Climate risks and stock market volatility over a century in an emerging market economy

Wu, Kejin; Karmakar, Sayar; Gupta, Rangan; Pierdzioch, Christian

doi:10.3390/cli12050068

Climate risks and stock market volatility over a century in an emerging market economy

The case of South Africa

Publication date

2024-05-08

Document type

Forschungsartikel

Author

Wu, Kejin

Karmakar, Sayar

Gupta, Rangan

Pierdzioch, Christian

Organisational unit

VWL, insb. Monetäre Ökonomik

DOI

10.3390/cli12050068

URI

https://openhsu.ub.hsu-hh.de/handle/10.24405/22215

Scopus ID

2-s2.0-85193985085

Publisher

MDPI

Series or journal

Climate

ISSN

2225-1154

Periodical volume

12

Periodical issue

5

Article ID

68

Is a version of

https://openhsu.ub.hsu-hh.de/handle/10.24405/19124

Peer-reviewed

✅

Part of the university bibliography

✅

Language

English

Keyword

climate risks

GARCH and GARCHX

model-free prediction

South Africa

volatility forecasting

Abstract

Because climate change broadcasts a large aggregate risk to the overall macroeconomy and the global financial system, we investigate how a temperature anomaly and/or its volatility affect the accuracy of forecasts of stock return volatility. To this end, we do not apply only the classical GARCH and GARCHX models, but rather we apply newly proposed model-free prediction methods, and use GARCH-NoVaS and GARCHX-NoVaS models to compute volatility predictions. These two models are based on a normalizing and variance-stabilizing transformation (NoVaS transformation) and are guided by a so-called model-free prediction principle. Applying the new models to data for South Africa, we find that climate-related information is helpful in forecasting stock return volatility. Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock return volatility is significant over a century of monthly data (February 1910–February 2023). Our findings have important implications for academics, investors, and policymakers.

Description

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Published version

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