Convolution Copula Econometrics
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 s
- PDF / 3,528,469 Bytes
- 99 Pages / 439.37 x 666.142 pts Page_size
- 74 Downloads / 212 Views
Umberto Cherubini Fabio Gobbi Sabrina Mulinacci
Convolution Copula Econometrics
SpringerBriefs in Statistics
More information about this series at http://www.springer.com/series/8921
Umberto Cherubini Fabio Gobbi Sabrina Mulinacci •
Convolution Copula Econometrics
123
Sabrina Mulinacci University of Bologna Bologna Italy
Umberto Cherubini University of Bologna Bologna Italy Fabio Gobbi University of Bologna Bologna Italy
ISSN 2191-544X SpringerBriefs in Statistics ISBN 978-3-319-48014-5 DOI 10.1007/978-3-319-48015-2
ISSN 2191-5458
(electronic)
ISBN 978-3-319-48015-2
(eBook)
Library of Congress Control Number: 2016955920 © The Author(s) 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
The mainstream of econometric research has been mostly devoted to linear models. Exceptions are mainly due to the need of addressing the issue of non-normality, and it has generally taken the form of assuming random jumps among different regimes describing different linear dynamics and different values of the parameters (typically again from a linear model) in different scenarios, to induce asymmetries in the distribution of variables or to model the tails properly. A notable exception to this general approach is the use of nonparametric tools in the specification of the dynamics of variables, and particularly the use of copula functions. This tool represents a natural way to address the non-normal distribution at the multivariate level, by separating a multivariate distribution in the specification of the marginal distributions and their dependence structure. Actually, copula functions have mostly been used for the study of cross-section dependence and they have become the dominant tool in fields like the analysis of credit risk for portfolios. There
Data Loading...
