Add to ORKGWe study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and certain generalizations thereof, for both stationary and non-stationary time series. This study is inspired by financial risk-management issues, and our results are relevant for estimating probabilities of consecutive value-at-risk violations.
The purpose of this selective review is to present recent theoretical findings on the modelling of A...
We consider a volatility model, named ARCH-NNH model, that is specifically an ARCH process with a no...
Abstract: The presence of tail dependencies invalidates the multivariate normality assumptions in po...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
Serial dependence in non-linear time series cannot always be reliably quantified using linear autoco...
Modeling the dependence between consecutive observations in a time series plays a crucial role in ri...
We examine the auto-dependence structure of strictly stationary solutions of linear stochastic recur...
In this thesis we model extreme log-returns on economic variables and apply this to Ortec Finance's ...
The class of conditionally heteroskedastic models known as ‘aug-mented ARCH ’ encompasses most linea...
The ARCH model and its many generalizations are very important in analysing discrete time financial ...
In order to analyse the entire tail dependence structure among random variables in a multidimensiona...
We present several notions of high-level dependence for stochastic processes, which have appeared in...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
The autodependogram is a graphical device recently proposed in the literature to analyze autodepende...
Models characterizing the asymptotic dependence structures of bivariate distributions have been intr...
The purpose of this selective review is to present recent theoretical findings on the modelling of A...
We consider a volatility model, named ARCH-NNH model, that is specifically an ARCH process with a no...
Abstract: The presence of tail dependencies invalidates the multivariate normality assumptions in po...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
Serial dependence in non-linear time series cannot always be reliably quantified using linear autoco...
Modeling the dependence between consecutive observations in a time series plays a crucial role in ri...
We examine the auto-dependence structure of strictly stationary solutions of linear stochastic recur...
In this thesis we model extreme log-returns on economic variables and apply this to Ortec Finance's ...
The class of conditionally heteroskedastic models known as ‘aug-mented ARCH ’ encompasses most linea...
The ARCH model and its many generalizations are very important in analysing discrete time financial ...
In order to analyse the entire tail dependence structure among random variables in a multidimensiona...
We present several notions of high-level dependence for stochastic processes, which have appeared in...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
The autodependogram is a graphical device recently proposed in the literature to analyze autodepende...
Models characterizing the asymptotic dependence structures of bivariate distributions have been intr...
The purpose of this selective review is to present recent theoretical findings on the modelling of A...
We consider a volatility model, named ARCH-NNH model, that is specifically an ARCH process with a no...
Abstract: The presence of tail dependencies invalidates the multivariate normality assumptions in po...