一種基于小波的高頻數(shù)據(jù)降噪和跳躍信息準則
發(fā)布時間：2018-08-05 20:54
【摘要】：檢驗高頻金融數(shù)據(jù)跳躍點和研究它的波動性在應用中是必要的,例如衍生品定價和風險管理。雖然近些年學者們提出大量的檢驗跳躍的方法,但這些方法依賴跳躍點數(shù)量已知或多重假設檢驗。這導致了這些檢驗方法表現(xiàn)出不穩(wěn)健性以及在實證研究中檢驗出虛假的跳躍點。另外降噪算法可以清洗數(shù)據(jù)并估計系統(tǒng)整體趨勢,因此對有跳躍點的高頻金融數(shù)據(jù)做降噪處理也是研究中很重要的一部分�；诰植烤�性尺度逼近(LLSA)和極大重疊離散小波變換(MODWT),本文提出了基于小波的跳躍信息準則(WJIC),它可以同時對數(shù)據(jù)降噪和識別跳躍點,并且我們構造得分函數(shù)優(yōu)化選擇參數(shù)。我們通過模擬實驗對比WJIC和其它方法的表現(xiàn),并且把我們的算法應用到美國全國證券交易商協(xié)會自動報價表(NASDAQ)。我們證明了 WJIC得到的估計量有良好的漸近性質,模擬及實證研究表明了 WJIC得到的估計量在數(shù)值計算中表現(xiàn)很好。
[Abstract]:It is necessary to examine the jump point of high-frequency financial data and study its volatility in applications such as derivatives pricing and risk management. Although a large number of test methods have been proposed in recent years, these methods depend on the number of hopping points known or multiple hypothesis tests. This leads to the unsoundness of these testing methods and the testing of false jump points in empirical research. In addition, the noise reduction algorithm can clean the data and estimate the overall trend of the system, so it is also an important part of the research to reduce the noise of the high-frequency financial data with jump points. Based on local linear scale approximation (LLSA) and maximal overlapping discrete wavelet transform (MODWT), this paper presents a hopping information criterion (WJIC),) based on wavelet, which can reduce data noise and identify jump points simultaneously, and we construct a score function to optimize the selection of parameters. We compare the performance of WJIC and other methods through simulation experiments, and apply our algorithm to the National Association of Securities Dealers automatic quotation form (NASDAQ). We prove that the estimator obtained by WJIC has good asymptotic property, and the simulation and empirical research show that the estimator obtained by WJIC performs well in numerical calculation.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F831.51
本文編號：2167004
[Abstract]:It is necessary to examine the jump point of high-frequency financial data and study its volatility in applications such as derivatives pricing and risk management. Although a large number of test methods have been proposed in recent years, these methods depend on the number of hopping points known or multiple hypothesis tests. This leads to the unsoundness of these testing methods and the testing of false jump points in empirical research. In addition, the noise reduction algorithm can clean the data and estimate the overall trend of the system, so it is also an important part of the research to reduce the noise of the high-frequency financial data with jump points. Based on local linear scale approximation (LLSA) and maximal overlapping discrete wavelet transform (MODWT), this paper presents a hopping information criterion (WJIC),) based on wavelet, which can reduce data noise and identify jump points simultaneously, and we construct a score function to optimize the selection of parameters. We compare the performance of WJIC and other methods through simulation experiments, and apply our algorithm to the National Association of Securities Dealers automatic quotation form (NASDAQ). We prove that the estimator obtained by WJIC has good asymptotic property, and the simulation and empirical research show that the estimator obtained by WJIC performs well in numerical calculation.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：F831.51
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