Contributions to Short-Term Financial Risk Management
Volatility in High Frequency Data, Levy Processes and the Dependence of Jumps
1st Edition
Published in November 2008
Book
Paperback/Softback
140 pages
978-3-86582-778-4 (ISBN)
Description
This thesis presents instruments and methodologies for fi nancial
risk management applications:
A method of estimating instantaneous volatility from transaction
data is developed. It explicitly accounts for microstructure noise.
Furthermore, an econometric method is introduced which
copes easily with short-term patterns in time series such as the
intraday volatility patterns.
Regarding extreme events, important aspects of Lévy processes
are discussed. A univariate approximation of Student Lévy processes is developed. In the context of multivariate Lévy processes, a modified, unbiased simulation algorithm is presented.
The concept of jump tail dependence is discussed, which
is a property of the Lévy copula. Especially on the short-term
horizon, it is of special relevance for optimal asset allocation.
Asymptotical results are derived, which allow for the estimation
of jump tail dependence.
risk management applications:
A method of estimating instantaneous volatility from transaction
data is developed. It explicitly accounts for microstructure noise.
Furthermore, an econometric method is introduced which
copes easily with short-term patterns in time series such as the
intraday volatility patterns.
Regarding extreme events, important aspects of Lévy processes
are discussed. A univariate approximation of Student Lévy processes is developed. In the context of multivariate Lévy processes, a modified, unbiased simulation algorithm is presented.
The concept of jump tail dependence is discussed, which
is a property of the Lévy copula. Especially on the short-term
horizon, it is of special relevance for optimal asset allocation.
Asymptotical results are derived, which allow for the estimation
of jump tail dependence.