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Statistical Inferences on High-Frequ...

Kim, Donggyu.

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Statistical Inferences on High-Frequency Financial Data and Quantum State Tomography.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Inferences on High-Frequency Financial Data and Quantum State Tomography./
Author:	Kim, Donggyu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	382 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
Contained By:	Dissertation Abstracts International77-12B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10147354
ISBN:	9781369025798

Statistical Inferences on High-Frequency Financial Data and Quantum State Tomography.
Kim, Donggyu.

Statistical Inferences on High-Frequency Financial Data and Quantum State Tomography. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 382 p.

Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2016.

In this dissertation, we study two topics, the volatility analysis based on the high-frequency financial data and quantum state tomography.

ISBN: 9781369025798Subjects--Topical Terms:

517247
Statistics.

Statistical Inferences on High-Frequency Financial Data and Quantum State Tomography.
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100 1 $a Kim, Donggyu. $3 3342157
245 1 0 $a Statistical Inferences on High-Frequency Financial Data and Quantum State Tomography.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 382 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-12(E), Section: B.
500 $a Adviser: Yazhen Wang.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2016.
520 $a In this dissertation, we study two topics, the volatility analysis based on the high-frequency financial data and quantum state tomography.
520 $a In Part I, we study the volatility analysis based on the high-frequency financial data. We first investigate how to estimate large volatility matrices effectively and efficiently. For example, we introduce threshold rules to regularize kernel realized volatility, pre-averaging realized volatility, and multi-scale realized volatility. Their convergence rates are derived under sparsity on the large integrated volatility matrix. To account for the sparse structure well, we employ the factor-based Ito processes and under the proposed factor-based model, we develop an estimation scheme called "blocking and regularizing". Also, we establish a minimax lower bound for the eigenspace estimation problem and propose sparse principal subspace estimation methods by using the multi-scale realized volatility matrix estimator or the pre-averaging realized volatility matrix estimator. Finally, we introduce a unified model, which can accommodate both continuous-time Ito processes used to model high-frequency stock prices and GARCH processes employed to model low-frequency stock prices, by embedding a discrete-time GARCH volatility in its continuous-time instantaneous volatility. We adopt realized volatility estimators based on high-frequency financial data and the quasi-likelihood function for the low-frequency GARCH structure to develop parameter estimation methods for the combined high-frequency and low-frequency data.
520 $a In Part II, we study the quantum state tomography with Pauli measurements. In the quantum science, the dimension of the quantum density matrix usually grows exponentially with the size of the quantum system, and thus it is important to develop effective and efficient estimation methods for the large quantum density matrices. We study large density matrix estimation methods and obtain the minimax lower bound under some sparse structures, for example, (i) the coefficients of the density matrix with respect to the Pauli basis are sparse; (ii) the rank is low; (iii) the eigenvectors are sparse. Their performances may depend on the sparse structure, and so it is essential to choose appropriate estimation methods according to the sparse structure. In light of this, we study how to conduct hypothesis tests for the sparse structure. Specifically, we propose hypothesis test procedures and develop central limit theorems for each test statistics. A simulation study is conducted to check the finite sample performances of proposed estimation methods and hypothesis tests.
590 $a School code: 0262.
650 4 $a Statistics. $3 517247
650 4 $a Finance. $3 542899
650 4 $a Quantum physics. $3 726746
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690 $a 0599
710 2 $a The University of Wisconsin - Madison. $b Statistics. $3 2101047
773 0 $t Dissertation Abstracts International $g 77-12B(E).
790 $a 0262
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10147354