Bonato, MatteoCepni, OguzhanGupta, RanganPierdzioch, Christian2023-10-052023-10-052023-11Bonato, M., Cepni, O., Gupta, R. et al. 2023, 'Climate risks and state-level stock market realized volatility', Journal of Financial Markets, vol. 66, art. 100854, pp. 1-18, doi : 10.1016/j.finmar.2023.100854.1386-418110.1016/j.finmar.2023.100854http://hdl.handle.net/2263/92716DATA AVAILABILITY : Data will be made available on request.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.en© 2023 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was submitted for publication in Journal of Financial Markets. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms are not reflected in this document. A definitive version was subsequently published in Journal of Financial Markets, vol. 66, art. 100854, pp. 1-18, doi : 10.1016/j.finmar.2023.100854.Prediction modelsClimate-related predictorsRealized stock market volatilityFinance state-level dataSDG-13: Climate actionSDG-08: Decent work and economic growthPreprint Article