本文關(guān)鍵詞：HAR類已實(shí)現(xiàn)波動(dòng)率模型及其在中國(guó)股票市場(chǎng)中的應(yīng)用研究　出處：《長(zhǎng)沙理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 已實(shí)現(xiàn)波動(dòng)率 HAR類模型 異質(zhì)市場(chǎng)假說 動(dòng)量效應(yīng) 資本利得突出量

【摘要】：股票市場(chǎng)的波動(dòng)率以及相關(guān)特征的研究，是國(guó)內(nèi)外金融學(xué)者和業(yè)界金融從業(yè)者研究金融風(fēng)險(xiǎn)度量、金融資產(chǎn)定價(jià)、金融衍生品定價(jià)等金融實(shí)務(wù)問題的基礎(chǔ)。定量分析這些實(shí)務(wù)問題，其前提是對(duì)資產(chǎn)波動(dòng)率進(jìn)行準(zhǔn)確的度量和預(yù)測(cè)。因此，資產(chǎn)波動(dòng)率的度量和預(yù)測(cè)一直是金融學(xué)者關(guān)注和研究的熱點(diǎn)。近年來，隨著通訊技術(shù)及計(jì)算機(jī)的使用和普及，在很大程度上降低了金融交易數(shù)據(jù)的記錄和存儲(chǔ)成本，從而使得金融高頻交易數(shù)據(jù)日益成為研究金融資產(chǎn)波動(dòng)率的重要手段。Anderson和Bollerslev（1998）運(yùn)用日內(nèi)高頻數(shù)據(jù)計(jì)算的已實(shí)現(xiàn)波動(dòng)率，相比傳統(tǒng)的資產(chǎn)波動(dòng)率度量模型，對(duì)股票市場(chǎng)波動(dòng)率的度量精度更高。因此，本文從已實(shí)現(xiàn)波動(dòng)率的角度，通過構(gòu)建新的已實(shí)現(xiàn)波動(dòng)率模型來研究中國(guó)股票市場(chǎng)的波動(dòng)率。 本文首先介紹已實(shí)現(xiàn)波動(dòng)率的基本理論，以及分析中國(guó)股票市場(chǎng)已實(shí)現(xiàn)波動(dòng)率的特征。然后，考慮隔夜收益方差，對(duì)現(xiàn)有的HAR-RV和HAR-CJ模型進(jìn)行改進(jìn)，構(gòu)建新的HAR-ARV和HAR-CJ模型；并在新的HAR-CJ模型的基礎(chǔ)上，加入投資者的行為偏差因素（動(dòng)量效應(yīng)），構(gòu)建了HAR-CJ-M模型。接著，以中國(guó)股票市場(chǎng)滬深300指數(shù)的5分鐘高頻數(shù)據(jù)為研究樣本，對(duì)HAR-ARV模型、HAR-CJ模型和HAR-CJ-M模型進(jìn)行參數(shù)估計(jì)，再使用這三個(gè)模型的其他兩種常用的表達(dá)形式、選擇不同的樣本長(zhǎng)度和選擇不同的參考價(jià)格（HAR-CJ-M模型的研究）對(duì)三個(gè)模型的參數(shù)估計(jì)結(jié)果進(jìn)行穩(wěn)健性檢驗(yàn)。最后，運(yùn)用損失函數(shù)法比較這三個(gè)模型對(duì)中國(guó)股票市場(chǎng)未來調(diào)整已實(shí)現(xiàn)波動(dòng)率的預(yù)測(cè)能力。 研究結(jié)果表明：中國(guó)股票市場(chǎng)已實(shí)現(xiàn)波動(dòng)率存在明顯的尖峰厚尾性、右偏性、跳躍性和長(zhǎng)記憶性特征；HAR-RV模型、HAR-CJ模型和HAR-CJ-M模型的構(gòu)建具有較強(qiáng)的理論支撐，且它們適用于中國(guó)股票市場(chǎng)的已實(shí)現(xiàn)波動(dòng)率的研究。其中，，HAR-ARV模型的估計(jì)結(jié)果證實(shí)了中國(guó)股票投資者異質(zhì)性的存在，與Müller等（1993）提出的“異質(zhì)市場(chǎng)假說”理論相吻合。HAR-CJ模型和HAR-CJ-M模型的估計(jì)結(jié)果可以看出中國(guó)股票市場(chǎng)中歷史的連續(xù)樣本路徑方差成分對(duì)未來波動(dòng)率有一定的預(yù)測(cè)作用，而歷史的離散跳躍方差成分的預(yù)測(cè)能力很弱，同時(shí)從HAR-CJ-M模型的估計(jì)結(jié)果中，發(fā)現(xiàn)不同期限（日、周和月）的動(dòng)量效應(yīng)因素（資本利得突出量）的系數(shù)大多都顯著，說明中國(guó)股票市場(chǎng)中各類投資者的這一非理性行為對(duì)未來的波動(dòng)率有一定的預(yù)測(cè)作用。另外，本文的研究還表明：HAR-RV模型、HAR-CJ模型和HAR-CJ-M模型對(duì)中國(guó)股票市場(chǎng)波動(dòng)率有不錯(cuò)的預(yù)測(cè)能力，而加入動(dòng)量效應(yīng)因素的HAR-CJ-M模型預(yù)測(cè)中國(guó)股票市場(chǎng)波動(dòng)率的能力明顯強(qiáng)于其它兩個(gè)模型，它更有利于金融風(fēng)險(xiǎn)度量、金融資產(chǎn)定價(jià)和金融衍生品定價(jià)等中國(guó)金融實(shí)務(wù)問題的研究。
[Abstract]:The research on volatility and related characteristics of stock market is a study of financial risk measurement and financial asset pricing by domestic and foreign financial scholars and industry financial practitioners. Financial derivatives pricing and other financial practical issues. Quantitative analysis of these practical issues, its premise is to accurately measure and predict the volatility of assets. The measurement and prediction of asset volatility has always been the focus of attention and research by financial scholars. In recent years, with the use and popularization of communication technology and computers. It greatly reduces the cost of recording and storing financial transaction data. As a result, financial high-frequency trading data