Fuzzy coefficient volatility (FCV) models with applications

Fuzzy coefficient volatility (FCV) models with applications

Recently, Carlsson and Fuller [C. Carlsson, R. Fuller, On possibilistic mean value and variance of fuzzy numbers, Fuzzy Sets and Systems 122 (2001) 315–326] have introduced possibilistic mean, variance and covariance of fuzzy numbers and Fuller and Majlender [R. Fuller, P. Majlender, On weighted possibilistic mean and variance of fuzzy numbers, Fuzzy Sets and Systems 136 (2003) 363–374] have in...

Weighted possibilistic moments of fuzzy numbers with applications to GARCH modeling and option pricing

Carlsson and Fuller [C. Carlsson, R. Fuller, On possibilistic mean value and variance of fuzzy numbers, Fuzzy Sets and Systems 122 (2001) 315–326] have introduced possibilisticmean, variance and covariance of fuzzy numbers and Fuller andMajlender [R. Fuller, P. Majlender, On weighted possibilistic mean and variance of fuzzy numbers, Fuzzy Sets and Systems 136 (2003) 363–374] have introduced the...

Fuzzy clustering models and applications

Complete Models with Stochastic Volatility

The paper proposes an original class of models for the continuous time price process of a nancial security with non-constant volatility. The idea is to deene instantaneous volatility in terms of exponentially-weighted moments of historic log-price. The instantaneous volatility is therefore driven by the same stochastic factors as the price process, so that unlike many other models of non-consta...

A Class of Stochastic Volatility Models for Environmental Applications

Many environmental data sets have a continuous domain, in time and/or space, and complex features that may be poorly modeled with a stationary Gaussian process (GP). We adapt stochastic volatility modeling to this context, resulting in a stochastic heteroscedastic process (SHP), which is unconditionally stationary and non-Gaussian. Conditional on a latent GP, the SHP is a heteroscedastic GP wit...