本文選題：CAViaR模型 + GARCH模型　； 參考：《系統(tǒng)工程》2017年03期

【摘要】：以我國期貨市場上交易最為活躍的滬深300股指期貨為例,分別采用CAViaR模型和GARCH模型對多頭VaR和空頭VaR進(jìn)行風(fēng)險建模,深入研究了股指期貨的收益分布特征和波動形態(tài)規(guī)律,并運用嚴(yán)謹(jǐn)?shù)暮鬁y檢驗的方法對比了各個模型的風(fēng)險預(yù)測精度。實證結(jié)果表明:(1)滬深300股指期貨具有明顯的"尖峰厚尾"現(xiàn)象,卻沒有顯著的有偏性和長記憶性;(2)基于杠桿效應(yīng)的GJR模型和兼具長記憶性和杠桿效應(yīng)的FIAPARCH模型并沒有表現(xiàn)出比傳統(tǒng)GARCH模型更高的預(yù)測精度,同時,先驗GED分布對金融收益分布特征的刻畫要優(yōu)于正態(tài)分布和SKST分布;(3)半?yún)?shù)法的CAViaR模型相比GARCH族模型表現(xiàn)出絕對優(yōu)異的預(yù)測能力�？傊�,CAViaR模型在股指期貨的風(fēng)險預(yù)測方面是相對更合理的模型選擇。
[Abstract]:Taking CSI 300 stock index futures, which is the most active traded stock index futures in futures market in China, as an example, this paper uses CAViaR model and GARCH model to model the risk of long VaR and short VaR, and deeply studies the distribution characteristics and fluctuation pattern of stock index futures.The risk prediction accuracy of each model is compared with the rigorous post-test test method.The empirical results show that the Shanghai and Shenzhen 300 stock index futures have obvious "peak and thick tail" phenomenon.However, there is no significant bias and long memory. The GJR model based on leverage and the FIAPARCH model with both long memory and leverage effect have no higher prediction accuracy than the traditional GARCH model.A priori GED distribution characterizes the characteristics of financial return distribution better than the CAViaR model based on normal distribution and SKST distribution. Compared with the GARCH family model, the CAViaR model shows absolutely superior predictive ability.In short, CAViaR model is a more reasonable model choice in the risk prediction of stock index futures.
【作者單位】： 復(fù)旦大學(xué)經(jīng)濟(jì)學(xué)院;
【分類號】：F224;F724.5

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本文編號：1733883