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  本文關鍵詞：高頻數(shù)據(jù)下的金融波動率研究　出處：《西北師范大學》2013年碩士論文　論文類型：學位論文

  更多相關文章： 高頻數(shù)據(jù) 波動率 Bayes估計 自加權積分波動 Ito半鞅

【摘要】：在金融領域,不確定的風險即資產(chǎn)收益過程波動是市場逐利的根源,也是金融市場活躍的根本所在.資產(chǎn)收益過程在市場環(huán)境中受種種因素影響客觀上形成不確定的波動,無論是出于逐利的目的或是資產(chǎn)保值的目的,人們總希望能夠?qū)磳⒌絹淼牟▌幼鞒鲎顬槔硐氲念A測,尤其在當今,利用先進的計算機技術和存儲技術使得高頻金融數(shù)據(jù)越來越容易得到,人們更希望使用對金融市場細節(jié)刻畫更為細膩的高頻金融數(shù)據(jù)對波動率作出更好的預測.因此,高頻金融數(shù)據(jù)下對資產(chǎn)價格波動的研究越來越為人們所關注. 本文的主要工作和創(chuàng)新之處可以概括如下： 1.本文針對”日歷效應”對波動率的影響這一問題,提出自加權波動的概念,通過自加權函數(shù)f來消除”日歷效應”對波動率的影響,從而更準確地描述實際波動的特征.并從理論上證明了自加權積分波動率的極限性質(zhì),對波動率的應用具有指導意義. 2.本文提出用Bayes估計的方法對金融高頻數(shù)據(jù)的波動率進行估計.通過利用以往信息,提出參數(shù)的先驗分布,并對不同損失函數(shù)下的估計結果進行計算比較,從而得出一合理的估計結果. 3.在以往高頻數(shù)據(jù)的研究中,觀測時間點的選取往往都是等間距的選取.此種方法,首先并不能完全記錄高頻數(shù)據(jù)的變化趨勢；其次,等間隔的時間點選取并沒有考慮“日歷效應”等對波動率的影響.本文針對以上問題,運用并改進了非等間隔選取觀測時間點的方法.從而最大限度地保持原有數(shù)據(jù)的特性,使得最終估計出的波動率更加準確可靠. 4.金融高頻數(shù)據(jù)中存在著微觀結構噪聲,并且微觀結構誤差隨抽樣頻率的增加而增大.本文選用兩個時間觀測序列,將微觀結構噪聲帶來的誤差從波動率估計量中消除掉,從而使估計出的波動率更加真實可靠.
[Abstract]:In the field of finance, the uncertain risk, that is, the volatility of asset return process, is the root of market profit-seeking. Asset income process is affected by various factors in the market environment to form an objective uncertainty of fluctuations, whether out of the purpose of profit-seeking or the purpose of asset preservation. People always want to be able to make the most ideal prediction of the coming fluctuations, especially in today's, the use of advanced computer technology and storage technology to make high-frequency financial data more and more easily available. People prefer to use the finer high-frequency financial data to predict volatility. Therefore, the research on asset price volatility under the high-frequency financial data has attracted more and more attention. The main work and innovations of this paper can be summarized as follows: 1. Aiming at the influence of "calendar effect" on volatility, the concept of self-weighted volatility is proposed in this paper, and the influence of "calendar effect" on volatility is eliminated by self-weighting function f. The limit property of self-weighted integral volatility is proved theoretically, which is of guiding significance to the application of volatility. 2. In this paper, the Bayes estimation method is proposed to estimate the volatility of high-frequency financial data, and a priori distribution of the parameters is proposed by using the previous information. The estimation results under different loss functions are calculated and compared, and a reasonable estimation result is obtained. 3. In the previous research of high frequency data, the choice of observation time point is often equal distance selection. This method can not record the change trend of high frequency data completely. Secondly, the effect of "calendar effect" on volatility is not taken into account in the selection of equal-interval time points. This paper applies and improves the method of selecting observation time points at non-equal intervals, so as to maintain the characteristics of the original data to the maximum extent and make the final estimated volatility more accurate and reliable. 4. There is microstructure noise in financial high frequency data, and the microstructure error increases with the increase of sampling frequency. In this paper, two time series are selected. The error caused by microstructural noise is eliminated from the volatility estimator, so that the estimated volatility is more true and reliable.
【學位授予單位】：西北師范大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F830.9;F224

【參考文獻】

相關期刊論文 前1條

1 唐勇,劉峰濤;金融市場波動測量方法新進展[J];華南農(nóng)業(yè)大學學報(社會科學版);2005年01期

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本文編號：1378670