Stochastic Volatility Models with ARMA Innovations an Application to G7 Inflation Forecasts

Zhang B, Chan JCC, Cross JL, June 2018, Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts paper no. 32/2018.

31st Australasian Finance and Banking Conference 2018

32 Pages Posted: 30 Jul 2018

See all articles by Bo Zhang

Bo Zhang

School of Accounting, Economics and Finance ; Australian National University (ANU) - College of Business and Economics

Joshua C. C. Chan

University of Technology Sydney (UTS) - UTS Business School; Purdue University

Jamie Cross

The Australian National University

Date Written: June 30, 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 C. C. and Cross, Jamie, Stochastic Volatility Models with ARMA Innovations an Application to G7 Inflation Forecasts (June 30, 2018). Zhang B, Chan JCC, Cross JL, June 2018, Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts paper no. 32/2018.; 31st Australasian Finance and Banking Conference 2018. Available at SSRN: https://ssrn.com/abstract=3222423 or http://dx.doi.org/10.2139/ssrn.3222423

Bo Zhang (Contact Author)

School of Accounting, Economics and Finance ( email )

Northfields Avenue
Wollongong, New South Wales 2522
Australia

Australian National University (ANU) - College of Business and Economics ( email )

Canberra
Australia

Joshua C. C. Chan

University of Technology Sydney (UTS) - UTS Business School ( email )

Sydney
Australia

Purdue University

West Lafayette, IN 47907-1310
United States

Jamie Cross

The Australian National University ( email )

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