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Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss

Author

Listed:
  • Konstantinos Gkillas

    () (Department of Business Administration, University of Patras – University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Christian Pierdzioch

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

Abstract

We analyze the role of global and regional measures of financial stress in forecasting realized volatility of the oil market based on 5-minute intraday data covering the period of 4th January, 2000 until 26th May, 2017. In this regard, we use various variants of the Heterogenous Autoregressive (HAR) model of realized volatility (HAR-RV). Our main finding is that indexes of financial stress help to improve forecasting performance, with it being important to differentiate between regional sources of financial stress (United States, other advanced economies, emerging markets). Another key finding is that the shape of the forecaster loss function that one uses to evaluate forecasting performance plays an important role. More specifically, forecasters who attach a higher cost to an overprediction of realized volatility as compared to an underprediction of the same absolute size should pay particular attention to financial stress originating in the U.S. But, in case an underprediction is more costly than a comparable overprediction, then forecasters should closely monitor financial stress caused by developments in emerging-market economies. In sum, financial stress does have predictive value for realized oil-price volatility, with alternative types of investors benefitting from monitoring different regional sources of financial stress.

Suggested Citation

  • Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2019. "Forecasting Realized Oil-Price Volatility: The Role of Financial Stress and Asymmetric Loss," Working Papers 201903, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201903
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    Cited by:

    1. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, Open Access Journal, vol. 12(10), pages 1-11, May.
    2. Asai, Manabu & Gupta, Rangan & McAleer, Michael, 2020. "Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 933-948.
    3. Claudiu Albulescu, 2020. "Coronavirus and oil price crash," Papers 2003.06184, arXiv.org, revised Mar 2020.
    4. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    5. Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch & Seong-Min Yoon, 2020. "OPEC News and Jumps in the Oil Market," Working Papers 202053, University of Pretoria, Department of Economics.
    6. Elie Bouri & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Infectious Diseases, Market Uncertainty and Oil Market Volatility," Energies, MDPI, Open Access Journal, vol. 13(16), pages 1-8, August.
    7. Afees A. Salisu & Rangan Gupta & Elie Bouri, 2020. "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers 202051, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Oil price; Realized volatility; Financial stress; Forecasting; Asymmetric loss;

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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