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

34 Pages Posted: 3 Jul 2018

See all articles by Bo Zhang

Bo Zhang

Research School of Economics, ANU

Joshua Chan

University of Technology Sydney (UTS)

Jamie Cross

The Australian National University

Date Written: June 28, 2018

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.

Keywords: autoregressive moving average errors, stochastic volatility, inflation forecast, state space models, unobserved components model

JEL Classification: C11, C52, C53, E37

Suggested Citation

Zhang, Bo and Chan, Joshua and Cross, Jamie, Stochastic Volatility Models with ARMA Innovations: An Application to G7 Inflation Forecasts (June 28, 2018). CAMA Working Paper No. 32/2018. Available at SSRN: https://ssrn.com/abstract=3204882 or http://dx.doi.org/10.2139/ssrn.3204882

Bo Zhang

Research School of Economics, ANU ( email )

Canberra, Australian Capital Territory 2601
Australia

Joshua Chan (Contact Author)

University of Technology Sydney (UTS) ( email )

15 Broadway, Ultimo
PO Box 123
Sydney, NSW 2007
Australia

Jamie Cross

The Australian National University ( email )

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