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Drivers of Realized Volatility for South Africa (and the BRIC Countries): Fundamentals versus Sentiment

Author

Listed:
  • Rangan Gupta

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

  • Jacobus Nel

    (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)

Abstract
We use a quantile machine learning (random forests) approach to analyze the predictive ability of newspapers-based Macroeconomic Attention Indexes (MAIs) on eight major fundamentals of the United States on the realized volatility of a major commodity exporting emerging stock market, namely South Africa. We compare the performance of the MAIs with the performance of a News Sentiment Index (NSI) of the US. We find that both fundamentals and sentiment improve predictive performance, but the relative impact of the former is stronger. We document how the impact of fundamentals and sentiment on predictive performance varies across the quantiles of the conditional distribution of realized volatility, and across different prediction horizons. In addition, we report several robustness checks, and check whether the obtained results for South Africa tend to carry over to other emerging countries such as, Brazil, China, India, and Russia. Our results have important implications for not only investors, but also policymakers.

Suggested Citation

  • Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2023. "Drivers of Realized Volatility for South Africa (and the BRIC Countries): Fundamentals versus Sentiment," Working Papers 202303, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202303
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    More about this item

    Keywords

    Stock markets; Realized volatility; Macroeconomic attention; Sentiment; Quantile random forests; Prediction Models; BRICS countries;
    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
    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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