本文選題：量價混合信息GJRGARCH + 量價關(guān)系　； 參考：《西南財經(jīng)大學》2014年碩士論文

【摘要】：量價關(guān)系研究是金融學術(shù)和實務(wù)分析研究中備受關(guān)注的命題。研究表明,金融資產(chǎn)的價格與成交量存在顯著的統(tǒng)計相關(guān)性,而這種量價關(guān)系可以分為兩個層次,一是資產(chǎn)價格的絕對值變化與成交量存在正相關(guān)關(guān)系,二是資產(chǎn)價格的波動率與成交量存在正相關(guān)關(guān)系。同時,量價理論中混合信息分布理論、信息不對稱理論、意見分歧理論等細分理論用實證分析的方法從經(jīng)濟學、社會學、心理學的角度對市場表現(xiàn)出的量價關(guān)系進行了研究,開辟并驗證了許多理解市場表現(xiàn)的新角度和新方式。作為金融市場技術(shù)面分析的重要內(nèi)容,它關(guān)系到市場投資者對資本市場的理解和資產(chǎn)價格未來走勢的判斷,用價格輔以成交量表現(xiàn)對未來價格走勢進行判斷,進而幫助確定金融資產(chǎn)的買賣決策,對市場交易產(chǎn)生著直接的影響。 在險價值(Value at Risk)對于風險測度與監(jiān)控的重要性已在過去幾十年全球金融市場的發(fā)展過程中得到了驗證。伴隨著全球經(jīng)濟金融一體化,以及金融衍生產(chǎn)品市場的迅速發(fā)展,暴露或隱藏于資本市場的風險及其對應(yīng)的監(jiān)控方法成為監(jiān)管層、學術(shù)界和投資者日益關(guān)注的熱點。在險價值的概念和測度模型在此環(huán)境中產(chǎn)生且逐步實現(xiàn)了成熟發(fā)展�！栋腿麪枀f(xié)議一》的補充文件《資本協(xié)議關(guān)于市場風險的補充規(guī)定》將資本金在險價值VaR模型納為規(guī)范的測算模型方法,《巴塞爾協(xié)議二》則要求各銀行在構(gòu)建風控模型的基礎(chǔ)上進行壓力測試,以計算VaR用以監(jiān)測金融資產(chǎn)的風險水平。 GARCH族模型是刻畫具有時變方差性質(zhì)的金融時間序列的有效手段。在其有效刻畫金融時間序列的基礎(chǔ)之上,能運用具有不同特征的子模型有針對性地進行量價關(guān)系和在險價值等拓展領(lǐng)域的深入研究。例如,運用經(jīng)典的GJR-GARCH等非對稱性模型能有效反映出金融市場的杠桿效應(yīng),在各類GARCH族模型中根據(jù)不同的樣本特征也可以根據(jù)需要選擇出最佳的在險價值測度模型。 在閱讀了相關(guān)文獻的基礎(chǔ)上,本文把能反應(yīng)昨日收益率和成交量信息的量價混合信息虛擬變量引入到經(jīng)典的GJR-GARCH模型中,依據(jù)所得實驗模型的回歸結(jié)果判定模型擬合樣本序列的有效性。若實驗模型擬合有效,則一方面進一步用實驗模型對杠桿效應(yīng)進行更為細化的結(jié)構(gòu)分析,并結(jié)合經(jīng)典的量價理論對上證指數(shù)進行量價分析。另一方面對實驗模型進行在險價值的測度和檢驗實驗,通過與其他經(jīng)典模型測度在險價值的能力進行比較,考察實驗模型在風險度量方面的能力。本文的結(jié)構(gòu)和主要內(nèi)容如下。 首先,“緒論”章節(jié)對為何進行量價關(guān)系和在險價值的研究進行了選題背景和意義的說明,對GARCH族模型、量價關(guān)系理論和在險價值理論的相關(guān)文獻進行了梳理和歸納,并提出了如上一段所述的研究思路。 其次,“量價混合信息GJR-GARCH模型的構(gòu)建與運用”章節(jié)對各類GARCH族的基本形式和VaR的測度與檢驗方法進行了詳細回顧,并對GARCH族模型建模過程中事前檢驗和事后檢驗等建模步驟進行闡述,而后介紹了量價混合信息虛擬變量的構(gòu)造方式,以及量價混合信息GJR-GARCH模型的建模思想與基本形式。 再者,“樣本數(shù)據(jù)的搜集與預處理”章節(jié)對本文樣本的選擇及選擇的依據(jù)進行說明,介紹了樣本預處理的目的和方法,描述了樣本的統(tǒng)計特征,并對樣本的自相關(guān)、異方差性質(zhì)及樣本分布進行了識別,為下一步的實證分析做好準備。 最后,“實證結(jié)果與分析”章節(jié)記錄了量價混合信息GJR-GARCH模型的實證建模過程,比較了實驗模型和經(jīng)典GARCH族模型的擬合結(jié)果,對實驗模型擬合的有效性進行了判定。在此基礎(chǔ)上用實驗模型對杠桿效應(yīng)進行更為細化的結(jié)構(gòu)分析,進而結(jié)合經(jīng)典的量價理論對上證指數(shù)進行量價分析,并比較了實驗模型與經(jīng)典GARCH族模型的風險測度能力�，F(xiàn)將該章節(jié)得到的主要結(jié)果分為實驗模型擬合的相關(guān)結(jié)果與上證指數(shù)量價關(guān)系分析的相關(guān)結(jié)果兩部進行闡述。 