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

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  • 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)

Abstract

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.

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Bibliographic Info

Article provided by Econjournals in its journal International Journal of Economics and Financial Issues.

Volume (Year): 2 (2012)
Issue (Month): 2 ()
Pages: 110-125

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Handle: RePEc:eco:journ1:2012-02-2

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Related research

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

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  1. 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.
  2. Jose A. Lopez, 1997. "Regulatory evaluation of value-at-risk models," Staff Reports 33, Federal Reserve Bank of New York.
  3. 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.
  4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
  5. 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.
  6. Luiz Renato Lima & Breno Pinheiro Néri, 2006. "Comparing Value-at-Risk Methodologies," Computing in Economics and Finance 2006 1, Society for Computational Economics.
  7. 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, 09.
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Cited by:
  1. Ourir, Awatef & Snoussi, Wafa, 2012. "Markets liquidity risk under extremal dependence: Analysis with VaRs methods," Economic Modelling, Elsevier, vol. 29(5), pages 1830-1836.

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