<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horta, Eduardo</style></author><author><style face="normal" font="default" size="100%">Flavio Ziegelmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Conjugate processes: Theory and application to risk forecasting</style></title><secondary-title><style face="normal" font="default" size="100%">Stochastic Processes and their Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">128</style></volume><pages><style face="normal" font="default" size="100%">727-755</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Many dynamical phenomena display a cyclic behavior, in the sense that time can be partitioned into units within which distributional aspects of a process are homogeneous. In this paper, we introduce a class of models – called conjugate processes – allowing the sequence of marginal distributions of a cyclic, continuous-time process to evolve stochastically in time. The connection between the two processes is given by a fundamental compatibility equation. Key results include Laws of Large Numbers in the presented framework. We provide a constructive example which illustrates the theory, and give a statistical implementation to risk forecasting in financial data.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>