Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts

Bo Zhang, Joshua Chan and Jamie Cross

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

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 (search for similar items in EconPapers)
JEL-codes: C11 C52 C53 E37 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2018-06
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-ore
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Citations: View citations in EconPapers (7)

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Related works:
Journal Article: Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2018-32

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