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Scenario Based Principal Component Value-at-Risk: an Application to Italian Banks' Interest Rate Risk Exposure

Author

Listed:
  • Roberta Fiori

    (Banca d'Italia)

  • Simonetta Iannotti

    (Banca d'Italia)

Abstract

The paper develops a Value-at-Risk methodology to assess Italian banks� interest rate risk exposure. By using 5 years of daily data, the exposure is evaluated through a Principal Component VaR based on Monte Carlo simulation according to two different approaches (parametric and non-parametric). The main contribution of the paper is a methodology for modelling interest rate changes when underlying risk factors are skewed and heavy-tailed. The methodology is then implemented on a one year holding period in order to compare the results from those resulting from the Basel II standardized approach. We find that the risk measure proposed by Basel II gives an adequate description of risk, provided that duration parameters are changed to reflect market conditions. Finally, the methodology is used to perform a stress testing analysis.

Suggested Citation

  • Roberta Fiori & Simonetta Iannotti, 2006. "Scenario Based Principal Component Value-at-Risk: an Application to Italian Banks' Interest Rate Risk Exposure," Temi di discussione (Economic working papers) 602, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_602_06
    as

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    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2006/2006-0602/tema_602.pdf
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    References listed on IDEAS

    as
    1. Christoffersen, Peter & Hahn, Jinyong & Inoue, Atsushi, 2001. "Testing and comparing Value-at-Risk measures," Journal of Empirical Finance, Elsevier, vol. 8(3), pages 325-342, July.
    2. Jeremy Berkowitz & James O'Brien, 2002. "How Accurate Are Value‐at‐Risk Models at Commercial Banks?," Journal of Finance, American Finance Association, vol. 57(3), pages 1093-1111, June.
    3. Bertocchi, Marida & Giacometti, Rosella & Zenios, Stavros A., 2005. "Risk factor analysis and portfolio immunization in the corporate bond market," European Journal of Operational Research, Elsevier, vol. 161(2), pages 348-363, March.
    4. D. Sornette & J. V. Andersen & P. Simonetti, 2000. "Portfolio Theory For "Fat Tails"," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 523-535.
    5. Matthew Pritsker, 1997. "Evaluating Value at Risk Methodologies: Accuracy versus Computational Time," Journal of Financial Services Research, Springer;Western Finance Association, vol. 12(2), pages 201-242, October.
    6. Niffikeer, Cindy I. & Hewins, Robin D. & Flavell, Richard B., 2000. "A synthetic factor approach to the estimation of value-at-risk of a portfolio of interest rate swaps," Journal of Banking & Finance, Elsevier, vol. 24(12), pages 1903-1932, December.
    7. Farshid Jamshidian & Yu Zhu, 1996. "Scenario Simulation: Theory and methodology (*)," Finance and Stochastics, Springer, vol. 1(1), pages 43-67.
    8. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    9. R. Brummelhuis & A. Córdoba & M. Quintanilla & L. Seco, 2002. "Principal Component Value at Risk," Mathematical Finance, Wiley Blackwell, vol. 12(1), pages 23-43, January.
    10. Vlaar, Peter J. G., 2000. "Value at risk models for Dutch bond portfolios," Journal of Banking & Finance, Elsevier, vol. 24(7), pages 1131-1154, July.
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    Cited by:

    1. Esposito, Lucia & Nobili, Andrea & Ropele, Tiziano, 2015. "The management of interest rate risk during the crisis: Evidence from Italian banks," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 486-504.

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    More about this item

    Keywords

    Interest rate risk; VAR; PCA; Non-normality; Non parametric methods;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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