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Value-at-Risk Analysis for the Tunisian Currency Market: A Comparative Study


  • Aymen BEN REJEB

    (Higher Institute of Management of Sousse, University of Sousse, Tunisia)

  • Ousama BEN SALHA

    (Higher Institute of Management of Sousse, University of Sousse, Tunisia)

  • Jaleleddine BEN REJEB

    (Higher Institute of Management of Sousse, University of Sousse, Tunisia)


The main purpose of this paper is to compare empirically four Value-at-Risk simulation methods, namely, the Variance-Covariance, the Historical Simulation, the Bootstrapping and the Monte Carlo. We tried to estimate the VaR associated to three currencies and four currency portfolios in the Tunisian exchange market. Data covers the period between 01-01-1999 and 31-12-2007. Independently of the used technique, the Japanese Yen seems to be the most risky currency. Moreover, the portfolio diversification reduces the exchange rate risk. Lastly, the number of violations, when they exist, does not generally differ between the simulation methods. Recent evaluation tests were applied to select the most appropriate technique predicting precisely the exchange rate risk. Results based on these tests suggest that the traditional Variance-Covariance is the most appropriate method.

Suggested Citation

  • Aymen BEN REJEB & Ousama BEN SALHA & Jaleleddine BEN REJEB, 2012. "Value-at-Risk Analysis for the Tunisian Currency Market: A Comparative Study," International Journal of Economics and Financial Issues, Econjournals, vol. 2(2), pages 110-125.
  • Handle: RePEc:eco:journ1:2012-02-2

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    References listed on IDEAS

    1. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    2. Lima, Luiz Renato & Néri, Breno Pinheiro, 2007. "Comparing Value-at-Risk Methodologies," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 27(1), May.
    3. Jose A. Lopez, 1996. "Regulatory Evaluation of Value-at-Risk Models," Center for Financial Institutions Working Papers 96-51, Wharton School Center for Financial Institutions, University of Pennsylvania.
    4. Don Bredin & Stuart Hyde, 2004. "FOREX Risk: Measurement and Evaluation Using Value-at-Risk," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(9-10), pages 1389-1417.
    5. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    6. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    7. Margaret Woods & Kevin Dowd & Christopher Humphrey, 2008. "The value of risk reporting: a critical analysis of value-at-risk disclosures in the banking sector," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 3(1), pages 45-64.
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    Cited by:

    1. Pablo Farías, 2014. "Divulgación del valor en riesgo (VaR) previo a la crisis en el sector bancario español," REVISTA AD-MINISTER, UNIVERSIDAD EAFIT, July.
    2. Ourir, Awatef & Snoussi, Wafa, 2012. "Markets liquidity risk under extremal dependence: Analysis with VaRs methods," Economic Modelling, Elsevier, vol. 29(5), pages 1830-1836.

    More about this item


    Value-at-Risk; Tunisian currency market; Monte Carlo simulation;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications


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