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Statistical Methods for High Frequen...

Zhang, Xin.

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Statistical Methods for High Frequency Financial Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Methods for High Frequency Financial Data./
作者:	Zhang, Xin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	99 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
Contained By:	Dissertation Abstracts International78-09B(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10276285
ISBN:	9781369731729

Statistical Methods for High Frequency Financial Data.
Zhang, Xin.

Statistical Methods for High Frequency Financial Data. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 99 p.

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.

This dissertation work focuses on developing statistical methods for volatility estimation and prediction with high frequency financial data. We consider two kinds of volatility: integrated volatility and jump variation.

ISBN: 9781369731729Subjects--Topical Terms:

517247
Statistics.

Statistical Methods for High Frequency Financial Data.
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300 $a 99 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
500 $a Adviser: Yazhen Wang.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.
520 $a This dissertation work focuses on developing statistical methods for volatility estimation and prediction with high frequency financial data. We consider two kinds of volatility: integrated volatility and jump variation.
520 $a In the first part, we introduce the methods for integrated volatility estimation with the presence of microstructure noise. We will first talk about the optimal sampling frequency for integrated volatility estimation since subsampling is very popular in practice. Then we will discuss about those methods based on subsampling. Two-scale estimator is developed using the subsampling idea while taking advantage of all of the data. An extension to the multi-scale further improves the efficiency of the estimation.
520 $a In the second part, we propose a heterogenous autoregressive model for the integrated volatility estimators based on subsampling. An empirical approach is to estimate integrated volatility using high frequency data and then fit the estimates to a low frequency heterogeneous autoregressive volatility model for prediction. We provide some theoretical justifications for the empirical approach by showing that these estimators approximately obey a heterogenous autoregressive model for some appropriate underlying price and volatility processes.
520 $a In the third part, we propose a method for jump variation estimation using wavelet techniques. Previously, jumps are not assumed in the model. In this part, we will concentrate on jump variation estimation and there- fore, we will be able to estimate the integrated volatility and jump variation individually. We show that by choosing a threshold, we will be able to detect the jump location, and by using the realized volatility processes instead of the original price process, we will be able to improve the convergence rate of estimation. We include both numerical and empirical results of this method.
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