Bayesian nonlinear expectation for time series modelling and its application to Bitcoin.

ABSTRACT: This paper proposes a two-stage approach to parametric nonlinear time series modelling in discrete time with the objective of incorporating uncertainty or misspecification in the conditional mean and volatility. At the first stage, a reference or approximating time series model is specified and estimated. At the second stage, Bayesian nonlinear expectations are introduced to incorporate model uncertainty or misspecification in prediction via specifying a family of alternative models. The Bayesian nonlinear expectations for prediction are constructed from closed-form Bayesian credible intervals evaluated using conjugate priors and residuals of the estimated approximating model. Using real Bitcoin data including some periods of Covid 19, applications of the proposed method to forecasting and

SUBMITTER: Siu TK 

PROVIDER: S-EPMC9130704 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature