東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Essays on High-Frequency Financial D...

Dong, Yingjie.

FindBook

Google Book

Amazon

博客來

Essays on High-Frequency Financial Data Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays on High-Frequency Financial Data Analysis./
作者:	Dong, Yingjie.
面頁冊數:	146 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-02(E), Section: A.
Contained By:	Dissertation Abstracts International77-02A(E).
標題:	Economic theory. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3732146
ISBN:	9781339188416

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

Essays on High-Frequency Financial Data Analysis. - 146 p.

Source: Dissertation Abstracts International, Volume: 77-02(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: 9781339188416Subjects--Topical Terms:

1556984
Economic theory.

Essays on High-Frequency Financial Data Analysis.
LDR:03440nmm a2200313 4500 001 2067944
005 20160418082956.5
008 170521s2015 ||||||||||||||||| ||eng d
020 $a 9781339188416
035 $a (MiAaPQ)AAI3732146
035 $a AAI3732146
040 $a MiAaPQ $c MiAaPQ
100 1 $a Dong, Yingjie. $3 3182817
245 1 0 $a Essays on High-Frequency Financial Data Analysis.
300 $a 146 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-02(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 highfrequency 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 Economic theory. $3 1556984
650 4 $a Finance. $3 542899
690 $a 0511
690 $a 0508
710 2 $a Singapore Management University (Singapore). $3 3180578
773 0 $t Dissertation Abstracts International $g 77-02A(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=3732146