一方面,通過實證分析,可得到的量價混合信息、GJR-GARCH模型擬合的相關(guān)結(jié)果包括如下幾方面。 1、量價混合信息GJR-GARCH模型在擬合效果上具有良好效果 首先,從該模型的參數(shù)顯著性檢驗、Box-Ljung檢驗、ARCH-LM檢驗、信號偏誤檢驗(sign bias test)和皮爾森卡方Goodness-of-Fit檢驗等事前和事后檢驗來看,量價混合信息GJRGARCH模型都有著良好的表現(xiàn)。Nyblom參數(shù)穩(wěn)定性檢驗所體現(xiàn)出的部分參數(shù)的穩(wěn)定性表現(xiàn)欠佳,但這是所擬合得的各類GARCH族模型的共同特征。 其次,GJR-GARCH模型的回歸參數(shù)符合模型符號的參數(shù)定義。量價混合信息GJR-GARCH模型中α+β之和在所有用于參照的GARCH族模型之中最小,說明量價混合信啟、GJR-GARCH模型對杠桿效應(yīng)的刻畫比其他參照模型能更有效地解釋部分短期波動信息的持續(xù)性影響。 最后,從信息準則統(tǒng)計量來看,量價混合信息虛擬變量GJR-GARCH模型的變量個數(shù)最多,但其AIC、BIC、SIC、HQIC等信息準則量是眾多模型之中的最小值(HQIC值為次小值),且這也從另一方面說明了該模型在擬合效果上的良好性質(zhì)。 2、量價混合信息GJR-GARCH模型能對杠桿效應(yīng)進行細化分析 從量價混合信息GJR-GARCH模型的回歸結(jié)果來看,并非所有昨日均值方程負殘差的出現(xiàn)均會使今日出現(xiàn)杠桿效應(yīng)。所有昨日負殘差相較非負殘差對應(yīng)的今日收益率的平均額外波動率之所以顯著,是由于昨日出現(xiàn)大額負向收益率和中等額度負向收益率時所引發(fā)的今日相對大幅的額外波動,拉升了所有昨日負向殘差對應(yīng)的今日平均額外波動的水平。因此,在運用和理解杠桿效應(yīng)模型時,應(yīng)當注意杠桿效應(yīng)是在平均意義上存在的。 3、基于量價混合信息GJR-GARCH模型和各類GARCH族模型進行的VaR測度和風險預測能力分析 首先,量價混合信息GJR-GARCH模型雖然在估計的似然值、信息準則和其他統(tǒng)計檢驗方面的表現(xiàn)均要優(yōu)于其他模型,但在風險測度方面并沒有因此而體現(xiàn)出獨到的優(yōu)越性,在某些分布假設(shè)的某些顯著性水平下甚至比部分模型的風險刻畫能力來得弱。 值得注意和思考的是,量價混合信息GJR-GARCH模型在t分布下估計的VR(Violation Rate)值普遍比其他模型都來得小,在正態(tài)分布的5%顯著性水平下的VR值也是所有模型中較小的,一方面體現(xiàn)出它對VaR較為大膽的估計和與之相應(yīng)而生的較強的風險警惕能力,另一方面小于1且偏離1較多的VR數(shù)值反應(yīng)出它過度估計風險、風險管理成本較高的性質(zhì)。 其次,不同分布假設(shè)與不同顯著性水平設(shè)定會對模型的在險價值測度效果產(chǎn)生影響。所用的樣本收益率序列在正態(tài)分布假定的5%顯著性水平下運用各類GARCH模型進行風險測度,均能取得良好的風險估計效果。而在student-t分布假定的5%顯著性水平下運用各類GARCH模型進行風險測度,幾乎不能取得準確的風險估計效果,所預測得的VaR值過度估計了風險。 當顯著性水平由5%變?yōu)?%時,正態(tài)分布假定下的各類GARCH模型所預測出的VaR值將大幅低估未來的風險,學生t分布假定下的各類GARCH模型高估了風險但部分模型能較為準確地對風險進行測度。 因此,在評價一個模型的風險測度能力是否良好時,應(yīng)當將分布與顯著性水平與其評價緊密結(jié)合,而不能簡單地以一種分布假定在一種特定的顯著性水平的風險測度結(jié)果作為衡量不同模型風險測度能力的唯一標準。 另一方面,基于量價混合信息GJR-GARCH模型的擬合結(jié)果,可得到的上證指數(shù)量價關(guān)系分析的相關(guān)結(jié)果如下所述。 當昨日市場出現(xiàn)大跌幅、中等跌幅表現(xiàn)時,所蘊含的價格信息平均來看會對今日收益率的額外波動產(chǎn)生顯著影響,且伴隨不同換手率的量能信息對今日收益率的額外波動會產(chǎn)生不同程度的影響。