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.
| Original language | English |
|---|---|
| Place of Publication | Pretoria |
| Publisher | University of Pretoria |
| Number of pages | 31 |
| Publication status | Published - Sept 2022 |
| Series | Working Paper Series / Department of Economics. University of Pretoria |
|---|---|
| Number | 2022-46 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- Finance
- State-level data
- Realized stock-market volatility
- Climate-related predictors
- Forecasting
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