Convolution Copula Econometrics

Authors: Umberto Cherubini, Fabio Gobbi, Sabrina Mulinacci



Provides ideas for further research in the field of time series analysis and copula functions

Presents an authoritative contribution on long memory features of macroeconomic and financial time series

Explores the use of convolution-based econometric tools for forecasting Markov processes

Features applications of the convolution-based technology such as tests of market efficiency

This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes. This approach allows for an arbitrary dependence structure in the increments and provides a generalization with respect to the standard linear independent increments assumption of classical time series models. The book offers a solution to the problem of a general semiparametric approach, which is given by a concept called C-convolution (convolution of dependent variables), and the corresponding theory of convolution-based copulas. Intended for econometrics and statistics scholars with a special interest in time series analysis and copula functions (or other nonparametric approaches), the book is also useful for doctoral students with a basic knowledge of copula functions wanting to learn about the latest research developments in the field.

Table of contents

Front Matter

The Dynamics of Economic Variables

Estimation of Copula Models

Copulas and Estimation of Markov Processes

Convolution-Based Processes

Application to Interest Rates

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