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  本文選題：高頻金融數(shù)據(jù)　切入點：波動率　出處：《長春工業(yè)大學(xué)》2013年碩士論文

【摘要】：高頻金融數(shù)據(jù)是指日內(nèi)的金融時間序列,是以小時、分鐘或秒為抽樣頻率的數(shù)據(jù)。高頻金融數(shù)據(jù)樣本容量大,采集周期短,比低頻數(shù)據(jù)包含了更豐富的日內(nèi)收益波動信息,能夠更好的反映金融市場特征。隨著計算機技術(shù)與通訊技術(shù)的飛速發(fā)展高頻金融數(shù)據(jù)的獲取、存儲、檢索和處理已經(jīng)十分便利。為了更深入地研究金融市場的微觀結(jié)構(gòu),對高頻金融數(shù)據(jù)波動率的研究已經(jīng)成為國內(nèi)外金融領(lǐng)域的研究熱點。 各國的經(jīng)濟學(xué)家們已經(jīng)對高頻金融數(shù)據(jù)波動率進行了比較深入的研究。波動率是衍生工具定價、投資組合以及金融風(fēng)險管理的關(guān)鍵變量,構(gòu)建準確的金融波動估計量具有重要的理論和實踐意義。Andersen與Bollerslev在1998年提出一種全新的波動率估計的計算方法——“已實現(xiàn)”波動(RV),該方法無模型,計算簡便,在一定條件下是波動率的一致估計量,為這一領(lǐng)域的研究提出了新思路。為了得到更有效的估計方法,國內(nèi)外學(xué)者又對“已實現(xiàn)”波動(RV)進行了改進和擴展,給出了多種計算方法。 本文首先介紹了研究背景、選題的意義以及主要的工作,然后介紹了金融高頻數(shù)據(jù)研究的現(xiàn)狀,并指出了目前研究中存在的問題。在此基礎(chǔ)上研究了金融高頻數(shù)據(jù)波動率計算方法,針對“已實現(xiàn)”波動(RV)和“已實現(xiàn)”雙冪次變差(RBV)這兩個高頻金融波動率的計算方法進行了介紹,并做了比較,指出“已實現(xiàn)”雙冪次變差(RBV)在定義形式上比“已實現(xiàn)”波動(RV)所包含的內(nèi)容更加廣泛,除了具有穩(wěn)健性,還證明了在兩種條件下,“已實現(xiàn)”雙冪次變差(RBV)比“已實現(xiàn)”波動(RV)更有效。并以定理的形式指出“已實現(xiàn)”雙冪次變差是比“已實現(xiàn)”波動更有效的波動率估計量。同時又對波動率的計算方法進行了改進和擴展,通過對深證成指和上證綜指2011年12月14日到2012年12月14日以1分鐘為采樣周期的收盤價高頻金融數(shù)據(jù)進行了實證分析,實證分析表明,改進和擴展的波動率計算方法是更有效的。
[Abstract]:High frequency financial data financial time series is in the days, hours, minutes or seconds for the sampling frequency data. High frequency financial data samples of large capacity, short cycle of data acquisition, more days volatility contains information than low frequency data, which can reflect the characteristics of financial market better. With the rapid development of high frequency acquisition the financial data of computer technology and communication technology for the storage, retrieval and processing have been very convenient. In order to further study the microscopic structure of the financial market, the research of high frequency financial data volatility has become a hot research topic at home and abroad in the financial sector.
Economists, countries have conducted deep research on high frequency financial data. The volatility of volatility is the pricing of derivatives, portfolio and key variables of financial risk management, construction of the accurate estimation of the amount of financial volatility has important theoretical and practical significance of.Andersen and Bollerslev proposed a new calculation method in volatility estimation 1998 -- "realized volatility (RV), the method of model free, simple calculation, under certain conditions is consistent with the estimation of volatility, put forward new ideas for the research in this field. In order to get a more effective estimation method, scholars at home and abroad on realized volatility (RV) the improvement and expansion, given a variety of calculation methods.
This paper first introduces the research background, the significance of the topic and the main work, then introduces the research status of financial high frequency data, and points out the existing problems in the current study. On the basis of the research on the volatility of high frequency data calculation method for realized volatility (RV) and realized the double power variation (RBV) method to calculate the two high frequency financial volatility are introduced and compared, pointed out that the realized bipower variation (RBV) in the form of definition than the realized volatility (RV) contains more widely, in addition to the robustness, also proved in the two condition, the realized bipower (RBV) than the realized volatility (RV) is more effective. And in the form of the theorem that the realized bipower variation is greater than the realized volatility of more effective volatility estimator. At the same time wave Calculating method of dynamic rate was improved and extended by the Shenzhen and Shanghai from December 14, 2011 to December 14, 2012 to 1 minutes for the closing price of high frequency financial data sampling period by empirical analysis, empirical analysis shows that the calculation method of the improvement and extension of the volatility is more effective.

【學(xué)位授予單位】：長春工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F224;F830.9

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本文編號：1698555