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FOREX Risk: Measurement and Evaluation Using Value‐at‐Risk

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  • Don Bredin
  • Stuart Hyde

Abstract

We measure and evaluate the performance of a number of Value‐at‐Risk (VaR) methods using a portfolio based on the foreign exchange exposure of a small open economy (Ireland) among its trading partners. The sample period highlights the changing nature of Ireland’s exposure to risk over the past decade in the run‐up to EMU. Our results offer an indication of the level of accuracy of the various approaches and discuss the issues of models ensuring statistical accuracy or more conservative leanings. Our findings suggest that the Orthogonal GARCH model is the most accurate methodology while the EWMA specification is the more conservative approach.

Suggested Citation

  • 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, November.
  • Handle: RePEc:bla:jbfnac:v:31:y:2004:i:9-10:p:1389-1417
    DOI: 10.1111/j.0306-686X.2004.00578.x
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    References listed on IDEAS

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    1. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. repec:cdl:ucsdec:qt5s2218dp is not listed on IDEAS
    4. Colleen Cassidy & Marianne Gizycki, 1997. "Measuring Traded Market Risk: Value-at-risk and Backtesting Techniques," RBA Research Discussion Papers rdp9708, Reserve Bank of Australia.
    5. Carol Alexander, 2002. "Principal Component Models for Generating Large GARCH Covariance Matrices," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 31(2), pages 337-359, July.
    6. repec:cdl:ucsdec:qt56j4143f is not listed on IDEAS
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    2. Crescenzio Gallo & Michelangelo De Bonis & Pierpaolo Palazzo, 2012. "Computer applications in the context of financial speculation," Quaderni DSEMS 02-2012, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
    3. Niango Ange Joseph Yapi, 2020. "Exchange rate predictive densities and currency risks: A quantile regression approach," EconomiX Working Papers 2020-16, University of Paris Nanterre, EconomiX.
    4. Hafiz Waqas Kamran & Abdelnaser Omran & Shamsul Bahrain Mohamed-Arshad, 2019. "Risk Management, Capital Adequacy and Audit Quality for Financial Stability: Assessment from Commercial Banks of Pakistan," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(6), pages 654-664.

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