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Desirable Portfolios in Fixed Income Markets: Application to Credit Risk Premiums

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  • José Garrido

    (Department of Mathematics and Statistics, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Ramin Okhrati

    (Mathematical Sciences, University of Southampton, Southampton SO17 1BJ, UK)

Abstract

An arbitrage portfolio provides a cash flow that can never be negative at zero cost. We define the weaker concept of a “desirable portfolio” delivering cash flows with negative risk at zero cost. Although these are not completely risk-free investments and subject to the risk measure used, they can provide attractive investment opportunities for investors. We investigate in detail the theoretical aspects of this portfolio selection procedure and the existence of such opportunities in fixed income markets. Then, we present two applications of the theory: one in analyzing market integration problem and the other in gauging the credit quality of defaultable bonds in a portfolio. We also discuss the model calibration and provide some numerical illustrations.

Suggested Citation

  • José Garrido & Ramin Okhrati, 2018. "Desirable Portfolios in Fixed Income Markets: Application to Credit Risk Premiums," Risks, MDPI, vol. 6(1), pages 1-21, March.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:1:p:23-:d:136998
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    References listed on IDEAS

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    1. R. Rockafellar & Stan Uryasev & Michael Zabarankin, 2006. "Generalized deviations in risk analysis," Finance and Stochastics, Springer, vol. 10(1), pages 51-74, January.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Chen, Zhiwu & Knez, Peter J, 1995. "Measurement of Market Integration and Arbitrage," Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 287-325.
    4. Alejandro Balbás & José Garrido & Silvia Mayoral, 2009. "Properties of Distortion Risk Measures," Methodology and Computing in Applied Probability, Springer, vol. 11(3), pages 385-399, September.
    5. Aharon Ben‐Tal & Marc Teboulle, 2007. "An Old‐New Concept Of Convex Risk Measures: The Optimized Certainty Equivalent," Mathematical Finance, Wiley Blackwell, vol. 17(3), pages 449-476, July.
    6. Kempf, Alexander & Korn, Olaf, 1998. "Trading System and Market Integration," Journal of Financial Intermediation, Elsevier, vol. 7(3), pages 220-239, July.
    7. J. Dhaene & R. J. A. Laeven & S. Vanduffel & G. Darkiewicz & M. J. Goovaerts, 2008. "Can a Coherent Risk Measure Be Too Subadditive?," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(2), pages 365-386, June.
    8. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    9. Daníelsson, Jón & Jorgensen, Bjørn N. & Samorodnitsky, Gennady & Sarma, Mandira & de Vries, Casper G., 2013. "Fat tails, VaR and subadditivity," Journal of Econometrics, Elsevier, vol. 172(2), pages 283-291.
    10. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    11. Boero, G. & Torricelli, C., 1996. "A comparative evaluation of alternative models of the term structure of interest rates," European Journal of Operational Research, Elsevier, vol. 93(1), pages 205-223, August.
    12. Hans Föllmer & Stefan Weber, 2015. "The Axiomatic Approach to Risk Measures for Capital Determination," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 301-337, December.
    13. Steven Kou & Xianhua Peng & Chris C. Heyde, 2013. "External Risk Measures and Basel Accords," Mathematics of Operations Research, INFORMS, vol. 38(3), pages 393-417, August.
    14. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    Cited by:

    1. Ahmed Imran Hunjra & Tahar Tayachi & Rashid Mehmood & Sidra Malik & Zoya Malik, 2020. "Impact of Credit Risk on Momentum and Contrarian Strategies: Evidence from South Asian Markets," Risks, MDPI, vol. 8(2), pages 1-14, April.

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