This book focuses on general frameworks for modeling heavy-tailed distributions in economics, finance, econometrics, statistics, risk management and insurance. A central theme is that of (non-)robustness, i.e., the fact that the presence of heavy tails can either reinforce or reverse the implications of a number of models in these fields, depending on the degree of heavy-tailedness. These results motivate the development and applications of robust inference approaches under heavy tails, heterogeneity and dependence in observations. Several recently developed robust inference approaches are discussed and illustrated, together with applications
tistical techniques are based on the assumption that the random variables are normally distributed. ...
International audienceThe estimation of extreme quantiles requires adapted methods to extrapolate be...
This textbook highlights the many practical uses of stable distributions, exploring the theory, nume...
The aim of this thesis is to show that the use of heavy-tailed distributions in finance is theoretic...
This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model...
Many of the concepts in theoretical and empirical finance developed over the past decades – includin...
ABSTRACT The structure of many models in economics depends on majorization properties of convolution...
The recent financial and economic crises have shown the dangers of assuming that the risks are nearl...
Abstract. Since the work of Mandelbrot in the 1960’s there has accumu-lated a great deal of empirica...
This chapter is devoted to the parametric statistical distributions of economic size phenomena of va...
Optimization problems depending on a probability measure correspond to many economic and financial a...
Traditionally, in science and engineering, most statistical techniques are based on the assumption t...
This title is written for the numerate nonspecialist, and hopes to serve three purposes. First it ga...
Typically, in constructing a model for a random variable, one utilizes available samples to construc...
This thesis develops novel Bayesian methodologies for statistical modelling of heavy-tailed data. H...
tistical techniques are based on the assumption that the random variables are normally distributed. ...
International audienceThe estimation of extreme quantiles requires adapted methods to extrapolate be...
This textbook highlights the many practical uses of stable distributions, exploring the theory, nume...
The aim of this thesis is to show that the use of heavy-tailed distributions in finance is theoretic...
This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model...
Many of the concepts in theoretical and empirical finance developed over the past decades – includin...
ABSTRACT The structure of many models in economics depends on majorization properties of convolution...
The recent financial and economic crises have shown the dangers of assuming that the risks are nearl...
Abstract. Since the work of Mandelbrot in the 1960’s there has accumu-lated a great deal of empirica...
This chapter is devoted to the parametric statistical distributions of economic size phenomena of va...
Optimization problems depending on a probability measure correspond to many economic and financial a...
Traditionally, in science and engineering, most statistical techniques are based on the assumption t...
This title is written for the numerate nonspecialist, and hopes to serve three purposes. First it ga...
Typically, in constructing a model for a random variable, one utilizes available samples to construc...
This thesis develops novel Bayesian methodologies for statistical modelling of heavy-tailed data. H...
tistical techniques are based on the assumption that the random variables are normally distributed. ...
International audienceThe estimation of extreme quantiles requires adapted methods to extrapolate be...
This textbook highlights the many practical uses of stable distributions, exploring the theory, nume...