Structured Dependence between Stochastic Processes

Description

The relatively young theory of structured dependence between stochastic processes has many real-life applications in areas including finance, insurance, seismology, neuroscience, and genetics. With this monograph, the first to be devoted to the modeling of structured dependence between random processes, the authors not only meet the demand for a solid theoretical account but also develop a stochastic processes counterpart of the classical copula theory that exists for finite-dimensional random variables. Presenting both the technical aspects and the applications of the theory, this is a valuable reference for researchers and practitioners in the field, as well as for graduate students in pure and applied mathematics programs. Numerous theoretical examples are included, alongside examples of both current and potential applications, aimed at helping those who need to model structured dependence between dynamic random phenomena.

Tomasz R. Bielecki is Professor of Applied Mathematics at the Illinois Institute of Technology, Chicago. He co-authored Credit Risk: Modelling, Valuation and Hedging (2002), Credit Risk Modelling (2010) and Counterparty Risk and Funding (2014), and he currently serves as an associate editor of several journals, including Stochastics: An International Journal of Probability and Stochastic Processes. Jacek Jakubowski is Professor of Mathematics at the University of Warsaw. He is the author of numerous research papers in the areas of functional analysis, probability theory, stochastic processes, stochastic analysis, and mathematical finance, and he has co-authored several books in Polish, including Introduction to Probability Theory (2000), which is now in its fourth edition. Mariusz Nieweglowski is currently an Assistant Professor in the Faculty of Mathematics and Information Science at Warsaw University of Technology. The areas of his current research include financial mathematics with a focus on credit risk and stochastic analysis with a focus on modeling of dependence between stochastic processes.