SpringerBriefs in Statistics

Convolution Copula Econometrics

Authors: Cherubini, Umberto, Gobbi, Fabio, Mulinacci, Sabrina

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  • 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
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About this book

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. 

About the authors

Umberto Cherubini is Associate professor of Financial Mathematics at the University of Bologna, where he heads the graduate program in Quantitative Finance. He is fellow of the Financial Econometrics Research Center (FERC), a member of the Scientific Committees of Abiformazione – the professional education arm of the Italian Banking Association, and AIFIRM – the Italian Association of Financial Risk Managers. He has been consulting and teaching in the field of finance and risk management for almost twenty years. Before joining academia he worked as an economist at the Economic Research Department of BCI Milan. He has published papers on finance and economics in international journals, and is a co-author of seven books on topics of risk management and financial mathematics, with special focus on the copula function technique.    

Fabio Gobbi is a post-doctoral researcher at the University of Bologna. He has a PhD in Statistics from the University of Florence and his area of research focuses on probability and financial econometrics. He is a co-author (with Umberto Cherubini and Sabrina Mulinacci) of the recent book Dynamic Copula Methods in Finance, the first book to introduce the theory of convolution-based copulas and the concept of C-convolution within the mainstream of the Darsow, Nguyen and Olsen (DNO) application of copulas to Markov processes. 

Sabrina Mulinacci is Associate Professor of Mathematical Methods for Economics and Finance at the University of Bologna. Prior to this, Sabrina was Associate Professor of Mathematical Methods for Economics and Actuarial Sciences at the Catholic University of Milan. She has a PhD in Mathematics from the University of Pisa and has published a number of research papers in international journals on probability and mathematical finance.

Reviews

“The goal of the book is to gather the main concepts of copula function theory that can be applied to the analysis of time series (so-called convolution-based copulas), and some new ideas, linked to copulas, such as estimation of copula-based Markov processes. … The book will be useful for the researchers working in econometrics, interest rate, Markov processes and copulas fields.” (Anatoliy Swishchuk, zbMATH 1360.62006, 2017)


Table of contents (5 chapters)

Table of contents (5 chapters)

Buy this book

eBook $54.99
price for USA (gross)
  • ISBN 978-3-319-48015-2
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $69.99
price for USA
  • ISBN 978-3-319-48014-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Convolution Copula Econometrics
Authors
Series Title
SpringerBriefs in Statistics
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
The Author(s)
eBook ISBN
978-3-319-48015-2
DOI
10.1007/978-3-319-48015-2
Softcover ISBN
978-3-319-48014-5
Series ISSN
2191-544X
Edition Number
1
Number of Pages
X, 90
Number of Illustrations
1 b/w illustrations, 30 illustrations in colour
Topics