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Climate Policy Uncertainty and Financial Stress: Evidence for China

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Qiang Ji

    (Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

Abstract
Focusing on China, we study the predictive value of Chinese climate policy uncertainty (CCPU) for subsequent stress in China’s financial markets in a sample of daily data running from October 2006 to December 2022. We control for the impact of international spillover effects of financial stress originating in the European Union (EU), the United Kingdom (UK), and the United States (US), and also for a large number of other important macroeconomic, financial, behavioral variables. Given the large number of predictors, we use random forests, an ensemble machine-learning technique, to trace out the impact of CCPU on financial stress by means of an out-of-sample forecasting experiment. We find that CCPU has predictive value for subsequent financial stress, and that its predictive power is stronger than that of measures of global climate risk. Its predictive value is strongest at a short (daily) forecast horizon and tends to decrease when the length of the forecast horizon increases. Moreover, we document the predictive value of CCPU across a spectrum of conditional quantiles of financial stress.

Suggested Citation

  • Rangan Gupta & Qiang Ji & Christian Pierdzioch, 2024. "Climate Policy Uncertainty and Financial Stress: Evidence for China," Working Papers 202428, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202428
    as

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    References listed on IDEAS

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    More about this item

    Keywords

    Financial stress; Climate risks; China; Random forests; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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