本文選題：MTPCOV + KCOV　； 參考：《西南財經(jīng)大學》2014年碩士論文

【摘要】：資本資產(chǎn)定價模型(CAPM)是現(xiàn)代金融學的理論基礎,這一模型在證券估價、投資組合等方面有著廣泛的應用,CAPM中最具突破性意義的是β系數(shù),它描述了單個資產(chǎn)受市場整體波動的影響程度,刻畫了證券資產(chǎn)的系統(tǒng)性風險。由于參數(shù)β在投資理論和實踐中有著重要的地位,因此對參數(shù)β的準確估計具有極其重要的理論價值和現(xiàn)實意義。 關于參數(shù)β估計的文獻中,主要存在兩方面的不足：(1)文獻中通常都是基于低頻數(shù)據(jù),運用高頻數(shù)據(jù)估計參數(shù)β的比較少。金融市場上的信息對資產(chǎn)價格的影響是連續(xù)的過程,當數(shù)據(jù)采樣頻率比較低時,就會導致數(shù)據(jù)的離散,數(shù)據(jù)的離散會導致不同程度的信息缺失。(2)隨著計算機的發(fā)展,高頻數(shù)據(jù)的獲得成為可能,學者們基于高頻數(shù)據(jù)提出了很多高頻協(xié)方差矩陣的估計方法,部分文獻也將這些估計方法運用到參數(shù)β的估計上。在復雜的金融市場上,所觀測到的高頻資產(chǎn)價格往往包含市場微觀結構噪聲和跳躍的成分,微觀結構噪聲對資產(chǎn)價格的影響程度隨著數(shù)據(jù)的采樣頻率的提高而加強。只考慮市場微觀結構噪聲和跳躍兩者之一對資產(chǎn)協(xié)方差矩陣的影響往往是不全面的,就無法準確的估計出高頻協(xié)方差矩陣。然而目前文獻對參數(shù)β估計所選用的高頻協(xié)方差矩陣估計方法僅僅考慮市場微觀結構噪聲或跳躍,沒有綜合考慮兩個方面的影響。 針對現(xiàn)有文獻所描述的不足之處,本文基于高頻數(shù)據(jù),運用綜合考慮金融市場微觀結構噪聲和跳躍影響的高頻協(xié)方差矩陣估計方法：修正的門限預平均已實現(xiàn)協(xié)方差陣(MTPCOV)來估計參數(shù)β, MTPCOV是用修正的預平均方法消除微觀結構噪聲、用門限的方法剔除跳躍,通過這個方法估計出來的參數(shù)β理論上比目前文獻中的其它估計方法要更加精確。 為了驗證這一點,本文分別基于低頻和高頻數(shù)據(jù)來對參數(shù)β進行估計,對于低頻數(shù)據(jù),選擇基于狀態(tài)空間模型的Kalman濾波算法和DCC-MVGARCH模型；對于高頻數(shù)據(jù),選擇多元核光滑協(xié)方差陣(KCOV),運用四種方法對參數(shù)β進行估計,并且用估計出來的時變參數(shù)β建立約束條件,構建動態(tài)投資組合,通過比較投資組合在分散非系統(tǒng)性風險和最大化組合收益率方面的效果,來分析時變參數(shù)β約束的有效性,從而可以比較四種方法估計參數(shù)β的精確性。 本文論述了文章選題的背景和意義,闡述了本文的研究思路、方法與創(chuàng)新,對近年來的相關文獻進行了梳理,對文章的章節(jié)安排、主要研究內容進行了說明。本文選取上證180指數(shù)作為市場組合數(shù)據(jù),時間跨度從2012年1月4日到2013年12月31日,按照時間跨度內上證180指數(shù)中180支樣本股的日度交易頻率高低均勻選取8支股票,以日度交易數(shù)據(jù)和1分鐘高頻交易數(shù)據(jù)作為研究樣本。 通過對時變參數(shù)β進行實證研究,本文得出： (1)參數(shù)β是不穩(wěn)定的,具有時變的特性。(2)DCC-MVGARCH模型、KCOV以及MTPCOV估計出來的參數(shù)β波動比較大,運用狀態(tài)空間模型估計出來的時變參數(shù)β波動較小。(3)MTPCOV估計出來的時變參數(shù)β約束下的動態(tài)市場組合的累計收益率大于KCOV、DCC-MVGARCH模型以及狀態(tài)空間模型估計出來的時變參數(shù)β約束下的動態(tài)市場組合；所構建的動態(tài)市場組合的系統(tǒng)性風險占總風險的比例也大于其它三種方法估計出來的時變參數(shù)β約束下的動態(tài)市場組合。
[Abstract]:The capital asset pricing model (CAPM) is the theoretical basis of modern finance. This model is widely used in securities valuation, investment portfolio and so on. The most breakthroughs in CAPM is the beta coefficient. It describes the impact of the volatility of a single asset by the market as a whole and portrays the systematic risk of securities assets. Capital theory and practice play an important role. Therefore, accurate estimation of parameter beta is of great theoretical and practical significance.
