Actuarial Science and Operations Seminar: Modelling volatility with v-transforms
Alexander McNeil, University of York, UK
Wednesday 29 January 2020 (11h00 - 12h00) - Extranef - 109
An approach to the modelling of financial return series using a class of transforms for uniform random variables is proposed. V-transforms describe the relationship between quantiles of the return distribution and quantiles of a positive-valued function of the return which acts as a predictable volatility proxy variable, such as the squared or absolute return. They allow the construction and estimation of models that combine arbitrary marginal distributions with linear or non-linear time series models for the dynamics of the volatility proxy. The idea is illustrated using a transfomed Gaussian ARMA process for volatility, yielding the class of VT-ARMA models. These can replicate many of the stylized facts of financial return series and facilitate the calculation of marginal and conditional characteristics of the model including quantile measures of risk. Estimation of models can be carried out by adapting the exact maximum likelihood approach to the estimation of ARMA processes.