本文選題：隔夜信息　切入點：股市波動率　出處：《山東大學(xué)》2017年博士論文

【摘要】：股市波動率的建模與預(yù)測一直以來是金融經(jīng)濟學(xué)研究的重要內(nèi)容。它對資產(chǎn)組合選擇、金融資產(chǎn)及其衍生品定價、以及金融機構(gòu)的風(fēng)險管理都具有重要意義。20世紀80年代起,國內(nèi)外學(xué)者提出了基于低頻數(shù)據(jù)的GARCH類和SV類等模型對股市波動率進行估計和預(yù)測,很好地刻畫了股市波動的集聚性和時變性特點。進入21世紀,基于高頻數(shù)據(jù)的股市波動率的建模與預(yù)測成為新的研究趨勢。在已實現(xiàn)波動率(RealizedVolatility,RV)的基礎(chǔ)上,涌現(xiàn)了能夠刻畫股市波動長記憶性和異質(zhì)特點的ARFIMA類和HAR類等經(jīng)典模型。信息的傳播和擴散是股市產(chǎn)生波動的內(nèi)在原因。由于股市的交易期間持續(xù)時間短,導(dǎo)致股市在兩個工作日之間的非交易時段內(nèi)積累了大量信息,這就是所謂的隔夜信息。由于政策傳導(dǎo)、經(jīng)濟走勢和國際聯(lián)動等因素,隔夜信息的產(chǎn)生涉及多個方面。因此,研究隔夜信息對中國股市波動預(yù)測的影響具有重要意義。以隔夜信息為新的切入點研究股市波動率的建模與預(yù)測是本文的核心內(nèi)容,并具有十分重要的意義。在學(xué)術(shù)方面,拓展了對隔夜信息的界定和分類,并結(jié)合隔夜信息對股市波動率的影響這一特點對其進行建模和預(yù)測研究,豐富了金融波動率建模的理論空間。在理論方面,為政策制定者、信息披露者和股市管理者提供理論依據(jù)。本研究致力于使決策管理部門能夠在保證調(diào)控目標和信息披露的前提下,清楚地認識到這些變動對股市波動造成的沖擊,從而形成合理健全的制度體系,有效地降低對股票市場和金融系統(tǒng)帶來的風(fēng)險,維護金融市場穩(wěn)定。在實踐方面,正確認識隔夜信息對股市波動率的影響對于股市投資者做出正確判斷有一定指導(dǎo)意義。股市波動并不是一種隨機行為,而是受到隔夜信息等因素的影響而變化的。投資者的正確認識一方面可以減少市場的投機行為,另一方面有利于他們充分利用隔夜信息做出合理的投資決策。本文首先回顧了國內(nèi)外學(xué)者在該領(lǐng)域的研究成果,確立了本文的研究方向和理論依據(jù),并為模型建立打下了實證支撐。在此基礎(chǔ)上,分別從理論和實證兩個方面論證隔夜信息對股市波動率的影響。理論方面,界定了隔夜信息的內(nèi)涵與分類,并通過信息與波動的相關(guān)理論、隔夜信息影響股市波動的微觀基礎(chǔ)以及隔夜信息影響股市波動的作用機理進行論證。實證方面,對各類隔夜信息、股市波動率及波動的隔夜表現(xiàn)和跳躍行為進行了度量,并通過格蘭杰因果關(guān)系檢驗和中介效應(yīng)分析兩條路徑來證明隔夜信息對股市波動的影響。最后,圍繞本文的的核心,分別提出了三種基于隔夜信息的股市波動率建模方式,并與傳統(tǒng)的波動率模型進行預(yù)測能力比較。其中,多因素-變系數(shù)模型和HAR-CJI模型是分別借助于隔夜信息影響股市波動的中介效應(yīng)——隔夜表現(xiàn)和跳躍行為對現(xiàn)有的經(jīng)典股市波動模型進行改進,將隔夜信息的影響考慮到波動率模型中。復(fù)合模型則是利用BP神經(jīng)網(wǎng)絡(luò)模型,將經(jīng)典波動模型的估計結(jié)果與隔夜信息綜合起來。通過對三種模型的實證檢驗發(fā)現(xiàn),隔夜信息能夠提升波動率模型的擬合效果和預(yù)測性能。相比較而言,前兩者模型具有較好的理論解釋能力,而后者則具有更好的預(yù)測效果。本文的研究結(jié)果體現(xiàn)在三個方面。首先,就隔夜信息對股市波動的影響來說,宏觀政策指標類信息、國際市場類信息和上市公司信息披露水平對股市波動表現(xiàn)出不同的影響。具體表現(xiàn)在,基準利率、存款準備金率與采購經(jīng)理指數(shù)等宏觀政策指標類信息的變動,國際油價、倫敦金價與納斯達克指數(shù)等國際市場類信息的利空表現(xiàn),上市公司信息披露程度的提高和兩個交易日之間的不連續(xù)對日內(nèi)波動均有增大效應(yīng)。同時,隔夜信息能夠通過影響股市的隔夜表現(xiàn)和股價波動的跳躍行為,對股市日內(nèi)波動率的預(yù)測起著重要作用。一方面,隔夜表現(xiàn)是各類隔夜信息影響股市波動的中介變量,而跳躍行為在部分隔夜信息對日內(nèi)波動的影響中表現(xiàn)出一定程度的中介效應(yīng)。其次,從基于隔夜信息的股市波動率模型構(gòu)建方面來看,本文所提出多因素-變系數(shù)模型、HAR-CJI模型和以BP神經(jīng)網(wǎng)絡(luò)為基礎(chǔ)的復(fù)合模型,在擬合效果和預(yù)測能力方面,比經(jīng)典波動模型和神經(jīng)網(wǎng)絡(luò)非參數(shù)模型表現(xiàn)更好。最后,從新模型的預(yù)測能力上看,考慮隔夜信息提高了模型在對股市波動率變動方向和數(shù)值大小預(yù)測方面的精度,同時提高了非參數(shù)模型的穩(wěn)定性。其中,對預(yù)測方向的改進主要表現(xiàn)在對股市波動正向變動的準確性上�；诟粢剐畔⒌慕�(jīng)典線性模型和神經(jīng)網(wǎng)絡(luò)模型在解釋股市波動率的理論意義、預(yù)測方式以及預(yù)測效果上存在差別。
[Abstract]:Modeling and forecasting of the volatility of the stock market is always an important part of financial economics. Its choice of a portfolio of financial assets and derivatives pricing and risk management of financial institutions is of great significance to the.20 century since 80s, domestic and foreign scholars put forward based on the low frequency data of GARCH and SV models on the stock market volatility to estimate and forecast, can depict the agglomeration and time-varying characteristics of volatility in the stock market. In twenty-first Century, modeling and prediction of stock market volatility of high frequency data rate to become the new trend of research. Based on realized volatility (RealizedVolatility, RV) on the basis of the emergence of the stock market can describe the long memory of volatility and the heterogeneous characteristics of the ARFIMA and HAR classes, such as the classical model. The information dissemination and diffusion is the inherent reason of stock market volatility. The stock market transactions during the short duration of lead Non trading periods between two working days of the stock market accumulated a large amount of information, which is called the overnight information. Because of policy, economy and international linkage and other factors, produce overnight information involves many aspects. Therefore, effect of overnight information has important significance to predicting the fluctuation of the stock market. China to overnight information for the modeling and prediction of the starting point to study the stock market volatility of the new is the core content, and has very important significance. In the academic field, expand the definition and classification of overnight information, combined with the overnight information on the study of modeling and prediction of the effect of stock market volatility of the characteristics of the rich. The theory of financial volatility modeling. In theory, for policy makers, and provide a theoretical basis for information disclosure and stock management. This research is committed to making decision management to guarantee The premise of control objectives and information disclosure, clearly aware of these changes on the stock market volatility caused by the impact, so as to form a sound system, effectively reduce the risk