本文選題：長(zhǎng)記憶性 + 高頻數(shù)據(jù)　； 參考：《西北農(nóng)林科技大學(xué)》2013年碩士論文

【摘要】：近半個(gè)世紀(jì)以來(lái)，中國(guó)的經(jīng)濟(jì)形式呈現(xiàn)出了前所未有的發(fā)展勢(shì)態(tài)，尤其值得關(guān)注的是自2001年中國(guó)正式成為WTO成員國(guó)以來(lái)，面對(duì)復(fù)雜多變的世界經(jīng)濟(jì)形勢(shì)，中國(guó)的經(jīng)濟(jì)將不可避免的受到來(lái)自外界環(huán)境的各種挑戰(zhàn)，而作為經(jīng)濟(jì)發(fā)展命脈的金融市場(chǎng)的發(fā)展已經(jīng)成為經(jīng)濟(jì)領(lǐng)域的研究者和管理者關(guān)注的焦點(diǎn)。在金融領(lǐng)域里關(guān)于各種金融資產(chǎn)價(jià)格波動(dòng)的研究一直是眾多經(jīng)濟(jì)研究者關(guān)注的焦點(diǎn)之一，特別是計(jì)算機(jī)技術(shù)的迅猛發(fā)展，極大地方便了各種金融數(shù)據(jù)的獲取和存儲(chǔ)，使得高頻甚至超高頻數(shù)據(jù)的獲取和存儲(chǔ)成為了現(xiàn)實(shí)。經(jīng)典的波動(dòng)率模型主要是以頻率較低的數(shù)據(jù)如日、星期、月為建模研究的對(duì)象，不僅數(shù)據(jù)量小，而且獲取周期比較長(zhǎng)，而利用金融高頻數(shù)據(jù)進(jìn)行建模研究，就可以在很短的時(shí)間段內(nèi)獲得大量的數(shù)據(jù)，大大的縮短了數(shù)據(jù)的獲取周期，從而也就節(jié)省了大量時(shí)間。和低頻數(shù)據(jù)相比較，高頻數(shù)據(jù)則體現(xiàn)出更加豐富的價(jià)格變動(dòng)過(guò)程中的信息和長(zhǎng)期日間現(xiàn)象的信息，這將大大有利于市場(chǎng)中某些微觀結(jié)構(gòu)理論的研究。所以，與傳統(tǒng)的建立在低頻數(shù)據(jù)基礎(chǔ)上的波動(dòng)率建模研究的不同，本文主要是在上證5間隔的高頻數(shù)據(jù)的基礎(chǔ)上研究波動(dòng)率及其長(zhǎng)記憶性。自從Engle（1951）發(fā)現(xiàn)時(shí)間序列的長(zhǎng)記憶性這一性質(zhì)以來(lái)，對(duì)長(zhǎng)記憶這一性質(zhì)的建模和分析正日益受到金融界的廣泛關(guān)注，因?yàn)殚L(zhǎng)記憶性就意味著現(xiàn)在的狀態(tài)將持續(xù)影響將來(lái)，這對(duì)于金融風(fēng)險(xiǎn)管理有著不可忽視的作用。本文研究了兩個(gè)非常重要的長(zhǎng)記憶時(shí)間序列模型：分?jǐn)?shù)差分噪聲（FDN）模型和分整自回歸移動(dòng)平均ARFIMA模型，還對(duì)已實(shí)現(xiàn)波動(dòng)率的ARFIMA-RV模型進(jìn)行了研究。 自從已實(shí)現(xiàn)波動(dòng)率被提出后，張世英等學(xué)者對(duì)其進(jìn)行了擴(kuò)展和改進(jìn)，得到了賦權(quán)已實(shí)現(xiàn)波動(dòng)率，查閱大量文獻(xiàn)資料，發(fā)現(xiàn)目前對(duì)于賦權(quán)已實(shí)現(xiàn)波動(dòng)率的建模研究鮮有涉及，盡管眾多學(xué)者認(rèn)為ARFIMA模型已經(jīng)能夠很好的預(yù)測(cè)波動(dòng)率，但是如何把賦權(quán)已實(shí)現(xiàn)波動(dòng)率和ARFIMA有效的結(jié)合起來(lái)，從而建議一個(gè)符合高頻數(shù)據(jù)自身性質(zhì)同時(shí)也可以用來(lái)估計(jì)市場(chǎng)波動(dòng)率的模型是目前關(guān)于波動(dòng)率建模中一個(gè)比較新的研究方向和難點(diǎn)，本文所做的研究就是在這一問(wèn)題背景下展開(kāi)的。通過(guò)實(shí)證分析，得出了賦權(quán)已實(shí)現(xiàn)波動(dòng)率存在長(zhǎng)記憶性，并且對(duì)賦權(quán)已實(shí)現(xiàn)波動(dòng)率取對(duì)數(shù)后，得到的序列表現(xiàn)出了非常明顯的正態(tài)性的特征，，基于此特點(diǎn)和其長(zhǎng)記憶性建立了對(duì)數(shù)賦權(quán)已實(shí)現(xiàn)波動(dòng)率的分整自回歸移動(dòng)平均模型（ARFIMA-ARIMA-lnWRV），通過(guò)參數(shù)估計(jì)確定了模型中各參數(shù)的值，同時(shí)對(duì)結(jié)果進(jìn)行了檢驗(yàn)，證實(shí)了模型的良好性，最后介紹了VaR模型的發(fā)展及應(yīng)用，并研究了賦權(quán)已實(shí)現(xiàn)波動(dòng)率在風(fēng)險(xiǎn)價(jià)值度量VaR中的應(yīng)用。
[Abstract]:In the past half century, China's economic form has taken on an unprecedented development, especially since China became a member of the WTO in 2001, facing the complex and changeable world economic situation. China's economy will inevitably be challenged by the external environment, and the development of the financial market, which is the lifeblood of economic development, has become the focus of attention of researchers and managers in the economic field. In the field of finance, the research on the fluctuation of the price of various kinds of financial assets has been one of the focuses of many economic researchers, especially the rapid development of computer technology, which greatly facilitates the acquisition and storage of all