Convolution Copula Econometrics - Brossura

Cherubini, Umberto; Gobbi, Fabio; Mulinacci, Sabrina

Brossura

ISBN 10: 3319480146 ISBN 13: 9783319480145

Casa editrice: Springer, 2016

Riassunto

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. 

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Informazioni sull?autore

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.

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3319480146
• ISBN 13: 9783319480145
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 100
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile