本文關(guān)鍵詞：基于VaR目標函數(shù)下的多元GARCH動態(tài)套期保值比率模型研究　出處：《西南財經(jīng)大學》2013年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 動態(tài)套期保值比率 VaR模型 多元GARCH模型 SKST分布

【摘要】：我國在2010年4月16日推出了滬深300股指期貨。滬深300股指期貨是針對我國資本市場量身定做的一款金融衍生產(chǎn)品。對于國內(nèi)投資者,尤其是機構(gòu)投資者來說,滬深300股指期貨的推出為他們提供了一個合理有效的風險管理工具。因為通過多年的實踐經(jīng)驗來看,用股指期貨的套期保值可以有效的規(guī)避股票市場的風險。在套期保值的交易中,對于如何確定合理的套期保值比率是十分重要的,因為套期保值比率的不同將會帶來不同的套期保值效果。以往的研究大多數(shù)是集中在靜態(tài)套期保值模型的研究中,但本文主要是研究了動態(tài)套期保值模型。本文主要是在VaR為目標函數(shù)的框架下,運用多元GARCH模型來計算最優(yōu)套期保值比率從而達到動態(tài)套期保值的目的。 本文第一章主要探討了股指期貨動態(tài)套期保值的研究背景,研究目的及意義和國內(nèi)外相關(guān)的研究文獻綜述。通過對以往研究文獻的梳理總結(jié),筆者發(fā)現(xiàn),在以前對于股指期貨套期保值的分析中大部分的研究都是在方差最小目標函數(shù)下進行的。所應(yīng)用的模型都是一些基本的計量經(jīng)濟分析模型,這些模型的一大特點就是他們對于數(shù)據(jù)的要求極為苛刻,以至于很多分析數(shù)據(jù)很難達到。一旦不能滿足模型對數(shù)據(jù)的要求那么計算出的結(jié)果就可能產(chǎn)生一些系統(tǒng)性的偏差,為最終的結(jié)果埋下了隱患。但以VaR為目標函數(shù)的套期保值比率模型對數(shù)據(jù)的要求沒有那么嚴格,更加適合實際情況的需要,遲國泰等(2008)提出了基于VaR確定期貨最優(yōu)套期保值比率的原理,他們的研究發(fā)現(xiàn)當期貨合約的期望收益率為零,期貨和現(xiàn)貨的收益率完全相關(guān)或者VaR的置信水平接近于100%的情況下,基于VaR確定的期貨最優(yōu)套期保值比率無限接近于最小方差的最優(yōu)套期保值比率。在動態(tài)套期保值比率模型的研究中,核心問題就是如何用模型來擬合金融資產(chǎn)收益率,目前應(yīng)用最多的就是GARCH模型,本文準備在GARCH模型的基礎(chǔ)上通過改進,再引入多元GARCH的方法對模型數(shù)據(jù)進行擬合。 第二章探討了不同目標函數(shù)下的最優(yōu)套期保值比率模型,本文主要集中在研究方差最小化框架下的最優(yōu)套期保值比率模型和基于VaR模型下的最優(yōu)套期保值比率模型。研究發(fā)現(xiàn)VaR目標函數(shù)下的最優(yōu)套期保值比率和方差最小化套期保值比率有著完全不同的性質(zhì),方差最小化套期保值比率完全是針對風險厭惡的投資者的,VaR目標函數(shù)下的最優(yōu)套期保值比率還兼顧到了投資者在套期保值中投機需求,由于VaR目標函數(shù)下的最優(yōu)套期保值比率模型在一定的情況下可以轉(zhuǎn)化為方差最小化最優(yōu)套期保值比率模型,所以VaR目標函數(shù)下的最優(yōu)套期保值比率模型擁有更強的適用性。它主要引入了期貨期望收益率這個關(guān)鍵的指標,從最后的表達式可以看出,當期望收益率為正的時候,也就是當投資者預(yù)期股指期貨將要上漲的時候,這時期望收益率為正,算出的最優(yōu)套期保值比率將比經(jīng)典模型計算出的最優(yōu)套期保值比率小。這一現(xiàn)象在現(xiàn)實市場中是可以解釋的,當投資者預(yù)計市場將會上漲時,他們一般是處于這樣的一種情況,即投資者預(yù)計在未來的一段時間將有一筆現(xiàn)金收入,但投資者又希望現(xiàn)在入市,由于現(xiàn)在手上沒有現(xiàn)金,那么投資者就可以在期貨市場上先買入一定量的股指期貨,所購買的股指期貨的品種與投資者現(xiàn)金到達的時間應(yīng)該接近。相反,當投資者對未來股票市場的期望收益率預(yù)計為負時,也就是未來股票市場會下跌的時候,這時期望收益率為負,由表達式計算出的最優(yōu)套期保值比率將比前文中經(jīng)典模型計算出的最優(yōu)套期保值比率大。同樣在現(xiàn)實市場中也可以解釋這一現(xiàn)象,一般投資者在預(yù)計后市會下跌的時候,他們都會賣出自己的股票,但一些投資者擔心自己的判斷失誤,會猶豫是否賣出股票,尤其是當投資者持有的股票已經(jīng)存在一定收益的情況下,更難做出決定。 第三章主要就是實證分析了方差最小化模型和以VaR為目標函數(shù)的套期保值比率模型,從實證分析的結(jié)果來看,VaR模型計算出的最優(yōu)套期保值比率和最小方差條件下的最優(yōu)套期保值比率差距不是特別大,這可能是由于本文數(shù)據(jù)選擇的原因,因為滬深300股指期貨的收盤價格數(shù)據(jù)和滬深300現(xiàn)貨指數(shù)之間有著非常強的相關(guān)關(guān)系,而且他們之間的相關(guān)關(guān)系幾乎是呈線性的相關(guān),所以這幾種方法計算出的結(jié)果都沒有太大的差異,但是從本章最后的計算結(jié)果來看,基于VaR方法的最優(yōu)套期保值比率可以給套期保值者一個選擇的就會,如果投資者真的是風險厭惡者,那么VaR方法的最優(yōu)套期保值比率可以轉(zhuǎn)化為最小方差最優(yōu)套期保值比率,這樣就能讓投資者在規(guī)避風險的同時也能獲得一定的風險收益。在股指期貨的操作中,都會涉及到很大的金額,VaR方法還有一個特點就是能夠節(jié)省套期保值者的交易成本,因為在預(yù)期收益率為正的情況下,VaR方法計算的套期保值比率會小于最小方差套期保值比率,這樣就可以為套期保值者節(jié)省交易保證金的費用。 第四章主要是分析了靜態(tài)套期保值理論的不足、股指期貨動態(tài)套期保值的意義,最后介紹了一下股指期貨動態(tài)套期保值的理論以及本文所要用到的模型。首先、本文分析的套期保值比率模型通過第三章的實證分析,繼續(xù)選用以VaR為目標函數(shù)的套期保值比率模型來進行套期保值比率的計算。通過加入時間變量,就可以很容易的把套期保值比率的模型改為動態(tài)的模型。因為套期保值涉及到兩個變量,所以本文采用的是二元GARCH模型。因為滬深300股指期貨和滬深300指數(shù)的分布都不是正態(tài)分布。具有多數(shù)金融序列的尖峰厚尾特性,同時具有一定的偏度,在第三章和第四章的分析中,都是采用的正態(tài)分布來擬合收益率的分布,這顯然是不合適的,所以在本章中,將引入SKST分布,也就是Skewed Student這樣的非對稱分布來擬合數(shù)據(jù)。