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
We review the notion of linearity of time series, and show that ARCH or stochastic volatility (SV) p...
We propose a set of dependence measures that are non-linear, local, invariant to a wide range of tra...
We consider a volatility model, named ARCH-NNH model, that is specifically an ARCH process with a no...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
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...
We examine the auto-dependence structure of strictly stationary solutions of linear stochastic recur...
Modeling the dependence between consecutive observations in a time series plays a crucial role in ri...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
In order to analyse the entire tail dependence structure among random variables in a multidimensiona...
In this thesis we model extreme log-returns on economic variables and apply this to Ortec Finance's ...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
With the advent of globalization and the recent financial turmoil, the interest for the analysis of ...
The fields of insurance and financial mathematics require increasingly intricate descriptors of depe...
We review several commonly used methods for estimating the tail dependence in a given data sample. I...
We review the notion of linearity of time series, and show that ARCH or stochastic volatility (SV) p...
We propose a set of dependence measures that are non-linear, local, invariant to a wide range of tra...
We consider a volatility model, named ARCH-NNH model, that is specifically an ARCH process with a no...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
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...
We examine the auto-dependence structure of strictly stationary solutions of linear stochastic recur...
Modeling the dependence between consecutive observations in a time series plays a crucial role in ri...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
In order to analyse the entire tail dependence structure among random variables in a multidimensiona...
In this thesis we model extreme log-returns on economic variables and apply this to Ortec Finance's ...
The thesis is composed of three parts. Part I introduces the mathematical and statistical tools that...
With the advent of globalization and the recent financial turmoil, the interest for the analysis of ...
The fields of insurance and financial mathematics require increasingly intricate descriptors of depe...
We review several commonly used methods for estimating the tail dependence in a given data sample. I...
We review the notion of linearity of time series, and show that ARCH or stochastic volatility (SV) p...
We propose a set of dependence measures that are non-linear, local, invariant to a wide range of tra...
We consider a volatility model, named ARCH-NNH model, that is specifically an ARCH process with a no...