本文選題：高頻數(shù)據(jù) + “日歷效應(yīng)”　； 參考：《中南大學(xué)》2013年碩士論文

【摘要】：隨著計(jì)算機(jī)技術(shù)的發(fā)展和通訊技術(shù)的革新,金融高頻數(shù)據(jù)的獲取與操作變得可行和簡(jiǎn)單。由于高頻數(shù)據(jù)包含了豐富的市場(chǎng)信息,及時(shí)從這大量數(shù)據(jù)中提取有效信息變得越來(lái)越重要。 本文主要從周期性和長(zhǎng)記憶性兩方面來(lái)分析我國(guó)股市高頻數(shù)據(jù)。首先,利用傳統(tǒng)方法對(duì)我國(guó)股市高頻數(shù)據(jù)進(jìn)行初步的統(tǒng)計(jì)分析,得出我國(guó)股市高頻收益率序列并不是正態(tài)分布,而是呈現(xiàn)出高峰厚尾性,且有著顯著的“日歷效應(yīng)”。其次,介紹了“已實(shí)現(xiàn)”波動(dòng)率及其擴(kuò)展形式,分析得出,由于賦權(quán)“已實(shí)現(xiàn)”波動(dòng)率充分考慮了“日歷效應(yīng)”,所以,賦權(quán)“已實(shí)現(xiàn)”波動(dòng)率比“已實(shí)現(xiàn)”波動(dòng)率和調(diào)整“已實(shí)現(xiàn)”波動(dòng)率更有效。同時(shí),在綜合考慮測(cè)量誤差和微觀結(jié)構(gòu)誤差這兩種誤差的基礎(chǔ)上得出“已實(shí)現(xiàn)”波動(dòng)率和賦權(quán)“已實(shí)現(xiàn)”波動(dòng)率的最優(yōu)抽樣頻率為5min。最后,利用R/S分析法和修正的R/S分析法分別進(jìn)行計(jì)算,其結(jié)果均表明賦權(quán)“已實(shí)現(xiàn)”波動(dòng)率具有長(zhǎng)記憶性。于是,在長(zhǎng)記憶HAR-RV模型的基礎(chǔ)上,本文提出了HAR-WRV模型,并以2011年度上證綜指采集頻率為5min的賦權(quán)“已實(shí)現(xiàn)”波動(dòng)率和“已實(shí)現(xiàn)”波動(dòng)率為例,通過(guò)實(shí)證分析得出,這兩個(gè)模型均能很好地對(duì)波動(dòng)率進(jìn)行擬合和預(yù)測(cè),且HAR-WRV模型的擬合和預(yù)測(cè)效果均優(yōu)于HAR-RV模型。
[Abstract]:With the development of computer technology and the innovation of communication technology, the acquisition and operation of financial high frequency data becomes feasible and simple. Because the high frequency data contain abundant market information, it becomes more and more important to extract effective information from the large amount of data in time. This paper analyzes the high frequency data of Chinese stock market from two aspects of periodicity and long memory. Firstly, using the traditional method to analyze the high frequency data of Chinese stock market, it is concluded that the series of high frequency returns is not a normal distribution, but a peak and a thick tail, and has a significant "calendar effect". Secondly, the "realized" volatility and its extended form are introduced. It is concluded that, because the "realized" volatility fully considers the "calendar effect", Weighted "realized" volatility is more effective than "realized" volatility and adjustment "realized" volatility. At the same time, the optimal sampling frequency of "realized" volatility and weighted "realized" volatility is 5 mins on the basis of synthetically considering two kinds of errors: measurement error and microstructure error. Finally, by using the R- / S method and the modified R- / S method, the results show that the weighted "realized" volatility has long memory. Therefore, based on the long memory HAR-RV model, this paper puts forward the HAR-WRV model, and takes the "realized" volatility and the "realized" volatility of the Shanghai Composite Index in 2011 as an example. Both models can fit and predict volatility, and the effect of HAR-WRV model is better than that of HAR-RV model.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F830.91;F224

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本文編號(hào)：2012517