在對收益率波動的擬合中,本文選擇了DCC-ECM-BGARCH模型,用DCC-BGARCH模型的原因是DCC模型相對容易估計,而且由于DCC模型是直接針對變量的相關(guān)性進行研究,所以它能夠更好的符合現(xiàn)實中的真實相關(guān)性。在均值方程中引入ECM項的原因是在第三章的實證分析中發(fā)現(xiàn),滬深300股指期貨的對數(shù)收益率與滬深300現(xiàn)貨指數(shù)的對數(shù)收益率之間存在著協(xié)整關(guān)系,故而選擇DCC-ECM-BGARCH模型來擬合具有時變特征的收益率。 第五章實證分析了以VaR為目標函數(shù)的DCC-ECM-BGARCH模型的動態(tài)套期保值比率,動態(tài)套期保值比率計算的過程中出現(xiàn)了最優(yōu)套期保值比率大于1的情況。這在實際操作中是可以解釋的,在計算靜態(tài)套期保值比率的時候,都是取一段時間跨度的數(shù)據(jù)來進行計算,而且為了保證最優(yōu)套期保值比率的適用性,這個時間跨度的選取都比較長,從長期的分析來看,股指期貨的價格波動性是會大于現(xiàn)貨價格的波動性。但是在本章中,最優(yōu)套期保值比率都是根據(jù)時間而變化的,這就不能排除在一個小段的時間范圍內(nèi)現(xiàn)貨價格波動大于股指期貨價格波動的情況。在一般的情況下,投資者都覺得由于我國特有的交易模式,股票市場是T+1交易模式,而股指期貨市場是T+0的交易模式,那么自然股指期貨的波動性肯定會比股票市場的波動更加劇烈,故而最優(yōu)套期保值比率應(yīng)該小于1,但本文的計算結(jié)果中出現(xiàn)了套期保值比率大于1。其實也可以反過來看,正因為我國股指期貨市場能夠很快的對市場的信息作出反應(yīng),那么在現(xiàn)貨市場上的投資者很可能利用股指期貨所提供的信息在股票市場里投機,這樣一來就有可能出現(xiàn)在短時間內(nèi)現(xiàn)貨市場的波動還大于期貨市場的波動。所以總的來說,本文最后計算出的最優(yōu)套期保值比率大于1,是因為股指期貨具有價格發(fā)現(xiàn)的功能,能改善現(xiàn)貨市場對市場信息的反應(yīng)模式,在一段時間里會出現(xiàn)股指期貨的價格波動性小于現(xiàn)貨的波動。
[Abstract]:China launched the Shanghai and Shenzhen 300 stock index futures in April 16, 2010. Shanghai and Shenzhen 300 stock index futures in China's capital market is tailored to a financial derivative products for domestic investors, especially institutional investors, the Shanghai and Shenzhen 300 index futures to provide a reasonable and effective risk management tools for them. Because through years of experience see, the risk of stock index futures hedging can effectively avoid the stock market. In hedging transactions, is very important for how to determine reasonable hedge ratio, because of the different hedging ratios will bring different hedging effects. The majority of previous studies focused on static hedging model but in this paper is to study the dynamic hedging model. This paper is mainly in the VaR framework for the objective function, the use of multiple The GARCH model is used to calculate the optimal hedging ratio so as to achieve the purpose of dynamic hedging.
The first chapter of this paper mainly discusses the research background of the stock index futures dynamic hedging, research purpose and significance of domestic and international related research literature review. Based on the previous research literature review, the author found that, in the previous researches on the large part of the analysis of stock index futures hedging in are in the minimum variance under the objective function the application of the model. Are some of the basic econometric model, a major feature of these models is that they have very strict requirements for data analysis, so that a lot of data is difficult to achieve. Once can not meet the requirements of the data model so the calculated results may have some systematic deviations for the final the results of potential problems. But the VaR hedge ratio model of objective function requirement of data is not so strict, need to be more suitable for the actual situation, Chi Guotai (2008) put forward the principle of futures optimal