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Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)

Author

Listed:
  • David Gabauer

    (Data Analysis Systems, Software Competence Center Hagenberg, Austria)

  • Rangan Gupta

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

  • Sayar Karmakar

    (Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA)

  • Joshua Nielsen

    (Boulder Investment Technologies, LLC, 1942 Broadway Suite 314C, Boulder, CO, 80302, USA)

Abstract
Firstly, we use the Multi-Scale LPPLS Confidence Indicator approach to detect both positive and negative bubbles at short-, medium- and long-term horizons for the stock markets of the G7 and the BRICS countries. We were able to detect major crashes and rallies in the 12 stock markets over the period of the 1st week of January, 1973 to the 2nd week of September, 2020. We also observed similar timing of strong (positive and negative) LPPLS indicator values across both G7 and BRICS countries, suggesting interconnectedness of the extreme movements in these stock markets. Secondly, we utilize these indicators to forecast gold returns and its volatility, using a method involving block means of residuals obtained from the popular LASSO routine, given that the number of covariates ranged between 42 to 72, and gold returns demonstrated a heavy upper tail. We found that, our bubbles indicators, particularly when both positive and negative bubbles are considered simultaneously, can accurately forecast gold returns at short- to medium-term, and also time-varying estimates of gold returns volatility to a lesser extent. Our results have important implications for the portfolio decisions of investors who seek a safe haven during boom-bust cycles of major global stock markets.

Suggested Citation

  • David Gabauer & Rangan Gupta & Sayar Karmakar & Joshua Nielsen, 2022. "Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)," Working Papers 202228, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202228
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    References listed on IDEAS

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    More about this item

    Keywords

    Gold; Stock Markets; Bubbles; Forecasting; Returns; Volatility;
    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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