具體體現(xiàn)在昨日大跌幅大換手率、大跌幅小換手率、大跌幅中換手率、中等跌幅大換手率、中等跌幅小換手率、中等跌幅中等換手率的量價表現(xiàn)對應(yīng)的回歸系數(shù)均在1%的顯著性水平下拒絕了系數(shù)為零的原假設(shè)；當昨日市場出現(xiàn)小跌幅表現(xiàn)時,所蘊含的價格信息平均來看對今日收益率的額外波動所產(chǎn)生影響的顯著性水平不高或者不顯著,具體體現(xiàn)在昨日小跌幅大換手率、小跌幅中等換手率的量價表現(xiàn)對應(yīng)的回歸系數(shù)在10%的顯著性水平下顯著不為零,小跌幅小換手率的量價表現(xiàn)對應(yīng)的回歸系數(shù)不顯著。 不同量價信息會對今日的杠桿效應(yīng)產(chǎn)生不同程度的影響。大跌幅大換手率和中等跌幅大換手率的昨日信息比未出現(xiàn)所有標的量價信息時的平均波動率高出額外0.9個百分點左右；大跌幅小換手率和大跌幅中等換手率的昨日信息比未出現(xiàn)所有標的量價信息時的平均波動率高出額外0.7個百分點左右；小跌幅大換手率、中跌幅小換手率、中跌幅中換手率的昨日信息比未出現(xiàn)所有標的量價信息時的平均波動率高出額外0.4到0.5個百分點左右。小跌幅小換手率和小跌幅中等換手率的昨日信息比未出現(xiàn)所有標的量價信息時的平均波動率高出額外0.1到0.2個百分點,且回歸系數(shù)的顯著性不強。 可歸納出,昨日量價表現(xiàn)對今日收益率波動影響的兩個特征。一是昨日跌幅等級越高,昨日的量價信息、對今日收益率波動的貢獻度往往越大。二是昨日同級別的跌幅等級中,大換手率等級的信息量對今日收益率波動的貢獻度最高,且遠大于小換手率等級和中等換手率等級的信息量對今日收益率波動的貢獻度；而昨日小換手率的成交量信息在對應(yīng)大跌幅和中跌幅的價格信息時,所帶來的今日收益率額外波動均比中等換手率的成交量信息所帶來的額外波動要大,僅在小換手率小跌幅信息量出現(xiàn)時不會帶來顯著的額外波動。 運用量價理論結(jié)合實證分析可對上述量價關(guān)系特征做出經(jīng)濟意義上的解析。 綜上,本論文具有如下特點。首先,本文在經(jīng)典GJR-GARCH模型的基礎(chǔ)上引入量價混合信息虛擬變量進行擬合效果試驗,實驗模型的擬合結(jié)果在統(tǒng)計檢驗和統(tǒng)計信息量上均表現(xiàn)良好,說明實驗模型能對樣本進行有效擬合。且與參照族的模型相比,實驗模型在擬合效果上具有一定的優(yōu)越性。其次,本文在GJR-GARCH模型中引入量價混合信息虛擬變量,實現(xiàn)了對非對稱杠桿效應(yīng)更為細致的刻畫,加深了對杠桿效應(yīng)的理解。再者,本文能運用經(jīng)典的量價關(guān)系理論對實驗模型的實證結(jié)果進行解析,從中捕獲上證市場交易的量價關(guān)系特征,將統(tǒng)計建模知識與金融學理論相結(jié)合,豐富了實證分析的經(jīng)濟學內(nèi)涵。最后,本文在不同的顯著性水平和不同的分布條件下,對實驗模型和傳統(tǒng)模型的在險價值測度能力進行了比較,提出了基于實證結(jié)果的風險測度模型的評價標準。
[Abstract]:The study of the relationship between volume and price is a topic of concern in the study of financial academic and practical analysis. The study shows that there is a significant statistical correlation between the price of financial assets and the volume of trading, which can be divided into two levels, one is that the change of the absolute value of the asset price has a positive correlation with the volume of the transaction, and the two is the fluctuation of the asset price. There is a positive correlation between rate and volume. At the same time, the theory of mixed information distribution, information asymmetry theory, disagreement theory and other subdivision theories have studied the relationship between price and price from the perspective of economics, sociology and psychology in the theory of mixed information distribution, information