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Are RiskMetrics forecasts good enough? Evidence from 31 stock markets

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  • McMillan, David G.
  • Kambouroudis, Dimos
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    Abstract

    Academic research has highlighted the inherent flaws within the RiskMetrics model and demonstrated the superiority of the GARCH approach in-sample. However, these results do not necessarily extend to forecasting performance. This paper seeks answer to the question of whether RiskMetrics volatility forecasts are adequate in comparison to those obtained from GARCH models. To answer the question stock index data is taken from 31 international markets and subjected to two exercises, a straightforward volatility forecasting exercise and a Value-at-Risk exceptions forecasting competition. Our results provide some simple answers to the above question. When forecasting volatility of the G7 stock markets the APARCH model, in particular, provides superior forecasts that are significantly different from the RiskMetrics models in over half the cases. This result also extends to the European markets with the APARCH model typically preferred. For the Asian markets the RiskMetrics model performs well, and is only significantly dominated by the GARCH models for one market, although there is evidence that the APARCH model provides a better forecast for the larger Asian markets. Regarding the Value-at-Risk exercise, when forecasting the 1% VaR the RiskMetrics model does a poor job and is typically the worst performing model, again the APARCH model does well. However, forecasting the 5% VaR then the RiskMetrics model does provide an adequate performance. In short, the RiskMetrics model only performs well in forecasting the volatility of small emerging markets and for broader VaR measures.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Review of Financial Analysis.

    Volume (Year): 18 (2009)
    Issue (Month): 3 (June)
    Pages: 117-124

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    Handle: RePEc:eee:finana:v:18:y:2009:i:3:p:117-124

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    Web page: http://www.elsevier.com/locate/inca/620166

    Related research

    Keywords: RiskMetrics GARCH Volatility forecasting Value-at-Risk;

    References

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    Cited by:
    1. Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2013. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 41(2), pages 249-265, February.
    2. Louzis, Dimitrios & Vouldis, Angelos, 2013. "A financial systemic stress index for Greece," Working Paper Series, European Central Bank 1563, European Central Bank.
    3. Louzis, Dimitrios P. & Vouldis, Angelos T., 2012. "A methodology for constructing a financial systemic stress index: An application to Greece," Economic Modelling, Elsevier, vol. 29(4), pages 1228-1241.
    4. Stavros Stavroyiannis & Leonidas Zarangas, 2013. "Out of Sample Value-at-Risk and Backtesting with the Standardized Pearson Type-IV Skewed Distribution," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 60(2), pages 231-247, April.
    5. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    6. Dimitrakopoulos, Dimitris N. & Kavussanos, Manolis G. & Spyrou, Spyros I., 2010. "Value at risk models for volatile emerging markets equity portfolios," The Quarterly Review of Economics and Finance, Elsevier, Elsevier, vol. 50(4), pages 515-526, November.
    7. Chrétien, Stéphane & Coggins, Frank, 2010. "Performance and conservatism of monthly FHS VaR: An international investigation," International Review of Financial Analysis, Elsevier, Elsevier, vol. 19(5), pages 323-333, December.
    8. Diamandis, Panayiotis F. & Drakos, Anastassios A. & Kouretas, Georgios P. & Zarangas, Leonidas, 2011. "Value-at-risk for long and short trading positions: Evidence from developed and emerging equity markets," International Review of Financial Analysis, Elsevier, Elsevier, vol. 20(3), pages 165-176, June.
    9. Harris, Richard D.F. & Mazibas, Murat, 2010. "Dynamic hedge fund portfolio construction," International Review of Financial Analysis, Elsevier, Elsevier, vol. 19(5), pages 351-357, December.
    10. Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013. "Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence," International Review of Financial Analysis, Elsevier, Elsevier, vol. 27(C), pages 21-33.

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