Forecasting macroeconomic volatility with score-driven models - Massimiliano Giacalone, Raffaele Mattera - Vita e Pensiero - Articolo Statistica & Applicazioni

Forecasting macroeconomic volatility with score-driven models

newdigital Forecasting macroeconomic volatility with score-driven models
Article
journal STATISTICA & APPLICAZIONI
issue STATISTICA & APPLICAZIONI - 2020 - 2
title Forecasting macroeconomic volatility with score-driven models
Generalized Autoregressive Score, Gross Domestic Product, GARCH, Model Confidence Set, Skewed Generalized Error Distribution.
authors
publisher Vita e Pensiero
format Article | Pdf
online since 07-2021
doi 10.26350/999999_000037
issn 1824-6672 (print) | 2283-6659 (digital)
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Business cycle volatility has been extensively studied by means of the well-known ARCH and GARCH processes. Aim of this paper is to show that the score-driven models are instead more accurate in predicting business cycle volatility than the GARCH-type models. Motivated by fact that the empirical evidence do not support the hypothesis of Gaussianity also for business cycles, we assume the Generalized Error Distribution and its extension for skewness in estimating the volatility models within the GARCH framework. After reviewing the basic properties of the score-driven approaches, we carry out an empirical analysis with respect to the business cycles of the United States and Japan. We show that the score-driven models provide superior performances than both Gaussian and non-Gaussian GARCH processes in forecasting business cycle volatility.

keywords

Generalized Autoregressive Score, Gross Domestic Product, GARCH, Model Confidence Set, Skewed Generalized Error Distribution.

Authors biography

Dipartimento di Economia e Statistica - Universita` di Napoli ‘‘Federico II’’ - via Cintia, Monte Sant’Angelo Complex, 80126, NAPOLI (e-mail: massimiliano.giacalone@unina.it;
raffaele.mattera@unina.it).

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