asymmetry theory and Opinion Divergence theory, which opened and verified many understanding of market performance. As an important part of the technical analysis of the financial market, it relates to the market investor's understanding of the capital market and the judgment of the future trend of the asset price, the judgment of the future price trend with the volume of the price, and the determination of the future price trend, and then to help determine the decision of the sale of the financial assets, and direct the market transaction. Influence.
The importance of Value at Risk to risk measurement and monitoring has been verified in the development of global financial markets over the past few decades. With the global economic and financial integration and the rapid development of the financial derivatives market, the risk of exposure or hidden in the capital market and its corresponding monitoring methods have become a supervision. The concept and measurement model of the risk value produced and gradually achieved mature development in this environment. < the supplementary document of the Basel agreement > < the supplementary provisions on the market risk of the capital agreement > the method of calculating the capital in the VaR model of the value of the value of the risk, < Basel. Agreement two > requires banks to conduct stress tests on the basis of building wind control models to calculate the risk level of VaR to monitor financial assets.
The GARCH model is an effective means to describe the time series of time-varying variance properties. On the basis of its effective characterization of the financial time series, we can use the sub models with different characteristics to carry out the in-depth study of the quantity price relation and the expansion of the value of the risk value. For example, using the classical GJR-GARCH and other asymmetries. The sexual model can effectively reflect the leverage effect of the financial market. In the various GARCH models, the best value measurement model can be selected according to the different sample characteristics.
