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

【摘要】：近年來(lái),對(duì)金融高頻數(shù)據(jù)研究已經(jīng)成為了金融計(jì)量學(xué)的一個(gè)全新的研究領(lǐng)域和方向。金融風(fēng)險(xiǎn)度量也是在目前金融自由化下風(fēng)險(xiǎn)管理中最重要的部分。本論文主要研究了金融市場(chǎng)高頻數(shù)據(jù)的特性、建模,并將它其應(yīng)用在到金融風(fēng)險(xiǎn)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(shí)現(xiàn)”波動(dòng)率及三個(gè)改進(jìn)高頻波動(dòng)量,調(diào)整“已實(shí)現(xiàn)”波動(dòng)率(ARV)、“已實(shí)現(xiàn)”雙冪次變差(RBV)、賦權(quán)“已實(shí)現(xiàn)”雙冪次變差(WRBV)。WRBV賦權(quán)“已實(shí)現(xiàn)”雙冪次變差考慮了“日歷效應(yīng)”,具有穩(wěn)健性,同時(shí)是有效的高頻波動(dòng)估計(jì)量。 (2)本文比較三種最優(yōu)時(shí)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(shí)間間隔是更簡(jiǎn)單易行的方法。并且用“已實(shí)現(xiàn)”波動(dòng)率及其改進(jìn)的三個(gè)波動(dòng)率估計(jì)量為例,實(shí)證研究證明了WRBV是比已實(shí)現(xiàn)波動(dòng)更有效的波動(dòng)率估計(jì)量。 (3)建立基于WRBV的ARFIMA高頻波動(dòng)模型,將其與GARCH模型相比,用AIC和SBC準(zhǔn)則對(duì)模型模擬效果進(jìn)行評(píng)價(jià),證明其為更好的擬合模型。 (4)用改進(jìn)的高頻波動(dòng)率WRBV代替RV用于計(jì)算VaR,即建立WRBV-VaR模型。用股指期貨的高頻數(shù)據(jù)對(duì)WRBV-VaR模型進(jìn)行實(shí)證研究,并將其與傳統(tǒng)的風(fēng)險(xiǎn)度量模型GARCH-VAR進(jìn)行風(fēng)險(xiǎn)估測(cè)效果的比較。用Kupiec失敗率檢驗(yàn)法對(duì)對(duì)風(fēng)險(xiǎn)價(jià)值模型的效果進(jìn)行評(píng)價(jià),得出WRBV-VaR模型對(duì)VaR有更好的估測(cè)。 VaR風(fēng)險(xiǎn)度量可以將不同市場(chǎng)因子、不同市場(chǎng)的風(fēng)險(xiǎn)集成為一個(gè)數(shù)。WRBV-VaR模型對(duì)金融風(fēng)險(xiǎn)的度量的有一定的綜合性,但是它只能對(duì)市場(chǎng)正常情況下的風(fēng)險(xiǎn)進(jìn)行預(yù)測(cè)和控制,而不能對(duì)市場(chǎng)上出現(xiàn)的極端事件進(jìn)行預(yù)測(cè)和控制。因此,本文對(duì)金融風(fēng)險(xiǎn)度量有一定的局限性。如何對(duì)模型本身進(jìn)行改進(jìn)使得預(yù)測(cè)效果更好,有待今后進(jìn)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(xué)位授予單位】：武漢理工大學(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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