本文選題：乘積誤差模型 + 非對稱性　； 參考：《西南財經(jīng)大學(xué)》2014年碩士論文

【摘要】：自從乘積誤差模型(MEM)提出后,國內(nèi)外涌現(xiàn)了許多對MEM模型的研究,國內(nèi)主要是運(yùn)用MEM模型結(jié)合中國金融市場進(jìn)行實證研究,關(guān)于非對稱MEM模型的研究較少。事實上,在金融市場上的非負(fù)值時間序列大多都存在非對稱性,我國金融市場也不例外,比如波動的不對稱性、金融持續(xù)時間的非對稱性等。本文基于MEM模型的基本理論,借鑒非對稱ACD模型的思想和結(jié)構(gòu),探討非對稱MEM模型,將非對稱結(jié)構(gòu)納入一般MEM里面,構(gòu)建非對稱MEM模型,并用極大似然法對經(jīng)典乘積誤差模型與非對稱乘積誤差進(jìn)行估計,利用蒙特卡洛模擬比較這兩種模型對具有非對稱性的非負(fù)值時間序列的刻畫能力。最后結(jié)合我國金融市場進(jìn)行實證分析。這是對乘積誤差模型理論的深入研究,彌補(bǔ)MEM模型對稱性的缺陷,同時為非負(fù)值時間序列研究提供了一個有力的工具。 本文主要研究工作： (1)根據(jù)經(jīng)典的MEM模型,結(jié)合非對稱GARCH模型和非對稱ACD模型的構(gòu)建思想,探索非對稱MEM模型結(jié)構(gòu),建立非對稱MEM模型,并對該模型誤差項分布的選取以及模型參數(shù)估計進(jìn)行介紹。 (2)為了比較經(jīng)典MEM模型與非對稱MEM模型對具有非對稱效應(yīng)的金融時間序列的刻畫能力,本文運(yùn)用蒙特卡洛模擬方法按照一定的數(shù)據(jù)生成過程,隨機(jī)生成具有非對稱性的非負(fù)值時間序列,然后通過經(jīng)典MEM模型與非對稱MEM模型的估計結(jié)果,根據(jù)預(yù)測能力指標(biāo)比較兩種模型對具有非對稱性的非負(fù)值時間序列的刻畫能力,結(jié)果發(fā)現(xiàn)非對稱MEM模型能更好的刻畫非負(fù)值時間序列。 (3)金融高頻數(shù)據(jù)包括價格持續(xù)期、交易持續(xù)期、最高價、最低價等金融時間序列都具有典型性特征,比如高峰厚尾、自相關(guān)和長記憶性以及日內(nèi)效應(yīng)等基本特征,本文選取招商銀行成交量、最高價、最低價等時間序列作為研究對象,驗證招商銀行的高頻數(shù)據(jù)是否具有這些典型性特征,分析結(jié)果表明成交量、最高價、最低價都具有高峰厚尾、自相關(guān)和長記憶性以及日內(nèi)效應(yīng)等典型性特征。 (4)本文選取招商銀行2014年年初的每隔5分鐘分時的高頻交易數(shù)據(jù),選取成交量、最高價、最低價等時間序列進(jìn)行研究,首先根據(jù)高頻數(shù)據(jù)的典型特征,對研究對象進(jìn)行數(shù)據(jù)處理,對成交量消除日內(nèi)效應(yīng),通過最高最低價格的變化計算出價格指示變量等,然后建立關(guān)于交易強(qiáng)度的非對稱MEM模型,目的是為了刻畫價格變化對交易強(qiáng)度的非對稱影響,運(yùn)用極大似然估計對非對稱MEM模型進(jìn)行估計。實證分析結(jié)果表明：從交易強(qiáng)度的動態(tài)運(yùn)動過程可得出,交易強(qiáng)度有較強(qiáng)的集聚特征,從模型結(jié)果發(fā)現(xiàn),無論是價格正向變化還是負(fù)向變化,都會使交易強(qiáng)度增加,即價格波動會促進(jìn)市場交易,但價格變化方向?qū)灰讖?qiáng)度的影響程度不同,說明交易強(qiáng)度具有明顯的非對稱性。 本文創(chuàng)新之處：通過對非對稱MEM模型的研究,一方面是對非對稱GARCH模型、非對稱ACD模型的拓展,為研究證券市場非負(fù)值時間序列提供一個更有力的研究工具,同時豐富了MEM模型。另一方面我們將對我國金融市場中的交易強(qiáng)度的非對稱性進(jìn)行實證研究,國內(nèi)對交易強(qiáng)度的研究較少,主要是研究市場因素對交易強(qiáng)度的長短期影響,因此,本文的實證研究對于了解交易制度和市場結(jié)構(gòu)對投資者和市場交易活動的影響,對于完善我國證券市場的監(jiān)管具有重要的實際應(yīng)用價值。 本文由2011年度國家自然科學(xué)基金青年科學(xué)基金項目《新興訂單驅(qū)動市場非負(fù)值金融時間序列的乘積誤差建模及應(yīng)用研究》(71101118)資助完成。
[Abstract]:Since the product error model (MEM) is proposed, many researches on MEM models have emerged at home and abroad. The domestic research is mainly about the use of the MEM model and the Chinese financial market, and the research on asymmetric MEM model is less. In fact, most of the non negative time series in the financial market are asymmetric, and our financial market is also the same. No exception, such as the asymmetry of volatility and the asymmetry of financial duration. Based on the basic theory of the MEM model and using the ideas and structures of the asymmetric ACD model, the asymmetric MEM model is discussed, the asymmetric structure is incorporated into the general MEM, and the asymmetric MEM model is constructed, and the classical product error model and the nonsymmetric model are used by the maximum likelihood method. The estimation of the symmetric product error is made by using Monte Carlo simulation to compare the characterization of the two models for non negative time series with non negative values. Finally, an empirical analysis is made with the financial market in China. This is an in-depth study of the theory of the product error model, which makes up for the defects of the symmetry of the MEM model and is a non negative time series. Research provides a powerful tool.
The main research work in this paper is:
(1) according to the classical MEM model, combining asymmetric GARCH model and asymmetric ACD model, the structure of asymmetric MEM model is explored and asymmetric MEM model is established. The selection of error term distribution and parameter estimation of the model are introduced.
