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Is the Best Generalized Autoregressive Conditional Heteroskedasticity(p,q) Value-at-risk Estimate also the Best in Reality? An Evidence from Australian Interconnected Power Markets

Author

Listed:
  • Rangga Handika

    (College of Business Administration, Abu Dhabi University, United Arab Emirates,)

  • Rangga Handika

    (Faculty of Business and Economics Universitas, Indonesia)

  • Sigit Triandaru

    (Universitas Atmajaya Yogyakarta, Indonesia.)

Abstract

This paper investigates whether the best value-at-risk (VaR) estimate will also perform the best in empirical performance. The study explores the linkage between statistical world and reality. This paper uses VaR generalized autoregressive conditional heteroskedasticity (GARCH)(p,q) estimates and performs the back testing from both generator (buyer) and retailer (seller) sides, at different confidence levels, and at different out-of-sample periods in the four regions of Australian interconnected power markets. Using VaR approach, we find that the best GARCH(p,q) model tends to generate best empirical performance. Our findings are consistent for both generator (buyer) and retailer (seller) sides, at different confidence levels and at different out-of-sample periods. However, our strong results are only in the daily series. Therefore, our study has two important practical implications in Australian power markets. First, generator and retailer can continue choosing the best GARCH(p,q) model based on statistical criteria. Second, the users of GARCH(p,q) model should be aware that the model tends to be appropriate for estimating the daily series only.

Suggested Citation

  • Rangga Handika & Rangga Handika & Sigit Triandaru, 2016. "Is the Best Generalized Autoregressive Conditional Heteroskedasticity(p,q) Value-at-risk Estimate also the Best in Reality? An Evidence from Australian Interconnected Power Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 6(4), pages 814-821.
    • Handle: RePEc:eco:journ2:2016-04-19
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    References listed on IDEAS

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    1. Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
    2. Wong, Woon K., 2010. "Backtesting value-at-risk based on tail losses," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 526-538, June.
    3. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
    4. Barbara Rossi, 2012. "The Changing Relationship Between Commodity Prices and Equity Prices in Commodity Exporting Countries," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(4), pages 533-569, December.
    5. Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, vol. 22(2), pages 283-300.
    6. Pérignon, Christophe & Deng, Zi Yin & Wang, Zhi Jun, 2008. "Do banks overstate their Value-at-Risk?," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 783-794, May.
    7. Narayan, Paresh Kumar & Narayan, Seema & Sharma, Susan Sunila, 2013. "An analysis of commodity markets: What gain for investors?," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3878-3889.
    8. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    9. Baillie, Richard T & Bollerslev, Tim, 2002. "The Message in Daily Exchange Rates: A Conditional-Variance Tale," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 60-68, January.
    10. Barbara Rossi, 2012. "The changing relationship between commodity prices and equity prices in commodity exporting," Economics Working Papers 1405, Department of Economics and Business, Universitat Pompeu Fabra.
    11. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    13. Menzie D. Chinn & Olivier Coibion, 2014. "The Predictive Content of Commodity Futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(7), pages 607-636, July.
    14. López Cabrera, Brenda & Schulz, Franziska, 2016. "Volatility linkages between energy and agricultural commodity prices," Energy Economics, Elsevier, vol. 54(C), pages 190-203.
    15. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-229, March.
    16. Youssef, Manel & Belkacem, Lotfi & Mokni, Khaled, 2015. "Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach," Energy Economics, Elsevier, vol. 51(C), pages 99-110.
    17. Higgs, Helen & Worthington, Andrew, 2008. "Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market," Energy Economics, Elsevier, vol. 30(6), pages 3172-3185, November.
    18. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
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    More about this item

    Keywords

    Power Markets; Generalized Autoregressive Conditional Heteroskedasticity; Value-at-risk;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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