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The predictive power of log-likelihood of GARCH volatility

Rangga Handika (Institute for International Strategy, Tokyo International University, Kawagoe, Japan)
Dony Abdul Chalid (Faculty of Economics and Business, Universitas Indonesia, Depok, Indonesia)

Review of Accounting and Finance

ISSN: 1475-7702

Article publication date: 13 November 2018

Issue publication date: 22 November 2018

313

Abstract

Purpose

This paper aims to investigate whether the best statistical model also corresponds to the best empirical performance in the volatility modeling of financialized commodity markets.

Design/methodology/approach

The authors use various p and q values in Value-at-Risk (VaR) GARCH(p, q) estimation and perform backtesting at different confidence levels, different out-of-sample periods and different data frequencies for eight financialized commodities.

Findings

They find that the best fitted GARCH(p,q) model tends to generate the best empirical performance for most financialized commodities. Their findings are consistent at different confidence levels and different out-of-sample periods. However, the strong results occur for both daily and weekly returns series. They obtain weak results for the monthly series.

Research limitations/implications

Their research method is limited to the GARCH(p,q) model and the eight discussed financialized commodities.

Practical implications

They conclude that they should continue to rely on the log-likelihood statistical criteria for choosing a GARCH(p,q) model in financialized commodity markets for daily and weekly forecasting horizons.

Social implications

The log-likelihood statistical criterion has strong predictive power in GARCH high-frequency data series (daily and weekly). This finding justifies the importance of using statistical criterion in financial market modeling.

Originality/value

First, this paper investigates whether the best statistical model corresponds to the best empirical performance. Second, this paper provides an indirect test for evaluating the accuracy of volatility modeling by using the VaR approach.

Keywords

Acknowledgements

We thank the participants at the ICOM 2016 conference at Abu Dhabi University, participants at ICBMR 2017 at Universitas Indonesia and anonymous referees who provide valuable comments and suggestions to improve the quality of our manuscript.

Citation

Handika, R. and Chalid, D.A. (2018), "The predictive power of log-likelihood of GARCH volatility", Review of Accounting and Finance, Vol. 17 No. 4, pp. 482-497. https://doi.org/10.1108/RAF-01-2017-0006

Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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