本文選題：滬深300指數期貨　切入點：市場風險　出處：《東北財經大學》2012年碩士論文

【摘要】：我國于2010年4月16日正式推出滬深300指數期貨,彌補了我國金融期貨的缺失。自上市至今,其成交量、成交金額以及持倉量都逐年穩(wěn)步攀升,已逐漸發(fā)展成為我國金融市場最重要的金融衍生品之一。然而,投資金融衍生產品往往都會因衍生品自身存在的一定缺陷而具有較高的風險。因此,有效合理的度量其投資的市場風險就具有重要意義。 本文主要研究滬深300指數期貨投資的市場風險,大致分為四個部分。文章的第一部分主要介紹本文的選題背景和意義,闡述度量市場風險的國內外研究現狀,同時還簡要說明本文框架和創(chuàng)新點。第二部分簡單概述滬深300指數期貨和在險價值(VaR)的相關知識。第三部分依據投資者進行投資的目的不同,將投資者分為投機者、套期保值者和套利者。相比投機者和套期保值者,套利者的風險相對較小,而且情況較為復雜。因此,本文使用經典的VaR方法主要針對投機者和套期保值者面臨的價格波動風險和基差風險進行市場風險度量的實證研究。第四部分總結全文,得出本文的研究結論與不足,并展望未來研究。 在本文實證研究價格波動風險和基差風險的部分：對于投機者,本文利用滬深300期指當月連續(xù)合約日收益率序列研究其面臨的價格波動風險。通過分析可知：該序列分布表現出非正態(tài)性,呈現出尖峰、左偏等特征;‘序列為平穩(wěn)序列：序列自相關性統計不顯著；并且該序列沒有統計顯著的ARCH效應。鑒于此,本文采用蒙特卡洛(MC)模擬和歷史模擬(HS)法計算價格波動風險日VaR。實證結果表明：與歷史模擬相比,蒙特卡洛模擬能合理有效地計算滬深300期指合約價格波動風險日VaR。 對于進行套期保值的投資者,本文利用所選樣本期內基差的變化序列研究其主要面臨的基差風險。通過分析可知：該序列自相關性統計顯著;與正態(tài)分布相比,序列分布呈現出尖峰、右偏等特點；并且該序列存在統計顯著的ARCH效應。鑒于此,本文采用基于GED分布和t分布假設下的ARMA(2,1)-GARCH(1,1)模型計算基差風險日VaR。實證結果表明：與t分布假設相比,通過GED分布假設求得基差風險日VaR的效果更好。并且GED參數u=1.419928,說明GED分布能很好的描述分布的“厚尾”性質。通過利用GARCH模型對條件方差進行擬合,最終求得套期保值風險日VaR,并且該模型通過了有效性后驗檢驗,這表明通過此種方法計算基差風險日VaR是有效可行的。
[Abstract]:China officially launched Shanghai and Shenzhen 300 Index Futures on April 16, 2010, which makes up for the lack of financial futures in China.Since listing, its turnover, transaction amount and positions have risen steadily year by year, and it has gradually developed into one of the most important financial derivatives in our financial market.However, the investment financial derivatives often have higher risk because of their own defects.Therefore, it is of great significance to measure the market risk of its investment effectively and reasonably.This paper mainly studies the market risk of Shanghai and Shenzhen 300 index futures investment, which is divided into four parts.The first part of this paper mainly introduces the background and significance of this paper, describes the domestic and foreign research status of measuring market risk, and also briefly explains the framework and innovation of this paper.The second part is a brief overview of Shanghai and Shenzhen 300 index futures and risk value VaR) related knowledge.In the third part, investors are divided into speculators, hedgers and arbitrages.Compared with speculators and hedgers, arbitrage risk is relatively small, and the situation is more complex.Therefore, this paper mainly uses the classical VaR method to study the market risk measurement based on the price volatility risk and the base risk faced by speculators and hedgers.The fourth part summarizes the full text, draws the conclusion and deficiency of this paper, and looks forward to the future research.In this paper, the empirical study of the risk of price volatility and base risk: for speculators, this paper studies the risk of price volatility by using the daily yield series of the continuous contract of the Shanghai and Shenzhen 300 futures index in the same month.Through analysis, we can see that the distribution of the sequence is non-normal, showing peak, and the left deviation isometric 'sequence is a stationary sequence, the autocorrelation statistics of the sequence is not significant, and there is no statistically significant ARCH effect in the sequence.In view of this, this paper uses Monte Carlo Monte Carlo simulation and historical simulation to calculate the daily VaR of price volatility risk.The empirical results show that compared with historical simulation, Monte Carlo simulation can reasonably and effectively calculate the daily VaR of Shanghai and Shenzhen 300 futures index contract price volatility risk.For hedging investors, the risk of base difference is studied by using the variation sequence of basis in the selected sample period.Through the analysis, we can know that the autocorrelation of the sequence is significant, compared with the normal distribution, the sequence has the characteristics of peak and right deviation, and there is statistically significant ARCH effect in the sequence.In view of this, this paper uses the ARMA-2GARCH1) model based on the assumptions of GED distribution and t-distribution to calculate the risk day of base risk.The empirical results show that compared with the t-distribution hypothesis, the GED distribution assumption is more effective to obtain the base risk day VaR.And the GED parameter U1. 419928 shows that the GED distribution can well describe the "thick tail" property of the distribution.By fitting conditional variance with GARCH model, the hedging risk day VaR is finally obtained, and the model has passed the validity posteriori test, which shows that it is effective and feasible to calculate the base risk day VaR by this method.
【學位授予單位】：東北財經大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F224;F832.5

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