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Modeling and Forecasting the Markets Volatility and VaR Dynamics of Commodity

Author

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  • Pinar KAYA
  • Bulent GULOGLU

Abstract

The purpose of this paper is to model and forecast the risk of six commodities namely, crude oil, copper, gold, silver, palladium, and platinum during the period from 02/01/2002 to 29/04/2016 using volatility, value at risk and expected shortfall as risk measures. After showing that squared returns of all six commodities have a significant long memory, the volatility, the value at risk and expected shortfall based on fractional GARCH models are estimated and forecasted. Both forecast performance of volatility models and backtest for value at risk indicate that in many cases FIAPARCH model outperforms the other GARCH models. Then volatility, value at risk and expected shortfall estimates based on FIAPARCH model show that the volatility and market risk of oil is much higher than the other commodities. This casts doubt on the use of oil as a hedging tool.

Suggested Citation

  • Pinar KAYA & Bulent GULOGLU, 2017. "Modeling and Forecasting the Markets Volatility and VaR Dynamics of Commodity," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 11(1), pages 9-49.
    • Handle: RePEc:bdd:journl:v:11:y:2017:i:1:p:9-49
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    File URL: http://www.bddk.org.tr/Content/docs/bddkDergiEn/dergi_0021_03.pdf
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    Citations

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    Cited by:

    1. Samuel Tabot Enow, 2022. "Modelling Stock Market Prices Using the Open, High and Closes Prices. Evidence from International Financial Markets," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 15(3), pages 52-59, December.

    More about this item

    Keywords

    Volatility Modelling; Commodity Markets; VaR Forecasting; Expected Shortfall;
    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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