On the basis of reading related literature, this paper introduces the virtual variable of volume and price mixed information that can respond to yesterday's rate of return and volume information into the classic GJR-GARCH model. According to the regression results of the experimental model, the model fits the validity of the sample sequence. If the experimental model is fit, then the experiment is further used. The model makes a more detailed structural analysis on the leverage effect, and analyzes the quantity and price of the Shanghai stock index with the classic price theory. On the other hand, the experimental model is measured and tested on the risk value. The experimental model is compared with other classical models to measure the ability of the risk value, and the experimental model is examined in the risk measurement. Ability. The structure and main contents of this article are as follows.
First, the chapter of "Introduction" explains the background and significance of the research on the relationship between quantity and price and the value of the value of risk, combing and summarizing the GARCH model, the theory of quantity and price relation and the related literature of the value theory, and puts forward the research ideas as described in the previous paragraph.
Secondly, the chapter "construction and application of the GJR-GARCH model of volume and price mixed information" is a detailed review of the basic forms of various GARCH families and the measurement and inspection methods of VaR, and the modeling steps of the GARCH model modeling process, such as pre test and post inspection, and then the construction of the virtual variables of the mixed information of quantity and price is introduced. Method and the modeling idea and basic form of mixed information GJR-GARCH model.
Furthermore, the section of "sample data collection and preprocessing" explains the selection and selection of the sample, introduces the purpose and method of sample pretreatment, describes the statistical characteristics of the sample, and identifies the autocorrelation, heteroscedasticity and sample distribution of the sample, and is prepared for the next step of the empirical analysis.
Finally, the empirical results and analysis section records the empirical modeling process of the mixed information GJR-GARCH model of quantity and price, compares the experimental model and the classic GARCH model, and determines the validity of the experimental model fitting. On this basis, the experimental model is used to make a more detailed structural analysis of the bar effect. Combined with the classic price theory, the quantity and price of Shanghai stock index is analyzed, and the risk measurement ability of the experimental model and the classic GARCH model is compared. The main results obtained in this chapter are divided into two parts of the correlation results of the experimental model fitting and the relationship analysis of the Shanghai Stock index and price.
On the one hand, through empirical analysis, we can get mixed information of volume and price, and the relevant results of GJR-GARCH model fitting include the following aspects.
1, the mixed information GJR-GARCH model has a good effect on the fitting effect.
First, from the parameter significance test of the model, Box-Ljung test, ARCH-LM test, signal error test (sign bias test) and Pearson chi square Goodness-of-Fit test and other pre and post test, the volume and price mixed information GJRGARCH model has a good performance of the stability of the.Nyblom parameter stability test of the stability of some of the parameters of stability. Poor performance, but this is the common feature of the GARCH models that are fitted.
Secondly, the regression parameters of the GJR-GARCH model conforms to the parameter definition of the model symbol. The sum of alpha + beta in the mixed information GJR-GARCH model of the quantity and price is the smallest in all the GARCH models used for reference, indicating that the amount and price are mixed, the depiction of the leverage effect in the GJR-GARCH model is more effective than the other reference models to explain some of the short-term volatility information. The persistence effect.
Finally, according to the information standard statistics, the variable number of GJR-GARCH model of volume and price mixed information virtual variable is the most, but its AIC, BIC, SIC, HQIC and other information criteria are the minimum values of many models (HQIC value is the sub value), and this also illustrates the good properties of the model in the other aspect.
2, volume price mixed information GJR-GARCH model can refine the leverage effect.
From the regression results of the mixed information GJR-GARCH model, not all the negative residuals of yesterday's mean equation are all leveraged. The average extra volatility of all yesterday's negative residual difference compared to the non negative residual is significant, which is due to the large negative rate of return and the middle level of yesterday. The relatively large additional volatility caused by the degree of negative returns has raised the level of today's average extra fluctuation corresponding to the negative residual of yesterday. Therefore, when using and understanding the leverage effect model, it should be noted that the leverage effect exists on the average.