(2) in order to compare the characterizations of the classical MEM model and the asymmetric MEM model for the financial time series with asymmetric effects, the Monte Carlo simulation method is used to generate the non negative time series with non negative values according to a certain data generation process, and then the estimation knot of the classical MEM model and the asymmetric MEM model is obtained. According to the prediction ability index, the two models are used to characterize the non negative time series with non negative values. The results show that the non negative time series can be depicted better by the asymmetric MEM model.
(3) the financial high frequency data including the price duration, the duration of the transaction, the highest price, the lowest price and other financial time series all have the typical characteristics, such as the basic characteristics of the high peak, the autocorrelation and the long memory and the day effect. This paper selects the time series of the volume, the highest price and the lowest price of China Merchants Bank as the research object, and verifies the recruitment. Whether the high frequency data of commercial banks have these typical characteristics, the analysis results show that the volume, the highest price and the lowest price have the typical characteristics such as the peak tail, the autocorrelation and the long memory and the intraday effect.
(4) this paper selects the high frequency transaction data every 5 minutes at the beginning of 2014 of China Merchants Bank, and selects the time series of the volume, the highest price, the lowest price and so on. First, according to the typical characteristics of the high frequency data, the research object is processed, the intra day effect is eliminated and the price is calculated by the maximum minimum price. In order to describe the asymmetric effect of price change on transaction intensity, the asymmetric MEM model of transaction intensity is established. The maximum likelihood estimation is used to estimate the asymmetric MEM model. The empirical analysis shows that the dynamic process of trading intensity can be obtained from the dynamic process of trading intensity. Characteristics, from the model results, it is found that both the price positive change or the negative change will increase the transaction intensity, that is, the price fluctuation will promote the market transaction, but the price change direction has different influence on the transaction intensity, indicating that the transaction intensity has obvious asymmetry.
Innovation in this paper: through the study of asymmetric MEM model, one aspect is the expansion of asymmetric GARCH model and asymmetric ACD model, which provides a more powerful research tool for the study of non negative time series in the securities market, and enriches the MEM model. On the other hand, we will be asymmetrical to the transaction intensity in our financial market. In the empirical study, there are few studies on the intensity of transaction in China. It is mainly to study the influence of market factors on the long and short period of the transaction intensity. Therefore, the empirical study of this paper has an important practical value for understanding the influence of the trading system and market structure on the investors and the market transaction activities and improving the supervision and regulation of the securities market in China. Value.
This paper is funded by the project of product error modeling and application of the non negative financial time series of the emerging order driven market of the National Natural Science Foundation of the National Natural Science Fund of 2011 (71101118).
【學(xué)位授予單位】：西南財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F832.51

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