Publication Type

PhD Dissertation

Publication Date

2015

Abstract

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. 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. 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. 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.

Keywords

high-frequency data, integrated volatility, time transformation function, noise-to-signal ratio

Degree Awarded

PhD in Economics

Discipline

Economics | Finance

Supervisor(s)

TSE, Yiu Kuen

First Page

1

Last Page

137

Publisher

Singapore Management University

City or Country

Singapore

Copyright Owner and License

Author

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Included in

Finance Commons

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