hedging ratio determined based on VaR, they found that when the futures contract the expected rate of return to zero, futures and spot returns the confidence level completely related or VaR is close to 100% under the condition of optimal hedge ratio futures optimal hedging ratio is VaR based on the infinite close to the minimum variance. In the study of dynamic hedging ratio model, the key problem is how to use the model to fit the return of financial assets, is currently the most widely used GARCH model, this paper prepared by the improved model based on GARCH method is introduced to fit the multivariate GARCH model data.
The second chapter discusses the optimal hedge ratio model under different objective functions, the optimal hedge ratio model this paper mainly focuses on the variance minimization framework and optimal hedge ratio model based on VaR model. The study found that the VaR objective function under the optimal hedge ratio and the minimum variance hedge ratio has a completely different nature the minimum Variance Hedge ratio is for risk averse investors, the optimal hedging ratios of VaR under the objective function but also to take into account the investors in hedging and speculative demand, the optimal hedge ratio model of VaR under the objective function under certain conditions can be transformed into the optimal hedge ratio with minimum variance model. So the applicability of the optimal hedge ratio model VaR objective function more. It mainly introduced The futures expected rate of return of the key indicators, from the last expression can be seen when the expected rate of return is a time when investors expect when stock index futures will rise, the expected rate of return is positive, the optimal hedging ratios will calculate the optimal hedge ratio than the classical model to calculate the small. This phenomenon can be explained in the real market, when investors expect the market will rise, they are generally in such a situation, investors are expected to have a cash income in the next period of time, but also hope investors into the market now, because there is no cash on hand, so investors in the futures market before buying a certain amount of the stock index futures, investors buy cash and varieties of stock index futures should be close to the time of arrival. On the contrary, when investors in the future stock market The expected rate of return is expected to be negative, that is when the stock market will decline in the future, when the expected rate of return is negative, the optimal hedging ratios calculated by the expression of the optimal hedge ratio than the classical model previously calculated. Also in the real market can also explain this phenomenon, generally when investors in the market outlook is expected to fall, they will sell their stocks, but some investors worry that mistakes in their own judgments, will hesitate to sell the stock, especially when investors hold stocks have certain benefits under the condition of more difficult to make a decision.
