A discussion on the robustness of conditional heteroskedasticity models: Simulation evidence and applications of the crude oil returns

Yanlin Shi

doi:10.1016/j.frl.2021.102053

A discussion on the robustness of conditional heteroskedasticity models: Simulation evidence and applications of the crude oil returns

Yanlin Shi^*

^*Corresponding author for this work

Department of Actuarial Studies and Business Analytics

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

This paper discusses the inherent robustness of the generalized autoregressive score (GAS) model, such that consistent estimators can still be obtained when large outliers do exist. A recent example includes the historical-writing negative price of the West Texas Intermediate (WTI) crude oil future in early-2020. Via simulation studies, we demonstrate that GAS can produce much more robust estimates than the popular GARCH model. Empirical analysis using the WTI returns over 2017–2020 further supports the superiority of GAS over GARCH models.

Original language	English
Article number	102053
Pages (from-to)	1-8
Number of pages	8
Journal	Finance Research Letters
Volume	44
Early online date	8 Apr 2021
DOIs	https://doi.org/10.1016/j.frl.2021.102053
Publication status	Published - Jan 2022

Keywords

Conditional volatility
Crude oil
GARCH
Generalized autoregressive score
Robustness

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abstract = "This paper discusses the inherent robustness of the generalized autoregressive score (GAS) model, such that consistent estimators can still be obtained when large outliers do exist. A recent example includes the historical-writing negative price of the West Texas Intermediate (WTI) crude oil future in early-2020. Via simulation studies, we demonstrate that GAS can produce much more robust estimates than the popular GARCH model. Empirical analysis using the WTI returns over 2017–2020 further supports the superiority of GAS over GARCH models.",

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AB - This paper discusses the inherent robustness of the generalized autoregressive score (GAS) model, such that consistent estimators can still be obtained when large outliers do exist. A recent example includes the historical-writing negative price of the West Texas Intermediate (WTI) crude oil future in early-2020. Via simulation studies, we demonstrate that GAS can produce much more robust estimates than the popular GARCH model. Empirical analysis using the WTI returns over 2017–2020 further supports the superiority of GAS over GARCH models.

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A discussion on the robustness of conditional heteroskedasticity models: Simulation evidence and applications of the crude oil returns

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Keywords

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