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Portfolio optimization with insider’s initial information and counterparty risk

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Abstract

We study the gain of an insider having private information which concerns the default risk of a counterparty. More precisely, the default time τ is modelled as the first time a stochastic process hits a random threshold L. The insider knows this threshold (as it can be the case for the manager of the counterparty) and this information is modelled by using an initial enlargement of filtration. The standard investors only observe the value of the threshold at the default time and estimate the default event by its conditional density process. The financial market consists of a risk-free asset and a risky asset whose price is exposed to a sudden jump at the default time of the counterparty. All investors aim to maximize the expected utility from terminal wealth given their own information at the initial date. We solve the optimization problem under short-selling and buying constraints and we compare through numerical illustrations the optimal processes for the insider and the standard investors.

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Notes

  1. We leave for future work the case where the threshold can be adjusted dynamically.

  2. The proof is based on the relationship between the two key processes p() and α(τ ). More precisely, the coefficient \(\frac{p_{T}(\ell)}{\alpha_{T}(\tau _{\ell})}\) coincides with the ratio between the Lagrange multipliers for the insider and the standard investor, respectively.

  3. Meaning that for any t≥0, the function \(Y^{i}_{t}(\cdot)\) is \(\mathcal{F}_{t}\otimes\mathcal{B}(\mathbb{R}_{+})\)-measurable.

References

  1. Amendinger, J.: Martingale representation theorems for initially enlarged filtrations. Stoch. Process. Appl. 89, 101–116 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  2. Amendinger, J., Becherer, D., Schweizer, M.: A monetary value for initial information in portfolio optimization. Finance Stoch. 7, 29–46 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  3. Baudoin, F.: Modelling anticipations on financial markets. In: Carmona, R.A., et al. (eds.) Paris–Princeton Lectures on Mathematical Finance. Lecture Notes in Mathematics, vol. 1814, pp. 43–94. Springer, New York (2003)

    Google Scholar 

  4. Beneš, V.E.: Existence of optimal strategies based on specified information for a class of stochastic decision problems. SIAM J. Comput. Optim. 8, 179–189 (1970)

    MATH  Google Scholar 

  5. Bielecki, T.R., Rutkowski, M.: Credit Risk: Modeling, Valuation and Hedging. Springer, Berlin (2002)

    Google Scholar 

  6. El Karoui, N., Jeanblanc, M., Jiao, Y.: What happens after a default: the conditional density approach. Stoch. Process. Appl. 120, 1011–1032 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  7. El Karoui, N., Jeanblanc, M., Jiao, Y., Zargari, B.: Conditional default probability and density. In: Kabanov, Yu., et al. (eds.) Inspired by Finance: the Musiela Festschrift, pp. 201–219. Springer, Berlin (2014)

    Chapter  Google Scholar 

  8. Grorud, A., Pontier, M.: Insider trading in a continuous time market model. Int. J. Theor. Appl. Finance 1, 331–347 (1998)

    Article  MATH  Google Scholar 

  9. Hillairet, C.: Existence of an equilibrium on a financial market with discontinuous prices, asymmetric information and non trivial initial σ-fields. Math. Finance 15, 99–117 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  10. Hillairet, C., Jiao, Y.: Information asymmetry in pricing of credit derivatives. Int. J. Theor. Appl. Finance 14, 611–633 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  11. Hillairet, C., Jiao, Y.: Credit risk with asymmetric information on the default threshold. Stochastics 84, 183–198 (2012)

    MATH  MathSciNet  Google Scholar 

  12. Howard, R.: Dynamic Programming and Markov Processes. MIT Press, Cambridge (1960)

    MATH  Google Scholar 

  13. Jacod, J.: Calcul Stochastique et Problèmes de Martingales. Lecture Notes in Mathematics, vol. 714. Springer, New York (1979)

    MATH  Google Scholar 

  14. Jacod, J.: Grossissement initial, hypothèse (H’) et théorème de Girsanov. In: Jeulin, T., Yor, M. (eds.) Grossissements de Filtrations: Exemples et Applications. Séminaire de Calcul Stochastique 1982/83. Lecture Notes in Mathematics, vol. 1118, pp. 15–35. Springer, New York (1985)

