Análisis del riesgo de renta variable en el marco de Solvencia IImodelos internos frente al estándar

  1. Durán Santomil, Pablo 1
  2. Otero González, Luís A. 1
  3. Vivel Búa, Milagros 1
  4. Fernández López, Sara 1
  1. 1 Departamento de Economía Financiera y Contabilidad Universidad de Santiago de Compostela (USC)
Journal:
Anales de ASEPUMA

ISSN: 2171-892X

Year of publication: 2010

Issue: 18

Type: Article

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Anales de ASEPUMA

Abstract

Solvency II will transform the system of determining capital requirements of the insurer. The new regulatory framework proposes a standard model, but at the same time, it encourages the application of internal models of self-assessment and risk management. This paper aims to examine alternative models proposed in the literature for the measurement of insurer´s equity risk exposure. We have used monthly data series on the IBEX-35 in the period between January 1992 and December 2008. The calibrated models have allowed comparing the resulting capital requirements against the proposal of the fourth quantitative impact study (QIS4). The results show that capital requirements obtained by the better fit models are significantly greater than those of the standard model. This means that companies using the standard model or another based on similar assumptions underestimate significantly their exposure to equity risk.

Bibliographic References

  • Ahlgrim, K. C.; D'Arcy, S. P.; Gorvett, R. W. (2004a): “Asset-liability modeling for insurers: Incorporating a regime-switching process for equity returns into a Dynamic Financial Analysis model”, Presentado a ASTIN Colloquium 2004.
  • Ahlgrim, K. C.; D'Arcy, S. P.; Gorvett, R. W. (2004b): “Modeling of Economic Series Coordinated with Interest Rate Scenarios”
  • Bayliffe D.; Pauling, B. (2003):” Long Term Equity Returns”, Presentado al 2003 Stochastic Modeling Symposium, 4-5 septiembre, Toronto.
  • Boudreault , Panneton (2009): “Multivariate Models of Equity Returns for Investment Guarantees Valuation”, North American Actuarial Journal, Vol.13, No.1, pp. 36-53.
  • D’Arcy, S.P.; Gorvett, R. W.; Herbers, J. A.; Hettinger, T. E.; Lehmann, S. G. Y Miller, M. J. (1997): “Building a Public Access PC-Based DFA Model”, CAS Forum, pp. 1–40.
  • D’Arcy, S. P.; Gorvett, R.W.; Hettinger, T.E.; Walling, III R.J. (1998): “Using the Public Access Dynamic Financial Analysis Model. A Case Study”, CAS Forum, pp. 53–118.
  • D’Arcy, S.P.; Gorvett, R. (2004): “The Use of Dynamic Financial Analysis to Determine Whether an Optimal Growth Rate Exists for a Property-Liability Insurer”, Journal of Risk and Insurance, Vol. 71, No. 4, pp. 583–615.
  • Glosten, L.; Jagannthan, R.; Runkle, D. (1993): “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks”, Journal of Finance, Vol. 48, No. 5, pp. 1779-1801.
  • Hamilton, J. D (1994): Time Series Analysis. Princeton: Princeton University Press.
  • Hardy, M. R. (2001): “A Regime Switching Model of Long-Term Stock Returns, North American Actuarial Journal, Vol. 5, No. 2, pp. 41–53.
  • Hardy, M. R.; Freeland, R. K.; Till, M. C. (2006): “Validation of LongTerm Equity Return Models for Equity-Linked Guarantees”, North American Actuarial Journal, Vol. 10, pp. 28–47.
  • Hibbert, J.; Mowbray, P. ; Turnbull, C. (2001): A Stochastic Asset Model & Calibration for Long-Term Financial Planning Purposes, Technical Report, Barrie & Hibbert Limited.
  • Kaufmann, R.; Gadmer, A.; Klett, R. (2001): “Introduction to Dynamic Financial Analysis”, ASTIN Bulletin, Vol. 31, No. 1, pp. 213–249.
  • Nelson, D.B. (1991) “Conditional heteroscedasticity in asset returns: a new approach”, Econometrica, Vol. 59, pp. 347-370.
  • Panneton, C.-M. (2003): “Mean-Reversion in Equity Models in the Context of Actuarial Provisions for Segregated Fund Investment Guarantees”, Presentado al 2003 Stochastic Modeling Symposium Proceedings, Canadian Institute of Actuaries.
  • Ruiz, E.; Veiga, H. (2008) “Modelos de volatilidad estocástica: una alternativa atractiva y factible para modelizar la evolución de la volatilidad”, Anales de Estudios Económicos y Empresariales, No 18, pp. 9-68.
  • Silverman, B. W. (1986) Density Estimation. London: Chapman and Hall.
  • Schmeiser, H., (2004): “New Risk-Based Capital Standards in the EU. A Proposal Based on Empirical Data”, Risk Management and Insurance Review, Vol. 7, No. 1, pp. 41–52.
  • Taylor, S.J. (1986): Modeling Financial Time Series, Wiley, New York.
  • Wong, C. S.; Chan, W. S. (2005): “Mixture Gaussian Time Series Modeling of Long-Term Market Returns”, North American Actuarial Journal, Vol. 9, No. 4, pp. 83–94.
  • Wong, C. S.; Li, W. K. (2000): “On a Mixture Autoregressive Model” Journal of the Royal Statistical Society B62, pp. 95–115.
  • Wong, C. S. Y LI, W. K. (2001): “On a Mixture Autoregressive Conditional Heteroscedastic Model”, Journal of the American Statistical Association, Vol. 96, pp. 982–995.
Data source: Dialnet