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Essays on High-Frequency Financial D...

Dong, Yingjie.

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Essays on High-Frequency Financial Data Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essays on High-Frequency Financial Data Analysis./
Author:	Dong, Yingjie.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	146 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-03(E), Section: A.
Contained By:	Dissertation Abstracts International78-03A(E).
Subject:	Economics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10169824
ISBN:	9781369239676

Essays on High-Frequency Financial Data Analysis.
Dong, Yingjie.

Essays on High-Frequency Financial Data Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 146 p.

Source: Dissertation Abstracts International, Volume: 78-03(E), Section: A.

Thesis (Ph.D.)--Singapore Management University (Singapore), 2015.

This dissertation consists of three essays on high-frequency financial data analysis. I consider intraday periodicity adjustment and its effect on intraday volatility estimation, the Business Time Sampling (BTS) scheme and the estimation of market microstructure noise using NYSE tick-by-tick transaction data.

ISBN: 9781369239676Subjects--Topical Terms:

517137
Economics.

Essays on High-Frequency Financial Data Analysis.
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245 1 0 $a Essays on High-Frequency Financial Data Analysis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2015
300 $a 146 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-03(E), Section: A.
500 $a Adviser: Yiu-Kuen Tse.
502 $a Thesis (Ph.D.)--Singapore Management University (Singapore), 2015.
520 $a This dissertation consists of three essays on high-frequency financial data analysis. I consider intraday periodicity adjustment and its effect on intraday volatility estimation, the Business Time Sampling (BTS) scheme and the estimation of market microstructure noise using NYSE tick-by-tick transaction data.
520 $a Chapter 2 studies two methods of adjusting for intraday periodicity of high-frequency financial data: the well-known Duration Adjustment (DA) method and the recently proposed Time Transformation (TT) method (Wu (2012)). I examine the effects of these adjustments on the estimation of intraday volatility using the Autoregressive Conditional Duration-Integrated Conditional Variance (ACD-ICV) method of Tse and Yang (2012). I find that daily volatility estimates are not sensitive to intraday periodicity adjustment. However, intraday volatility is found to have a weaker U-shaped volatility smile and a biased trough if intraday periodicity adjustment is not applied. In addition, adjustment taking account of trades with zero duration (multiple trades at the same time stamp) results in deeper intraday volatility smile.
520 $a Chapter 3 proposes a new method to implement the Business Time Sampling (BTS) scheme for high-frequency financial data using a time-transformation function. The sampled BTS returns have approximately equal volatility given a target average sampling frequency. My Monte Carlo results show that the Tripower Realized Volatility (TRV) estimates of daily volatility using the BTS returns produce smaller root mean-squared error than estimates using returns based on the Calendar Time Sampling (CTS) and Tick Time Sampling (TTS) schemes, with and without subsampling. Based on the BTS methodology I propose a modified ACD-ICV estimate of intraday volatility and find that this new method has superior performance over the Realized Kernel estimate and the ACD-ICV estimate based on sampling by price events.
520 $a Chapter 4 proposes new methods to estimate the noise variance of high-frequency stock returns using differences of subsampling realized variance estimates at two or multiple time scales. Noise-variance estimates are compared and the new proposed estimates perform the best in reporting lower mean error and root mean-squared error. This chapter shows significant estimation error of noise-variance estimates when transactions are selected at too high or too low frequencies. For a typical New York Stock Exchange stock, the noise-to-signal ratio is around 0.005% in the period from 2010 to 2013.
590 $a School code: 1583.
650 4 $a Economics. $3 517137
690 $a 0501
710 2 $a Singapore Management University (Singapore). $b Economics. $3 2096191
773 0 $t Dissertation Abstracts International $g 78-03A(E).
790 $a 1583
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10169824