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

【摘要】：隨著計算機技術的發(fā)展和通訊技術的革新,金融高頻數(shù)據(jù)的獲取與操作變得可行和簡單。由于高頻數(shù)據(jù)包含了豐富的市場信息,及時從這大量數(shù)據(jù)中提取有效信息變得越來越重要。 本文主要從周期性和長記憶性兩方面來分析我國股市高頻數(shù)據(jù)。首先,利用傳統(tǒng)方法對我國股市高頻數(shù)據(jù)進行初步的統(tǒng)計分析,得出我國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”。其次,介紹了“已實現(xiàn)”波動率及其擴展形式,分析得出,由于賦權“已實現(xiàn)”波動率充分考慮了“日歷效應”,所以,賦權“已實現(xiàn)”波動率比“已實現(xiàn)”波動率和調整“已實現(xiàn)”波動率更有效。同時,在綜合考慮測量誤差和微觀結構誤差這兩種誤差的基礎上得出“已實現(xiàn)”波動率和賦權“已實現(xiàn)”波動率的最優(yōu)抽樣頻率為5min。最后,利用R/S分析法和修正的R/S分析法分別進行計算,其結果均表明賦權“已實現(xiàn)”波動率具有長記憶性。于是,在長記憶HAR-RV模型的基礎上,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“已實現(xiàn)”波動率和“已實現(xiàn)”波動率為例,通過實證分析得出,這兩個模型均能很好地對波動率進行擬合和預測,且HAR-WRV模型的擬合和預測效果均優(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.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F830.91;F224

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本文編號：2012517