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Financial Stress and Realized Volatility: The Case of Agricultural Commodities

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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)

  • Oguzhan Cepni

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

  • 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)

Abstract

Given recent debates about the financialization of commodity markets, we analyze the predictive power of financial stress for the realized volatility of agricultural commodity price returns. We estimate realized volatility from high-frequency intra-day data, where the sample period ranges from 2009 to 2020. We study the in-sample and out-of-sample predictability of realized volatility using variants of the popular heterogeneous autoregressive (HAR) model for realized volatility. We analyze the predictive value of financial stress by region of origin and by financial source, and we also control for various realized moments (leverage, realized skewness, realized kurtosis, realized jumps, realized upside tail risk, and realized downside tail risk). We find evidence of in-sample predictive value of financial stress for realized volatility, consistent with the financialialization hypothesis. This in-sample evidence, however, in general does not extend to an out-of-sample forecasting environment.

Suggested Citation

  • Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2023. "Financial Stress and Realized Volatility: The Case of Agricultural Commodities," Working Papers 202320, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202320
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    References listed on IDEAS

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    More about this item

    Keywords

    Realized volatility; Agricultural commodities; Financialization; Realized moments; Predictability;
    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
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General

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