Title: "Imprecise input data and Value at Risk estimation"
         

DOI: 10.15224/978-1-63248-058-3-56
Page(s): 21 - 25
Authors: MICHAL HOLCAPEK    , TOMAS TICHY   

Abstract

During last decades the stochastic simulation approach, both via MC and QMC has been vastly applied and subsequently analyzed in almost all branches of science. Very nice applications can be found in areas that rely on modeling via stochastic processes, such as finance. However, since financial quantities -- opposed to natural processes -- depend on human activity, their modeling is often very challenging. Many scholars therefor suggest to specify some parts of financial models by means of fuzzy set theory. In this paper we formulate a fuzzystochastic model to be solved by Monte Carlo simulation. The application possibility is shown on the case of Value at Risk estimation of a single position, though it can be easily generalized to deal with more complex problem.