Testing and Modeling Distributions of Returns and Volatility of Financial Assets
Author(s)
Zikes, Filip
Type
Thesis
Abstract
This thesis exploits the information contained in high-frequency data to test and
model the distributions of returns of financial assets and their volatility. In Chapter
1 we study the asymptotics of some common tests for normality when applied to returns
standardized by noise measures of volatility based on the use of high-frequency
data. Chapter 2 proposes dynamic models for conditional quantiles of daily returns
and realized volatility exploiting the information contained in various components of
historical volatility as well as option-implied volatility. Chapter 3 provides a comprehensive
simulation-based comparison of alternative tests for jumps in asset prices in
order to get a better understanding of the performance of the tests under different,
empirically relevant, scenarios. Chapter 4 extends the testing procedures studies in
Chapter 1 to the multivariate context and provides new empirical evidence about the
validity of the mixture of normals hypothesis in foreign exchange markets. Chapter
5 studies the dynamics of the tail risk in the hedge fund industry. Finally, Chapter 6
introduces a new method for estimating large covariance matrices.
model the distributions of returns of financial assets and their volatility. In Chapter
1 we study the asymptotics of some common tests for normality when applied to returns
standardized by noise measures of volatility based on the use of high-frequency
data. Chapter 2 proposes dynamic models for conditional quantiles of daily returns
and realized volatility exploiting the information contained in various components of
historical volatility as well as option-implied volatility. Chapter 3 provides a comprehensive
simulation-based comparison of alternative tests for jumps in asset prices in
order to get a better understanding of the performance of the tests under different,
empirically relevant, scenarios. Chapter 4 extends the testing procedures studies in
Chapter 1 to the multivariate context and provides new empirical evidence about the
validity of the mixture of normals hypothesis in foreign exchange markets. Chapter
5 studies the dynamics of the tail risk in the hedge fund industry. Finally, Chapter 6
introduces a new method for estimating large covariance matrices.
Date Issued
2011
Date Awarded
2011-04
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Distaso, Walter
Abadir, Karim
Creator
Zikes, Filip
Publisher Department
Imperial College Business School
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)