Title: Gamma-driven Markov processes with application to realized volatility Authors:  Fernanda Mendes - Universidade Federal de Minas Gerais (Brazil)
Wagner Barreto-Souza - University College Dublin (Ireland) [presenting]
Sokol Ndreca - Universidade Federal de Minas Gerais (Brazil)
Abstract: A novel class of Markov processes is proposed for dealing with continuous positive time series data, which is constructed based on a latent gamma effect and named gamma-driven (GD) models. The GD processes possess desirable properties and features: (i) it can produce any desirable invariant distribution with support on R+, (ii) it is time-reversible, and (iii) it has the transition density function given in an explicit form. Estimation of parameters is performed through the maximum likelihood method combined with a Gauss Laguerre quadrature to approximate the likelihood function. The evaluation of the estimators and also confidence intervals of parameters are explored via Monte Carlo simulation studies. Two generalizations of the GD processes are also proposed to handle non-stationary and long-memory time series. The proposed methodologies are applied to analyze the daily realized volatility of the FTSE 100 equity index.