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ESG Uncertainty and Forecasting Realized Volatility of Gold Returns: A Boosting Approach

Author

Listed:
  • Matteo Bonato

    (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France)

  • Rangan Gupta

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

  • Christian Pierdzioch

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

  • Onur Polat

    (Department of Public Finance, Bilecik Seyh Edebali University, Bilecik, Turkiye)

Abstract

Based on monthly data for the sample period from 2002:11 to 2024:09, we report that 25 country-level ESG-related uncertainty indexes improve the accuracy of out-of-sample forecasts of the realized volatility of gold returns, and also produce economic benefits. Using a component-wise gradient descent boosting algorithm, we show that this main result of our empirical study is robust to alternative model specifications, forecast evaluation criteria, and three different forecast horizons. Importantly, we find for a somewhat shorter sample period of 2008:01 to 2024:09 that, general measures of economic uncertainty do not go beyond the country-level ESG-based uncertainty indexes in improving the forecasting performance of the realized volatility of gold returns. Our findings have important implications from the perspective of investors as well as policymakers.

Suggested Citation

  • Matteo Bonato & Rangan Gupta & Christian Pierdzioch & Onur Polat, 2025. "ESG Uncertainty and Forecasting Realized Volatility of Gold Returns: A Boosting Approach," Working Papers 202513, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202513
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    More about this item

    Keywords

    Gold; Realized volatility; ESG uncertainty; 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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