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About the Impact of Model Risk on Capital Reserves: A Quantitative Analysis

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  • Bertram, Philip
  • Sibbertsen, Philipp
  • Stahl, Gerhard

Abstract

This paper analyzes and quantifies the idea of model risk in the environment of internal model building. We define various types of model risk including estimation risk, model risk in distribution and model risk in functional form. By the quantification of these concepts we analyze the impact of the modeling process of an econometric model on the resulting company model. Utilizing real insurance data we specify, estimate and simulate various linear and nonlinear time series models for the inflation rate and examine its impact on pension liabilities under the aspect of model risk. Under consideration of different risk measures it is shown that model risk can differ profoundly due to the specification process of the econometric model resulting in remarkable monetary differences concerning capital reserves. We furthermore propose a specification strategy for univariate time series models and demonstrate that thereby market risk and capital reserves can be reduced distinctively.

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File URL: http://www3.wiwi.uni-hannover.de/Forschung/Diskussionspapiere/dp-469.pdf
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Bibliographic Info

Paper provided by Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Diskussionspapiere der Wirtschaftswissenschaftlichen Fakultät der Leibniz Universität Hannover with number dp-469.

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Length: 28 pages
Date of creation: Mar 2011
Date of revision:
Handle: RePEc:han:dpaper:dp-469

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Related research

Keywords: Model risk; Estimation risk; Misspecification risk; Basel multiplication factor; Empirical model specification; Capital reserves;

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  1. Hassler, Uwe & Wolters, Jurgen, 1995. "Long Memory in Inflation Rates: International Evidence," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 37-45, January.
  2. Davidson, James & Sibbertsen, Philipp, 2009. "Tests of bias in log-periodogram regression," Economics Letters, Elsevier, vol. 102(2), pages 83-86, February.
  3. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
  4. Andros Gregoriou & Alexandros Kontonikas, 2006. "Inflation Targeting And The Stationarity Of Inflation: New Results From An Estar Unit Root Test," Bulletin of Economic Research, Wiley Blackwell, vol. 58(4), pages 309-322, October.
  5. Joshua V. Rosenberg & Til Schuermann, 2004. "A general approach to integrated risk management with skewed, fat-tailed risks," Staff Reports 185, Federal Reserve Bank of New York.
  6. Pena, Daniel & Rodriguez, Julio, 2005. "Detecting nonlinearity in time series by model selection criteria," International Journal of Forecasting, Elsevier, vol. 21(4), pages 731-748.
  7. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
  8. John P. Judd & Glenn D. Rudebusch, 1998. "Taylor's rule and the Fed, 1970-1997," Economic Review, Federal Reserve Bank of San Francisco, pages 3-16.
  9. Kerkhof, Jeroen & Melenberg, Bertrand & Schumacher, Hans, 2010. "Model risk and capital reserves," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 267-279, January.
  10. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
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