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Optimal Value-at-Risk Disclosure

Author

Listed:
  • Seixas, Mário
  • Barbosa, António

Abstract

In 1995, the Basel Accords introduced an alternative method to compute the market risk charge through the use of a risk model developed internally by the financial institution. These internal models, based on the Value-at-Risk (VaR), follow certain rules that are defined under the Basel Accords. From this moment on, risk analysts and financial academics focused their attentions on how to accurately estimate the VaR in order to reduce the regulatory capital. However, considering the market risk framework defined in the Basel Accords, the best strategy to optimize the regulatory capital may not lie in truthfully disclosing an accurate VaR estimation. In this study, we propose to solve, through dynamic programming, for the optimal policy function for disclosing the reported VaR based on the estimated value that minimizes the daily capital charge. This policy function will provide the optimal percentage of the estimated 1-day VaR that should be disclosed, taking into account the impact that this disclosure decision will have in future capital charges, by managing the rules defined in the Basel Accords. Our goal is to prove that truthful disclosure of an accurately estimated VaR is suboptimal. The main results from our investigation show that using the optimal reporting strategy leads to an average daily reduction in the capital requirements of 4.32% in a simulated environment, compared with a normal strategy of always truthfully disclosing the estimated 1-day VaR, and leads to an average daily saving of 7.22% when applied to our S&P500 test portfolio.

Suggested Citation

  • Seixas, Mário & Barbosa, António, 2019. "Optimal Value-at-Risk Disclosure," MPRA Paper 97526, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:97526
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    File URL: https://mpra.ub.uni-muenchen.de/97526/1/MPRA_paper_97526.pdf
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    References listed on IDEAS

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    1. Włodzimierz Ogryczak & Tomasz Śliwiński, 2011. "On solving the dual for portfolio selection by optimizing Conditional Value at Risk," Computational Optimization and Applications, Springer, vol. 50(3), pages 591-595, December.
    2. Pérignon, Christophe & Smith, Daniel R., 2010. "The level and quality of Value-at-Risk disclosure by commercial banks," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 362-377, February.
    3. Wolfgang Aussenegg & Tatiana Miazhynskaia, 2006. "Uncertainty in Value-at-risk Estimates under Parametric and Non-parametric Modeling," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(3), pages 243-264, September.
    4. Richard Bellman, 1954. "Some Applications of the Theory of Dynamic Programming---A Review," Operations Research, INFORMS, vol. 2(3), pages 275-288, August.
    5. Michael McAleer, 2009. "The Ten Commandments For Optimizing Value‐At‐Risk And Daily Capital Charges," Journal of Economic Surveys, Wiley Blackwell, vol. 23(5), pages 831-849, December.
    6. Geng Deng & Tim Dulaney & Craig McCann & Olivia Wang, 2013. "Robust portfolio optimization with Value-at-Risk-adjusted Sharpe ratios," Journal of Asset Management, Palgrave Macmillan, vol. 14(5), pages 293-305, October.
    7. Churlzu Lim & Hanif Sherali & Stan Uryasev, 2010. "Portfolio optimization by minimizing conditional value-at-risk via nondifferentiable optimization," Computational Optimization and Applications, Springer, vol. 46(3), pages 391-415, July.
    8. Alexandra Künzi-Bay & János Mayer, 2006. "Computational aspects of minimizing conditional value-at-risk," Computational Management Science, Springer, vol. 3(1), pages 3-27, January.
    9. Richard Bellman, 1954. "On some applications of the theory of dynamic programming to logistics," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(2), pages 141-153, June.
    10. Renata Mansini & Włodzimierz Ogryczak & M. Speranza, 2007. "Conditional value at risk and related linear programming models for portfolio optimization," Annals of Operations Research, Springer, vol. 152(1), pages 227-256, July.
    11. Griselda Deelstra & Ahmed Ezzine & Dries Heyman & Michèle Vanmaele, 2007. "Managing value-at-risk for a bond using bond put options," Computational Economics, Springer;Society for Computational Economics, vol. 29(2), pages 139-149, March.
    12. Dong‐Hyun Ahn & Jacob Boudoukh & Matthew Richardson & Robert F. Whitelaw, 1999. "Optimal Risk Management Using Options," Journal of Finance, American Finance Association, vol. 54(1), pages 359-375, February.
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    More about this item

    Keywords

    Value-at-Risk; Regulatory Capital; Market Risk Charge; Optimal Disclosure; Dynamic Programming;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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