The third chapter is the empirical analysis of the variance minimization model and using VaR as the target function of the hedge ratio model, from the empirical analysis results, the VaR model to calculate the optimal hedge ratio and the minimum variance under the condition of the optimal hedging ratio of the gap is not particularly large, this may be due to the data selection. Because there is a strong correlation between the Shanghai and Shenzhen 300 stock index futures price data and the CSI 300 stock index, and the relationship between them is almost linearly related, are not too big difference so that several methods to calculate the results, but from the end of this chapter the calculation results show that the optimal hedging the ratio of VaR method can give the hedgers a choice will be based on, if investors are risk averse, then the VaR method of optimal hedging ratio The rate can be transformed into the minimum variance optimal hedging ratio, which allows investors to avoid the risk of also can get some benefits. The risk in the stock index futures operation, will involve a great amount of VaR has a characteristic that is able to save the transaction cost of hedgers, because the expected rate of return as is the case, the VaR method to calculate the hedging ratio will be less than the minimum variance hedge ratio, so it can save the trading margin for hedging costs.
The fourth chapter mainly analyzes the disadvantages of static hedging theory, stock index futures dynamic hedging significance, finally introduced by the use of a stock index futures dynamic hedging theory and the model of this paper. Firstly, this paper analysis the hedging ratio model through the empirical analysis of the third chapter, continue to choose calculation with VaR hedging the ratio of objective function model of hedging ratio. By adding the time variable, you can easily put the hedge ratio model to a dynamic model. Because the hedging involves two variables, so this paper is two yuan GARCH model. Because the distribution of Shanghai and Shenzhen 300 stock index futures and the Shanghai and Shenzhen 300 index are not normal distribution. The peak thick tail has the characteristics of most financial series, but also has some skewness, in the analysis of the third chapter and the fourth chapter, are used The distribution of normal distribution to fit the yield, which is obviously not suitable, so in this chapter, the introduction of SKST distribution, which is Skewed Student this asymmetric distribution to fit the data. The fitting fluctuation on the rate of return, this paper chose the DCC-ECM-BGARCH model with DCC-BGARCH model is DCC the model is relatively easy to estimate, and the DCC model is studied for direct correlation of variables, so it can accord with the true correlation in reality better. The reasons for the introduction of ECM in the mean equation is found in the third chapter of the empirical analysis, cointegration relationship exists between the logarithm of rate of return and the Shanghai Shenzhen 300 stock index futures the 300 stock index return rate, choose the DCC-ECM-BGARCH model to fit the time-varying characteristics of the rate of return it.
The fifth chapter is the empirical analysis of the dynamic hedge ratio based on VaR DCC-ECM-BGARCH model of the objective function, the process of dynamic hedge ratio calculation in the optimal hedge ratio is greater than 1. This can be explained in the actual operation, when calculating the static hedging ratio, are taken for a period of time span the data to be calculated, and in order to ensure the applicability of the optimal hedging ratio, choose this time span is long, from a long-term perspective, the price volatility of the stock index futures volatility is greater than the spot price. But in this chapter, the optimal hedge ratio is changed according to time it cannot be ruled out, the spot price volatility is greater than the stock index futures price volatility in a short time range. In general, investors feel due to China's special Some trading patterns, the stock market is the T+1 transaction mode, and stock index futures market is T+0 trading model, then the volatility of stock index futures will naturally than the stock market fluctuation, therefore the optimal hedging ratio should be less than 1, but the results of this paper appeared in the hedge ratio is greater than 1. but can also turn look, just because of China's stock index futures market on the market quickly to respond, then on the spot market investors are likely to use stock index futures to provide information in the stock market speculation, so there may be a spot market in a short period of time is greater than the volatility of futures market volatility. In general, the optimal hedge ratio is calculated at the end of more than 1, because the stock index futures has price discovery function, can improve the stock market on the market channel In a period of time, the price volatility of stock index futures is less than the fluctuation of spot.

【學位授予單位】：西南財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F832.51;F224

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