Stochastic Volatility Models with ARMA Innovations: An Application to G7 Inflation Forecasts

Authors

Bo Zhang, University of WollongongFollow
Joshua Chan, University of Technology Sydney
Jamie L. Cross, Australian National University

RIS ID

133762

Publication Details

Zhang, B., Chan, J. C. C. & Cross, J. L. (2018). Stochastic Volatility Models with ARMA Innovations: An Application to G7 Inflation Forecasts. CAMA Working Paper Series, 32 1-32.

Abstract

We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial dependence which renders standard Kalman filter techniques not directly applicable. To overcome this hurdle we develop an efficient posterior simulator that builds on recently developed precision based algorithms. We assess the usefulness of these new models in an inflation forecasting exercise across all G7 economies. We find that the new models generally provide competitive point and density forecasts compared to standard benchmarks, and are especially useful for Canada, France, Italy and the US.