This book deals with extreme value theory for univariate and multivariate time series models characterized by power-law tails. These include the classical ARMA models with heavy-tailed noise and financial econometrics models such as the GARCH and stochastic volatility models.

Rigorous descriptions of power-law tails are provided through the concept of regular variation. Several chapters are devoted to the exploration of regularly varying structures.

The remaining chapters focus on the impact of heavy tails on time series, including the study of extremal cluster phenomena through point process techniques.

A major part of the book investigates how extremal dependence alters the limit structure of sample means, maxima, order statistics, sample autocorrelations. 

This text illuminates the theory through hundreds of examples and as many graphs showcasing its applications to real-life financial and simulated data.

The book can serve as a text for PhD and Master courses on applied probability, extreme value theory, and time series analysis.

It is a unique reference source for the heavy-tail modeler. Its reference quality is enhanced by an exhaustive bibliography, annotated by notes and comments making the book broadly and easily accessible.

This book deals with extreme value theory for univariate and multivariate time series models characterized by power-law tails. These include the classical ARMA models with heavy-tailed noise and financial econometrics models such as the GARCH and stochastic volatility models.

Rigorous descriptions of power-law tails are provided through the concept of regular variation. Several chapters are devoted to the exploration of regularly varying structures.

The remaining chapters focus on the impact of heavy tails on time series, including the study of extremal cluster phenomena through point process techniques.

A major part of the book investigates how extremal dependence alters the limit structure of sample means, maxima, order statistics, sample autocorrelations. 

This text illuminates the theory through hundreds of examples and as many graphs showcasing its applications to real-life financial and simulated data.

The book can serve as a text for PhD and Master courses on applied probability, extreme value theory, and time series analysis.

It is a unique reference source for the heavy-tail modeler. Its reference quality is enhanced by an exhaustive bibliography, annotated by notes and comments making the book broadly and easily accessible.

This book deals with extreme value theory for univariate and multivariate time series models characterized by power-law tails. These include the classical ARMA models with heavy-tailed noise and financial econometrics models such as the GARCH and stochastic volatility models.

Rigorous descriptions of power-law tails are provided through the concept of regular variation. Several chapters are devoted to the exploration of regularly varying structures.

The remaining chapters focus on the impact of heavy tails on time series, including the study of extremal cluster phenomena through point process techniques.

A major part of the book investigates how extremal dependence alters the limit structure of sample means, maxima, order statistics, sample autocorrelations. 

This text illuminates the theory through hundreds of examples and as many graphs showcasing its applications to real-life financial and simulated data.

The book can serve as a text for PhD and Master courses on applied probability, extreme value theory, and time series analysis.

It is a unique reference source for the heavy-tail modeler. Its reference quality is enhanced by an exhaustive bibliography, annotated by notes and comments making the book broadly and easily accessible.