3, the VaR measure and risk prediction capability analysis based on the mixed price information GJR-GARCH model and all kinds of GARCH family models.
First, the mixed information GJR-GARCH model is superior to the other models in the estimated likelihood, information criteria and other statistical tests, but it does not reflect the unique superiority in the risk measurement. At some significant levels of some distribution assumptions, it is even better than the risk characterization of some models. The strength is weak.
It is worth noting and thinking that the VR (Violation Rate) value estimated by the mixed information GJR-GARCH model in the t distribution is generally smaller than that of the other models. The VR value under the 5% saliency level of the normal distribution is also the smaller of all the models. On the one hand, it embodies the stronger estimation of the VaR and the stronger wind corresponding to it. Risk alert ability, on the other hand, is less than 1 and deviates from 1 more VR values, reflecting its overestimation risk and higher risk management cost.
Secondly, different distribution assumptions and different levels of significant level setting will affect the value measurement effect of the model. The sample return sequence uses all kinds of GARCH models under the 5% saliency level of the normal distribution hypothesis to measure the risk, which can achieve a good risk estimation effect, while the Student-t distribution assumption is 5% significant. Using all kinds of GARCH models to measure risk at the level of sex, almost no accurate estimation of risk can be achieved. The predicted VaR value overestimates the risk.
When the significant level changes from 5% to 1%, the VaR values predicted by all kinds of GARCH models under the normal distribution assumption will significantly underestimate the risk of the future. The various GARCH models under the t distribution hypothesis of the students will overestimate the risk, but some of the models can measure the risk more accurately.
Therefore, when evaluating the risk measurement ability of a model, the distribution and significance level should be closely combined with its evaluation, and the risk measurement results of a specific level of risk can not be used as the only criterion to measure the risk measurement ability of different models.
On the other hand, based on the fitting results of the mixed GJR-GARCH model of volume and price, the related results of the Shanghai stock index volume and price relationship can be obtained as follows.
When there was a big drop in the market and a medium decline, the price information contained on average would have a significant impact on the extra volatility of today's rate of return, and the amount of energy information with different turnover rates would have different effects on the extra volatility of today's rate of return. The turnover rate, the turnover rate in the large decline, the medium drop large turnover rate, the medium drop small turnover rate, the medium decrease medium turnover rate, the corresponding regression coefficient at the 1% significant level all reject the original hypothesis that the coefficient is zero. The significant level of the impact of the rate of return volatility is not high or significant. It is embodied in yesterday's small turnover rate, and the corresponding regression coefficient of the small drop medium rate is not zero under the significant level of 10%, and the corresponding regression coefficient of the small exchange rate is not significant.
The information of different prices will have different degrees of influence on the leverage effect today. The information of yesterday's big and medium drop rate is about 0.9 percentage points higher than the average volatility of all the price information. The average volatility of all the marked price information is about 0.7 percentage points higher, and the small change rate, the small turnover rate, the exchange rate in the middle drop rate are 0.4 to 0.5 100 points higher than the average volatility when the price information is not presented. The average exchange rate of yesterday's information is 0.1 to 0.2 percentage points higher than the average volatility of all the standard volume of information, and the significance of the regression coefficient is not strong.
It can be concluded that yesterday's volume price performance has two characteristics on the impact of today's yield fluctuation. One is the higher the decline level of yesterday, the information of yesterday's volume and price, the greater the contribution to the fluctuation of the rate of return today. Two is the decline grade of yesterday's same level, the amount of information of the large turnover rate has the highest contribution to the fluctuation of the rate of return today. The contribution of the amount of information that is larger than the small turnover rate and the medium turnover rate to the fluctuation of the rate of return today; while the volume information of yesterday's small turnover rate is related to the price information of the large decline and the middle decline, and the additional volatility of today's rate of return is more than the medium turnover rate.

【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】：2014
【分類號】：F832.51

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