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Simulation Results

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Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 331))

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Abstract

In this chapter the simulation results are presented from the experiments performed on the applications of value at risk (VaR) and subjective value at risk (SVaR) as well as fuzzy versions of VaR and SVaR in several optimization settings. The problem of risk control is presented using VaR and fuzzy VaR estimates along with the linear regression hedging problem. The equivalence of chance and value at risk constraints is illustrated through an example. The problem of portfolio rebalancing strategies in context of risk and deviation concludes the chapter. The risk management experiments are performed in MATLAB.

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References

  1. Benchmark Datasets in Portfolio Optimization: http://www.cs.nott.ac.uk/~rxq/POdata.htm

  2. Best, M.J.: Portfolio Optimization. Chapman and Hall, CRC Finance Series (2010)

    Google Scholar 

  3. Bucay, N., Rosen, D.: Credit risk of an international bond portfolio: a case study. Algo Res. Q. 2(1), 9–29 (1999)

    Google Scholar 

  4. Chaudhuri, A.: A Study of Operational Risk using Possibility Theory. Technical Report, Birla Institute of Technology Mesra, Patna Campus, India (2010)

    Google Scholar 

  5. Cretu, O., Stewart, R.B., Berends, T.: Risk Management for Design and Construction. Wiley (2011)

    Google Scholar 

  6. Dutta, K., Perry, J.: A Tale of Tails: An Empirical Analysis of Loss Distribution Models for Estimating Operational Risk Capital. Federal Reserve Bank of Boston, Working Paper Number 6–13 (2006)

    Google Scholar 

  7. Elton, E.J., Gruber, M.J., Brown, S.J., Goetzmann, W.N.: Modern Portfolio Theory and Investment Analysis, 8th edn. Wiley India, New Delhi (2010)

    Google Scholar 

  8. Khatri, D.K.: Security Analysis and Portfolio Management. Trinity Press (2012)

    Google Scholar 

  9. Laha, R.G., Rohatgi, V.K.: Probability Theory, vol 43. Wiley Series in Probability and Mathematical Statistics. Wiley (1979)

    Google Scholar 

  10. Larsen, N., Mausser, H., Uryasev, S.: Algorithms for optimization of value at risk. In: Pardalos, P.M., Tsitsiringos, V.K. (eds.) Financial Engineering, E-Commerce and Supply Chain, pp. 129–157. Kluwer Academic Publishers, Dordrecht (2000)

    Google Scholar 

  11. Lim, K.G.: Financial Valuation and Econometrics. World Scientific Press (2011)

    Google Scholar 

  12. Lodwick, W.A.: Fuzzy Optimization: Recent Advances and Applications, Studies in Fuzziness and Soft Computing. Springer, Berlin (2010)

    Google Scholar 

  13. Markowitz, H.M.: Portfolio selection. J. Fin. 7(1), 77–91 (1952)

    Google Scholar 

  14. Markowitz, H.M.: Portfolio Selection: Efficient Diversification of Investments. Wiley, Chapman and Hall Limited, New York, London (1959)

    Google Scholar 

  15. Mausser, H., Rosen, D.: Applying scenario optimization to portfolio credit risk. Algo Res. Q. 2(2), 19–33 (1999)

    Google Scholar 

  16. Mausser, H., Rosen, D.: Efficient risk/return frontiers for credit risk. Algo Res. Q. 2(4), 35–48 (1999)

    Google Scholar 

  17. Mcneil, A.J., Frey, R., Embrechts, P., Quantitative Risk Management—Concepts, Techniques and Tools. Princeton Series in Finance. Princeton University Press (2005)

    Google Scholar 

  18. Panjer, H.H.: Operational Risk: Modeling Analytics. Wiley Series in Probability and Statistics, New York (2006)

    Book  MATH  Google Scholar 

  19. Rao, S.S.: Engineering Optimization: Theory and Practice, 4th edn. Wiley, New York (2009)

    Book  Google Scholar 

  20. Rockafellar, R.T., Uryasev, S., Zabarankin, M.: Risk tuning with generalized linear regression. Math. Oper. Res. 33(3), 712–729 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  21. Rockafellar, R.T., Uryasev, S., Zabarankin, M.: Deviation Measures in Generalized Linear Regression. Research Report 2002–9, Department of Industrial and Systems Engineering, University of Florida, Gainesville (2002)

    Google Scholar 

  22. Ryan, T.P.: Modern Regression Methods, 2nd edn. Wiley Series in Probability and Statistics. Wiley (2009)

    Google Scholar 

  23. Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1(1), 3–28 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  24. Zappe, C., Albright, S.C., Winston, W.L.: Data Analytics, Optimization and Simulation Modeling, 4th edn. Ceneage Learning India, New Delhi (2011)

    Google Scholar 

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Authors and Affiliations

  1. Samsung R&D Institute Delhi, Noida, Uttar Pradesh, India

    Arindam Chaudhuri

  2. School of Information Technology, Indian Institute of Technology, Takshashila Building, Kharagpur, India

    Soumya K. Ghosh

Authors
  1. Arindam Chaudhuri
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  2. Soumya K. Ghosh
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Correspondence to Arindam Chaudhuri .

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Chaudhuri, A., Ghosh, S.K. (2016). Simulation Results. In: Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory. Studies in Fuzziness and Soft Computing, vol 331. Springer, Cham. https://doi.org/10.1007/978-3-319-26039-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-26039-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26037-2

  • Online ISBN: 978-3-319-26039-6

  • eBook Packages: EngineeringEngineering (R0)

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