of the stock market and the financial system, maintain the stability of financial markets. In practice, the correct understanding of the overnight information influence on the volatility of the stock market on the stock market investors make the right judgment has certain guiding significance. The fluctuation of the stock market is not a random behavior, but influenced by the factors such as the overnight information changes. A correct understanding of investors can reduce market speculation, on the other hand it helps them to make full use of the overnight information to make rational investment decisions. This paper reviews the domestic and foreign scholars in this field of research, established the research direction and theoretical basis, and gives empirical support in the model. On this basis, respectively from two aspects of theoretical and empirical demonstration of overnight information on the stock market volatility. The theory, classification and definition of overnight information, and through the relevant theories of information and volatility, the overnight information affects the micro foundation of stock market volatility and stock market volatility of the overnight information influence mechanism of empirical demonstration. For all kinds of information, overnight, overnight performance of the stock market volatility and volatility and jump behavior of measurement, and through the Grainger causality test and the mediating effect of the two paths to prove the effects of the overnight information on the stock market fluctuation. Finally, around the core of this paper, we proposed three different stock market volatility overnight information rate based on the modeling method, and the traditional volatility model forecast ability. Among them, multi factor variable coefficient model and HAR-CJI model respectively with the help of the overnight letter Intermediary: overnight performance and jump behavior of stock market fluctuations of interest effect to improve the classic stock market volatility model existing, will affect the overnight information considering the volatility model. Composite model is to use the BP neural network model, the estimation results of classical wave model and the overnight information together. By empirical test on the three the model found that the overnight information can enhance the volatility model fitting effect and prediction performance. By comparison, the model has good explanation ability, while the latter has a better prediction effect. The results reflected in three aspects. First, effects of overnight information on stock market volatility. The macro policy index information, the level of international market information and information disclosure of listed companies have different impact on the stock market volatility. Specific performance in the benchmark interest rate, deposit A reserve ratio and purchasing managers index of macro policy index information changes, the international oil prices, the price of gold in London and the bad performance of NASDAQ and other international market information, between the information disclosure of listed companies increased and two trading days is not continuous on internal dynamic increased effect. At the same time, the overnight information can be affected by the stock market performance and stock price volatility of the overnight jump behavior, which plays an important role in the prediction of stock market intraday volatility. On the one hand, overnight is all kinds of overnight information affect the intermediary variables of stock market volatility, and jump in some overnight information influence on the daily fluctuations in showing the mediating effect to a certain extent. Secondly based on the model, from the stock market volatility overnight information, this paper proposed the multi factors - varying coefficient model, HAR-CJI model and BP neural network based complex In the model, fitting effect and prediction ability, than the classical wave model and neural network non parametric model performs better. Finally, the forecasting ability of the new model from the point of view, considering the overnight information improve the model in prediction accuracy and value direction of change on the stock market volatility, and improve the stability of non parameter model. The improvement of prediction direction is mainly reflected in the accuracy of volatility on the stock market. The positive changes in the overnight information of classical linear model and neural network model in theory explain the volatility of the stock market based on different prediction methods and prediction results.

【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：F832.51

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