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Gold Futures Returns and Realized Moments: A Forecasting Experiment Using a Quantile-Boosting Approach

Author

Listed:
  • Matteo Bonato

    () (Department of Economics and Econometrics, University of Johannesburg, South Africa.)

  • Riza Demirer

    () (Department of Economics and Finance, Southern Illinois University Edwardsville, USA)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

  • Christian Pierdzioch

    () (Department of Economics, Helmut Schmidt University, Germany)

Abstract

This paper proposes an iterative model-building approach known as quantile boosting to trace out the predictive value of realized volatility and skewness for gold futures returns. Controlling for several widely studied market- and sentiment-based variables, we examine the predictive value of realized moments across alternative forecast horizons and across the quantiles of the conditional distribution of gold futures returns. We find that the realized moments often significantly improve the predictive value of the estimated forecasting models at intermediate forecast horizons and across quantiles representing distressed market conditions. We argue that realized moments carry information that reflects investors’ tradeoff between diversification and skewed payoffs, particularly during periods of market stress, which may be especially relevant for gold as the traditional accepted safe haven.

Suggested Citation

  • Matteo Bonato & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2016. "Gold Futures Returns and Realized Moments: A Forecasting Experiment Using a Quantile-Boosting Approach," Working Papers 201645, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201645
    as

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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Gold futures returns; Realized volatility; Realized skewness; Forecasting; Quantile boosting;

    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
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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