分別計算了度量尾部風險的VaR、ES及尾部扭曲風險測度(TDRM)值。研究發(fā)現(xiàn):股指期貨日收益率序列呈現(xiàn)負偏、尖峰厚尾及波動聚集的形態(tài);使用隨機波動率模型可以較好地預測波動率的變化;假設殘差分布服從極值分布的模型結果優(yōu)于假設殘差分布服從正態(tài)分布的模型結果,說明極值模型在尾部分析上比正態(tài)分布更加適用;使用扭曲尾部風險測度估計尾部風險,通過扭曲函數(shù)的選取與風險厭惡系數(shù)的不同設定,調整尾部風險發(fā)生的概率,反映了投資者的主觀風險偏好,在相同置信水平下,得到的尾部風險估計值比VaR更精確。
[Abstract]:Using the stochastic volatility model to modify the volatility aggregation effect of Shanghai and Shenzhen 300 stock index futures return series, and under the assumption of normal distribution and extreme value distribution of residual service, the VaRNES and TDRM of tail risk are calculated respectively. It is found that the daily yield series of stock index futures show negative deviation, thick tail of peak and aggregation of volatility, and the random volatility model can better predict the change of volatility. The model result of assuming residual distribution from extreme value distribution is better than that of assuming residual distribution from normal distribution, which shows that extreme value model is more suitable for tail analysis than normal distribution, and use distorted tail risk measure to estimate tail risk. Through the selection of distortion function and the different setting of risk aversion coefficient, the probability of tail risk occurrence is adjusted to reflect the subjective risk preference of investors. Under the same confidence level, the estimated value of tail risk is more accurate than that of VaR.
【作者單位】： 大連理工大學管理與經濟學部;
【基金】：國家自然科學基金資助項目(71471026,71171032)
【分類號】：F224;F724.5

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本文編號：1843646