kinds of financial data. It makes the acquisition and storage of high frequency and even ultra high frequency data become a reality. The classical volatility model is mainly based on low-frequency data such as day, week and month. It not only has a small amount of data, but also has a long acquisition period. It can obtain a lot of data in a very short period of time, greatly shorten the period of data acquisition, and thus save a lot of time. Compared with the low-frequency data, the high-frequency data reflect more abundant information in the process of price change and the information of long-term daytime phenomenon, which will greatly benefit the study of some microstructural theories in the market. Therefore, unlike the traditional research on volatility modeling based on low frequency data, this paper mainly studies volatility and its long memory on the basis of high frequency data of 5 interval in Shanghai Stock Exchange. Since Engle discovered the property of long memory in time series, the modeling and analysis of long memory has attracted more and more attention in the financial world, because long memory means that the present state will continue to affect the future. This for financial risk management has a role that can not be ignored. In this paper, two very important long memory time series models: fractional differential noise (FDN) model and fractional autoregressive moving average (ARFIMA) model are studied. The ARFIMA-RV model of realized volatility is also studied. Since the realization of volatility has been put forward, Zhang Shiying and other scholars have extended and improved it, obtained weighted realized volatility, consulted a lot of literature, found that the modeling of weighted realized volatility is rarely involved. Although many scholars think that ARFIMA model has been able to predict volatility, but how to effectively combine weighted realized volatility with ARFIMA. Therefore, it is suggested that a model which can also be used to estimate market volatility in accordance with the nature of high frequency data is a relatively new research direction and difficulty in volatility modeling. The research done in this paper is carried out under the background of this problem. Through the empirical analysis, it is concluded that the weighted realized volatility has long memory, and the logarithm of the weighted realized volatility shows the characteristic of normality. Based on this characteristic and its long memory property, an ARFIMA-ARIMA-lnWRVV model of integral autoregressive moving average (ARFIMA-ARIMA-lnWRVV) with logarithmic weighting of realized volatility is established. The values of each parameter in the model are determined by parameter estimation, and the results are verified and the model is proved to be good. Finally, the development and application of VaR model are introduced, and the application of weighted realized volatility in VaR is studied.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F224;F832.5

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