is increasingly becoming an important means of studying volatility of financial assets. Anderson and Bollerslev 1998). Realized volatility calculated using intraday high frequency data. Compared with the traditional asset volatility measurement model, the measurement accuracy of the volatility of the stock market is higher. Therefore, this paper from the point of view of realized volatility. The volatility of Chinese stock market is studied by constructing a new realized volatility model. This paper first introduces the basic theory of realized volatility, and analyzes the characteristics of realized volatility in Chinese stock market. Then, the variance of overnight return is considered. The existing HAR-RV and HAR-CJ models are improved to construct new HAR-ARV and HAR-CJ models. On the basis of the new HAR-CJ model, the HAR-CJ-M model is constructed by adding the investor behavior deviation factor (momentum effect). Based on the 5-minute high frequency data of CSI 300 index in Chinese stock market, the parameters of HAR-ARV model, HAR-CJ model and HAR-CJ-M model are estimated. Then use the other two common expressions of these three models. Select different sample length and choose different reference price to study HAR-CJ-M model. The loss function method is used to compare the forecasting ability of the three models for the future adjustment of the realized volatility in the Chinese stock market. The results show that: the volatility of Chinese stock market has obvious characteristics of sharp tail, right deviation, jump and long memory; The construction of HAR-RV model HAR-CJ model and HAR-CJ-M model has strong theoretical support, and they are suitable for the research of realized volatility in Chinese stock market. The estimation results of HAR-ARV model confirm the existence of heterogeneity of Chinese stock investors. M 眉 ller et al. 1993). Heterogeneous Market hypothesis. The estimation results of HAR-CJ model and HAR-CJ-M model show that the component of path variance of continuous sample in Chinese stock market can predict the future volatility to some extent. The history of discrete jump variance components of the prediction ability is very weak, and from the HAR-CJ-M model estimates, we found that different periods (days). Most of the momentum factors (capital gain protruding) of the week and month are significant, indicating that the irrational behavior of various investors in the Chinese stock market has a certain predictive effect on the future volatility. The research of this paper also shows that the HAR-CJ model and HAR-CJ-M model have a good ability to predict the volatility of Chinese stock market. However, the HAR-CJ-M model with momentum effect is better than the other two models in predicting the volatility of Chinese stock market, which is more favorable to the measurement of financial risk. Financial asset pricing and financial derivatives pricing and other issues of financial practice in China.
【學(xué)位授予單位】：長(zhǎng)沙理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F832.51;F224

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