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Modelling volatility in financial ma...

University of Toronto (Canada).

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Modelling volatility in financial markets.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modelling volatility in financial markets./
Author:	Liu, Chun.
Description:	123 p.
Notes:	Source: Dissertation Abstracts International, Volume: 69-06, Section: A, page: 2390.
Contained By:	Dissertation Abstracts International69-06A.
Subject:	Economics, Finance. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR39470
ISBN:	9780494394700

Modelling volatility in financial markets.
Liu, Chun.

Modelling volatility in financial markets. - 123 p.

Source: Dissertation Abstracts International, Volume: 69-06, Section: A, page: 2390.

Thesis (Ph.D.)--University of Toronto (Canada), 2007.

In this thesis, I study the dynamics of the volatility process and focus on estimation and forecasting. Recent research uses high frequency intraday data to construct ex post measures of daily volatility including realized volatility (RV). Chapter 1 is the introduction. In Chapter 2, I use a Bayesian approach to investigate the evidence for structural breaks in reduced form time-series models of RV. I focus on the popular heterogeneous autoregressive (HAR) models of the logarithm of realized volatility. Using Monte Carlo simulations I demonstrate that the estimation approach is effective in identifying and dating structural breaks. Applied to daily S&P 500 data, I find strong evidence of a single structural break in log(RV). The main effect of the break is on the long-run mean and variance of log-volatility.

ISBN: 9780494394700Subjects--Topical Terms:

626650
Economics, Finance.

Modelling volatility in financial markets.
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100 1 $a Liu, Chun. $3 1029761
245 1 0 $a Modelling volatility in financial markets.
300 $a 123 p.
500 $a Source: Dissertation Abstracts International, Volume: 69-06, Section: A, page: 2390.
502 $a Thesis (Ph.D.)--University of Toronto (Canada), 2007.
520 $a In this thesis, I study the dynamics of the volatility process and focus on estimation and forecasting. Recent research uses high frequency intraday data to construct ex post measures of daily volatility including realized volatility (RV). Chapter 1 is the introduction. In Chapter 2, I use a Bayesian approach to investigate the evidence for structural breaks in reduced form time-series models of RV. I focus on the popular heterogeneous autoregressive (HAR) models of the logarithm of realized volatility. Using Monte Carlo simulations I demonstrate that the estimation approach is effective in identifying and dating structural breaks. Applied to daily S&P 500 data, I find strong evidence of a single structural break in log(RV). The main effect of the break is on the long-run mean and variance of log-volatility.
520 $a Chapter 3 uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include HAR specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and leverage term. The Bayesian model averaging provides very competitive density forecasts and consistent but modest improvements in point forecasts over the benchmarks. Applied to equity and exchange rate volatility over several forecast horizons, the Bayesian model averaging provides the best performance compared to the benchmarks including HAR, AR and simple model averaging models. I discuss the reasons for this, including the importance of using realized power variation as a predictor.
520 $a In the last chapter, I propose a new joint model of volatility and duration in high frequency framework using tick-by-tick data. This model decomposes the conditional variance into different volatility components associated with different transaction horizons. Using stock market data, I demonstrate its superiority over the traditional GARCH counterpart. In addition, I show that a fat-tailed t-distribution for return innovations and a Burr distribution for duration innovations improve density forecasts, compared with normal and exponential distribution, respectively.
590 $a School code: 0779.
650 4 $a Economics, Finance. $3 626650
690 $a 0508
710 2 $a University of Toronto (Canada). $3 1017674
773 0 $t Dissertation Abstracts International $g 69-06A.
790 $a 0779
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR39470