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

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  • Roberta Fiori

    ()
    (Banca d'Italia)

  • Simonetta Iannotti

    ()
    (Banca d'Italia)

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    Abstract

    The paper develops a Value-at-Risk methodology to assess Italian banksÂ’ 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.

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    File URL: http://www.bancaditalia.it/pubblicazioni/econo/temidi/td06/td602_06/td602/tema_602.pdf
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    Bibliographic Info

    Paper provided by Bank of Italy, Economic Research and International Relations Area in its series Temi di discussione (Economic working papers) with number 602.

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    Date of creation: Sep 2006
    Date of revision:
    Handle: RePEc:bdi:wptemi:td_602_06

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    Postal: Via Nazionale, 91 - 00184 Roma
    Web page: http://www.bancaditalia.it
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    Related research

    Keywords: Interest rate risk; VAR; PCA; Non-normality; Non parametric methods;

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    References

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    1. Leslie E. Papke & Jeffrey M. Wooldridge, 1993. "Econometric Methods for Fractional Response Variables with an Application to 401(k) Plan Participation Rates," NBER Technical Working Papers 0147, National Bureau of Economic Research, Inc.
    2. 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.
    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. Farshid Jamshidian & Yu Zhu, 1996. "Scenario Simulation: Theory and methodology (*)," Finance and Stochastics, Springer, vol. 1(1), pages 43-67.
    5. 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.
    6. R. Brummelhuis & A. Cãrdoba & M. Quintanilla & L. Seco, 2002. "Principal Component Value at Risk," Mathematical Finance, Wiley Blackwell, vol. 12(1), pages 23-43.
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    Cited by:
    1. Lucia Esposito & Andrea Nobili & Tiziano Ropele, 2013. "The management of interest rate risk during the crisis: evidence from Italian banks," Temi di discussione (Economic working papers) 933, Bank of Italy, Economic Research and International Relations Area.

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