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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: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Working Paper: Climate Risks and State-Level Stock-Market Realized Volatility (2022)
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DOI: 10.1016/j.finmar.2023.100854

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