Climate Risks and State-level Stock Market Realized Volatility

Matteo Bonato; Oguzhan Cepni; Rangan Gupta; Christian Pierdzioch

doi:10.1016/j.finmar.2023.100854

Climate Risks and State-level Stock Market Realized Volatility

Matteo Bonato, Oguzhan Cepni, Rangan Gupta, Christian Pierdzioch^*

^*Corresponding author af dette arbejde

Økonomisk Institut

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

Abstract

We analyze the predictive value of climate risks for state-level realized stock market volatility, computed, along with other realized moments, based on high-frequency intra-day U.S. data (September, 2011 to October, 2021). A model-based bagging algorithm recovers that climate risks have predictive value for realized volatility at intermediate and long (one and two months) forecast horizons. This finding also holds for upside (“good”) and downside (“bad”) realized volatility. The benefits of using climate risks for predicting state-level realized stock market volatility depend on the shape and (as-)symmetry of a forecaster’s loss function.

Originalsprog	Engelsk
Artikelnummer	100854
Tidsskrift	Journal of Financial Markets
Vol/bind	66
Antal sider	18
ISSN	1386-4181
DOI	https://doi.org/10.1016/j.finmar.2023.100854
Status	Udgivet - nov. 2023

Bibliografisk note

Published online: 10 July 2023.

Emneord

Finance
State-level data
Realized stock market volatility
Climate-related predictors
Prediction models

FN’s Verdensmål

Dette resultat bidrager til følgende verdensmål

Adgang til dokumentet

10.1016/j.finmar.2023.100854Licens: Ikke-specificeret

Handle.net

Persistent link

Citationsformater

@article{991aef4e0c4a45d8805fdde27918ceec,

title = "Climate Risks and State-level Stock Market Realized Volatility",

abstract = "We analyze the predictive value of climate risks for state-level realized stock market volatility, computed, along with other realized moments, based on high-frequency intra-day U.S. data (September, 2011 to October, 2021). A model-based bagging algorithm recovers that climate risks have predictive value for realized volatility at intermediate and long (one and two months) forecast horizons. This finding also holds for upside (“good”) and downside (“bad”) realized volatility. The benefits of using climate risks for predicting state-level realized stock market volatility depend on the shape and (as-)symmetry of a forecaster{\textquoteright}s loss function.",

keywords = "Finance, State-level data, Realized stock market volatility, Climate-related predictors, Prediction models, Finance, State-level data, Realized stock market volatility, Climate-related predictors, Prediction models",

author = "Matteo Bonato and Oguzhan Cepni and Rangan Gupta and Christian Pierdzioch",

note = "Published online: 10 July 2023.",

year = "2023",

month = nov,

doi = "10.1016/j.finmar.2023.100854",

language = "English",

volume = "66",

journal = "Journal of Financial Markets",

issn = "1386-4181",

publisher = "Elsevier",

}

TY - JOUR

T1 - Climate Risks and State-level Stock Market Realized Volatility

AU - Bonato, Matteo

AU - Cepni, Oguzhan

AU - Gupta, Rangan

AU - Pierdzioch, Christian

N1 - Published online: 10 July 2023.

PY - 2023/11

Y1 - 2023/11

N2 - We analyze the predictive value of climate risks for state-level realized stock market volatility, computed, along with other realized moments, based on high-frequency intra-day U.S. data (September, 2011 to October, 2021). A model-based bagging algorithm recovers that climate risks have predictive value for realized volatility at intermediate and long (one and two months) forecast horizons. This finding also holds for upside (“good”) and downside (“bad”) realized volatility. The benefits of using climate risks for predicting state-level realized stock market volatility depend on the shape and (as-)symmetry of a forecaster’s loss function.

AB - We analyze the predictive value of climate risks for state-level realized stock market volatility, computed, along with other realized moments, based on high-frequency intra-day U.S. data (September, 2011 to October, 2021). A model-based bagging algorithm recovers that climate risks have predictive value for realized volatility at intermediate and long (one and two months) forecast horizons. This finding also holds for upside (“good”) and downside (“bad”) realized volatility. The benefits of using climate risks for predicting state-level realized stock market volatility depend on the shape and (as-)symmetry of a forecaster’s loss function.

KW - Finance

KW - State-level data

KW - Realized stock market volatility

KW - Climate-related predictors

KW - Prediction models

KW - Finance

KW - State-level data

KW - Realized stock market volatility

KW - Climate-related predictors

KW - Prediction models

U2 - 10.1016/j.finmar.2023.100854

DO - 10.1016/j.finmar.2023.100854

M3 - Journal article

SN - 1386-4181

VL - 66

JO - Journal of Financial Markets

JF - Journal of Financial Markets

M1 - 100854

ER -