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Analysis Based on the Risk Metrics Model

Author

Listed:
  • Bogdan ZUGRAVU
  • Dumitru Cristian OANEA
  • Victoria Gabriela ANGHELACHE

    (Academy of Economic Studies, Bucharest)

Abstract

The first aim of this paper is to see if there is some differences regarding the value of decay factor estimated based on squared error loss, the RiskMetrics approach, and the values obtain from implementing the check error loss function in estimating the decay factors. Regarding the equity market, all investors recorded losses during the financial crisis if they used the RiskMetrics methodology in forecasting the risk. Moreover the only model which was able to predict the risk is represented by RiskMetrics-2006, at 99% confidence level. For exchange rates and commodities, RiskMetrics seems to have a good performance, because for both types of loss functions and under both distribution assumptions, on overall the Risk Metrics is able to forecast the risk.

Suggested Citation

  • Bogdan ZUGRAVU & Dumitru Cristian OANEA & Victoria Gabriela ANGHELACHE, 2013. "Analysis Based on the Risk Metrics Model," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(2), pages 145-154, May.
  • Handle: RePEc:rsr:supplm:v:61:y:2013:i:2:p:145-154
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    References listed on IDEAS

    as
    1. Hammoudeh, Shawkat & Malik, Farooq & McAleer, Michael, 2011. "Risk management of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(4), pages 435-441.
    2. Ulf Nielsson, 2009. "Measuring and regulating extreme risk," Journal of Financial Regulation and Compliance, Emerald Group Publishing, vol. 17(2), pages 156-171, May.
    3. Halbleib, Roxana & Pohlmeier, Winfried, 2012. "Improving the value at risk forecasts: Theory and evidence from the financial crisis," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1212-1228.
    4. Robert Sollis, 2009. "Value at risk: a critical overview," Journal of Financial Regulation and Compliance, Emerald Group Publishing, vol. 17(4), pages 398-414, November.
    5. Fernando Caio Galdi & Leonel Molero Pereira, 2007. "Value at Risk (VaR) Using Volatility Forecasting Models: EWMA, GARCH and Stochastic Volatility," Brazilian Business Review, Fucape Business School, vol. 4(1), pages 74-94, January.
    6. Patev Plamen & Kanaryan Nigokhos & Lyroudi Katerina, 2009. "Modelling and Forecasting the Volatility of Thin Emerging Stock Markets: the Case of Bulgaria," Comparative Economic Research, Sciendo, vol. 12(4), pages 47-60, January.
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    Citations

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    Cited by:

    1. Dumitru-Cristian OANEA & Gabriela-Victoria ANGHELACHE, 2014. "Systemic Risk Caused By Romanian Financial Intermediaries During Financial Crisis: A Covar Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 14, pages 171-178, December.
    2. Gabriela Anghelache & Dumitru-Cristian Oanea, 2014. "Main Romanian Commercial Banks’ Systemic Risk during Financial Crisis: a CoVar Approach," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 6(2), pages 069-080, December.
    3. Krastyu Georgiev & Young Kim & Stoyan Stoyanov, 2015. "Periodic portfolio revision with transaction costs," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 81(3), pages 337-359, June.

    More about this item

    Keywords

    analysis; decay factor estimated; risk metrics; financial crisis;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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