    Chapter  Google Scholar 

  15. Jeulin, J.: Semi-martingales et Grossissement d’une Filtration. Lecture Notes, vol. 833. Springer, Berlin (1980)

    MATH  Google Scholar 

  16. Jiao, Y., Pham, H.: Optimal investment with counterparty risk: a default-density model approach. Finance Stoch. 15, 725–753 (2011)

    Article  MathSciNet  Google Scholar 

  17. Kharroubi, I., Lim, T.: Progressive enlargement of filtrations and backward stochastic differential equations with jumps. J. Theor. Probab. 27, 683–724 (2014)

    Article  MathSciNet  Google Scholar 

  18. Merton, R.: Lifetime portfolio selection under uncertainty: the continuous time case. Rev. Econ. Stat. 51, 239–265 (1969)

    Article  Google Scholar 

  19. Merton, R.: Optimal consumption and portfolio rules in a continuous-time model. J. Econ. Theory 3, 373–413 (1971)

    Article  MATH  MathSciNet  Google Scholar 

  20. Song, S.: Optional splitting formula in a progressively enlarged filtration. ESAIM Probab. Stat. (2014, to appear). Available at arXiv:1208.4149v2

Download references

Acknowledgements

This research is part of a project of Europlace Institute of Finance. We thank Laurent Denis, Nicole El Karoui, Monique Jeanblanc, Arturo Kohatsu-Higa, Huyên Pham, Marek Rutkowski, Abass Sagna, Nizar Touzi and Lioudmila Vostrikova for discussions. Caroline Hillairet is supported by Chair Financial Risks of the Risk Foundation, Chair Derivatives of the Future sponsored by the Fédération Bancaire Française, Chair Finance and Sustainable Development sponsored by EDF and Calyon. Ying Jiao is supported by Alma Recherche and NSFC grant 11201010.

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Appendix

Appendix

We recall the canonical decomposition of \(\mathbb {G}^{M}\)-adapted (resp., \(\mathbb {G}^{M}\)-predictable) processes (see Jeulin [15], Lemmas 3.13 and 4.4).

Lemma A.1

1. For t≥0, any \(\mathcal {G}_{t}^{M}\)-measurable random variable can be written in the form

$$Y_t=1_{\tau>t}Y_t^0(\ell)+1_{\tau\leq t}Y_t^1(\tau), $$

where \(Y_{t}^{0}(\cdot)\) and \(Y_{t}^{1}(\cdot)\) are \(\mathcal {F}_{t}\otimes\mathcal{B}(\mathbb {R}_{+})\)-measurable.

2. Any \(\mathbb {G}^{M}\)-adapted process Y admits a decomposition of the form

$$Y_t=1_{\tau> t}Y_t^0(\ell)+1_{\tau\leq t}Y_t^1(\tau), $$

where Y 0(⋅) and Y 1(⋅) are \(\mathbb {F}\otimes\mathcal{B}(\mathbb {R}_{+})\)-adapted.Footnote 3

3. Any \(\mathbb {G}^{M}\)-predictable process Y admits a decomposition of the form

$$Y_t=1_{\tau\geq t}Y_t^0(\ell)+1_{\tau< t}Y_t^1(\tau), $$

where Y 0(⋅) and Y 1(⋅) are \(\mathcal {P}(\mathbb {F})\otimes\mathcal{B}(\mathbb {R}_{+})\)-measurable, \(\mathcal {P}(\mathbb {F})\) being the predictable σ-algebra associated with the filtration \(\mathbb {F}\).

Remark A.2

To compare with the case of a standard investor, we recall that any \(\mathcal {G}_{t}\)-measurable random variable Z t can be written as \(Z_{t}=1_{\tau>t} Z_{t}^{0}+1_{\tau\leq t}Z_{t}^{1}(\tau)\), where \(Z_{t}^{0}\) and \(Z_{t}^{1}(\cdot)\) are respectively \(\mathcal {F}_{t}\)-measurable and \(\mathcal {F}_{t}\otimes\mathcal{B}(\mathbb {R}_{+})\)-measurable.

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Hillairet, C., Jiao, Y. Portfolio optimization with insider’s initial information and counterparty risk. Finance Stoch 19, 109–134 (2015). https://doi.org/10.1007/s00780-014-0246-7

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