Climate risks and state-level stock market realized volatility
Matteo Bonato,
Oguzhan Cepni,
Rangan Gupta and
Christian Pierdzioch
Journal of Financial Markets, 2023, vol. 66, issue C
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.
Keywords: Finance; State-level data; Realized stock market volatility; Climate-related predictors; Prediction models (search for similar items in EconPapers)
JEL-codes: C22 C53 G10 G17 Q54 (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1386418123000526
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Climate Risks and State-Level Stock-Market Realized Volatility (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:finmar:v:66:y:2023:i:c:s1386418123000526
DOI: 10.1016/j.finmar.2023.100854
Access Statistics for this article
Journal of Financial Markets is currently edited by B. Lehmann, D. Seppi and A. Subrahmanyam
More articles in Journal of Financial Markets from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).