There are two main deficiencies in the literature on parameter beta estimation: (1) there are usually low frequency data based on low frequency data and low estimation of parameters by high frequency data. The impact of information on asset prices in financial markets is a continuous process. When the ratio of data sampling frequency is low, the data will be dispersed and the data is discrete. (2) with the development of computers, the acquisition of high frequency data is possible, and many high frequency covariance matrices are proposed based on high frequency data. Some literature also applies these methods to the estimation of parameter beta. In complex financial markets, the high frequency assets are observed. The price often includes the components of the market micro structure noise and jumping, and the influence of the microstructural noise on the asset price is strengthened with the increase of the sampling frequency of the data. Only considering the influence of the market micro structure noise and jumping both on the asset covariance matrix is often not comprehensive, and the high frequency can not be accurately estimated. Covariance matrix. However, the high frequency covariance matrix estimation method used in the current literature on parameter beta estimation only takes into account the market microstructure noise or jump, and does not consider the effects of two aspects.
In view of the shortcomings described in the existing literature, based on the high frequency data, this paper uses the high frequency covariance matrix estimation method which considers the microstructural noise and jumping effects of the financial market synthetically: the modified threshold preaverage has realized the covariance matrix (MTPCOV) to estimate the parameter beta, and the MTPCOV is to eliminate the microstructural noise by the modified preaverage method. The method of thresholding is used to eliminate hopping. The parameter beta estimated by this method is more accurate than other estimation methods in the literature.
In order to verify this point, this paper estimates the parameter beta based on the low frequency and high frequency data respectively. For low frequency data, we choose the Kalman filtering algorithm and the DCC-MVGARCH model based on the state space model. For the high frequency data, the multiple kernel smooth covariance matrix (KCOV) is selected and the parameter beta is estimated with four methods, and the estimation is used. The time varying parameter beta builds the constraint conditions, constructs the dynamic portfolio, and compares the effectiveness of the time-varying parameter beta constraint by comparing the effect of the investment portfolio in dispersing the non systematic risk and maximizing the combination yield, thus comparing the four methods to estimate the accuracy of the parameter beta.
This paper expounds the background and significance of the topic selection, expounds the research ideas, methods and innovations of this article, combs the relevant literature in recent years, and explains the main contents of the chapters in the article. This paper selects the Shanghai 180 index as the number of the market combination, the time span from January 4, 2012 to December 31, 2013, According to the daily transaction frequency of 180 sample stocks in the 180 Index of the time span, 8 shares are evenly selected, and the daily transaction data and the 1 minute high frequency transaction data are used as the research samples.
Through empirical research on time-varying parameter beta, the paper concludes that:
(1) the parameter beta is unstable and has time-varying characteristics. (2) the DCC-MVGARCH model, KCOV and MTPCOV estimate the parameter beta fluctuation relatively large, and the time-varying parameter beta fluctuation estimated by the state space model is smaller. (3) the cumulative yield of the dynamic market combination under the time-varying parameter beta constraint under the MTPCOV estimation is greater than KCOV, DCC-MVGARCH The dynamic market combination under the constraint of time-varying parameter beta is estimated by the model and the state space model. The proportion of the systematic risk of the dynamic market portfolio to the total risk is greater than the dynamic market combination under the time variable parameter beta constraint estimated by the other three methods.
【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】：F224;F830.91

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本文編號：2009292