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  本文關(guān)鍵詞： 賦權(quán)“已實現(xiàn)”雙冪次變差 高頻波動 VaR　出處：《武漢理工大學》2013年碩士論文　論文類型：學位論文

【摘要】：近年來,對金融高頻數(shù)據(jù)研究已經(jīng)成為了金融計量學的一個全新的研究領(lǐng)域和方向。金融風險度量也是在目前金融自由化下風險管理中最重要的部分。本論文主要研究了金融市場高頻數(shù)據(jù)的特性、建模,并將它其應(yīng)用在到金融風險度量VaR中。本文的主要工作可以概括如下： (1)本文在高頻數(shù)據(jù)的基礎(chǔ)上,提出“已實現(xiàn)”波動率及三個改進高頻波動量,調(diào)整“已實現(xiàn)”波動率(ARV)、“已實現(xiàn)”雙冪次變差(RBV)、賦權(quán)“已實現(xiàn)”雙冪次變差(WRBV)。WRBV賦權(quán)“已實現(xiàn)”雙冪次變差考慮了“日歷效應(yīng)”,具有穩(wěn)健性,同時是有效的高頻波動估計量。 (2)本文比較三種最優(yōu)時間間隔的選擇方法的思路,指出了方差法是獲取最優(yōu)抽樣時間間隔是更簡單易行的方法。并且用“已實現(xiàn)”波動率及其改進的三個波動率估計量為例,實證研究證明了WRBV是比已實現(xiàn)波動更有效的波動率估計量。 (3)建立基于WRBV的ARFIMA高頻波動模型,將其與GARCH模型相比,用AIC和SBC準則對模型模擬效果進行評價,證明其為更好的擬合模型。 (4)用改進的高頻波動率WRBV代替RV用于計算VaR,即建立WRBV-VaR模型。用股指期貨的高頻數(shù)據(jù)對WRBV-VaR模型進行實證研究,并將其與傳統(tǒng)的風險度量模型GARCH-VAR進行風險估測效果的比較。用Kupiec失敗率檢驗法對對風險價值模型的效果進行評價,得出WRBV-VaR模型對VaR有更好的估測。 VaR風險度量可以將不同市場因子、不同市場的風險集成為一個數(shù)。WRBV-VaR模型對金融風險的度量的有一定的綜合性,但是它只能對市場正常情況下的風險進行預(yù)測和控制,而不能對市場上出現(xiàn)的極端事件進行預(yù)測和控制。因此,本文對金融風險度量有一定的局限性。如何對模型本身進行改進使得預(yù)測效果更好,有待今后進一步的研究。
[Abstract]:In recent years, The research on financial high-frequency data has become a new research field and direction of financial metrology. Financial risk measurement is also the most important part of risk management under the current financial liberalization. Characteristics of high-frequency data in financial markets, Modeling and applying it to the financial risk measurement VaR. The main work of this paper can be summarized as follows:. 1) on the basis of high frequency data, this paper puts forward the "realized" volatility and three improved high frequency fluctuations. To adjust the "realized" volatility rate (ARV), "realized" "double power variation difference" (RBV), to "realize" "double power variation", to realize "double power variation" has been realized. "double power variation" takes into account "calendar effect" and is robust, and is an effective high frequency fluctuation estimator at the same time. This paper compares three methods of selecting optimal time interval, and points out that the variance method is a more simple and feasible method to obtain the optimal sampling interval, and takes "realized" volatility and its improved three volatility estimators as examples. Empirical studies prove that WRBV is a more effective volatility estimator than realized volatility. (3) the high frequency fluctuation model of ARFIMA based on WRBV is established. Compared with the GARCH model, the AIC and SBC criteria are used to evaluate the simulation effect of the model, and it is proved that the model is a better fit model. In this paper, we use improved high frequency volatility WRBV instead of RV to calculate VaR, that is, to establish WRBV-VaR model. Using the high frequency data of stock index futures, we make an empirical study on WRBV-VaR model. Compared with the traditional risk measurement model (GARCH-VAR), this paper evaluates the effect of the risk value model by using the Kupiec failure rate test method, and concludes that the WRBV-VaR model has a better estimate of the VaR. VaR risk measurement can integrate different market factors and different market risks into a number. WRBV-VaR model has a certain degree of integration to measure financial risk, but it can only predict and control the risk under normal market conditions. Therefore, this paper has some limitations on financial risk measurement. How to improve the model itself to make the forecasting effect better, need further research in the future.
【學位授予單位】：武漢理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F830.91;F224

【參考文獻】

相關(guān)期刊論文 前10條

1 徐正國,張世英;調(diào)整"已實現(xiàn)"波動率與GARCH及SV模型對波動的預(yù)測能力的比較研究[J];系統(tǒng)工程;2004年08期

2 鄭文通;金融風險管理的VAR方法及其應(yīng)用[J];國際金融研究;1997年09期

3 田新時,劉漢中,李耀;滬深股市一般誤差分布(GED)下的VaR計算[J];管理工程學報;2003年01期

4 胡煒童;李振東;;基于不同殘差分布假定的GARCH類模型預(yù)測能力比較[J];甘肅科學學報;2008年01期

5 熊正德;張潔;;“已實現(xiàn)”波動率理論研究與評價[J];湖南大學學報(社會科學版);2007年04期

6 王春峰,萬海輝,李剛;基于MCMC的金融市場風險VaR的估計[J];管理科學學報;2000年02期

7 余素紅,張世英,宋軍;基于GARCH模型和SV模型的VaR比較[J];管理科學學報;2004年05期

8 黃后川,陳浪南;中國股票市場波動率的高頻估計與特性分析[J];經(jīng)濟研究;2003年02期

9 邵錫棟;殷煉乾;;基于實現(xiàn)極差和實現(xiàn)波動率的中國金融市場風險測度研究[J];金融研究;2008年06期

10 霍光耀;郭名媛;;金融波動研究的新進展及未來展望[J];西北農(nóng)林科技大學學報(社會科學版);2007年05期

相關(guān)博士學位論文 前1條

1 唐勇;基于高頻數(shù)據(jù)的金融市場分析[D];天津大學;2007年

，

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