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.
Similar content being viewed by others
Notes
We leave for future work the case where the threshold can be adjusted dynamically.
The proof is based on the relationship between the two key processes p(ℓ) and α(τ ℓ ). 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.
Meaning that for any t≥0, the function \(Y^{i}_{t}(\cdot)\) is \(\mathcal{F}_{t}\otimes\mathcal{B}(\mathbb{R}_{+})\)-measurable.
References
Amendinger, J.: Martingale representation theorems for initially enlarged filtrations. Stoch. Process. Appl. 89, 101–116 (2000)
Amendinger, J., Becherer, D., Schweizer, M.: A monetary value for initial information in portfolio optimization. Finance Stoch. 7, 29–46 (2003)
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)
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)
Bielecki, T.R., Rutkowski, M.: Credit Risk: Modeling, Valuation and Hedging. Springer, Berlin (2002)
El Karoui, N., Jeanblanc, M., Jiao, Y.: What happens after a default: the conditional density approach. Stoch. Process. Appl. 120, 1011–1032 (2010)
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)
Grorud, A., Pontier, M.: Insider trading in a continuous time market model. Int. J. Theor. Appl. Finance 1, 331–347 (1998)
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)
Hillairet, C., Jiao, Y.: Information asymmetry in pricing of credit derivatives. Int. J. Theor. Appl. Finance 14, 611–633 (2011)
Hillairet, C., Jiao, Y.: Credit risk with asymmetric information on the default threshold. Stochastics 84, 183–198 (2012)
Howard, R.: Dynamic Programming and Markov Processes. MIT Press, Cambridge (1960)
Jacod, J.: Calcul Stochastique et Problèmes de Martingales. Lecture Notes in Mathematics, vol. 714. Springer, New York (1979)
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)
Jeulin, J.: Semi-martingales et Grossissement d’une Filtration. Lecture Notes, vol. 833. Springer, Berlin (1980)
Jiao, Y., Pham, H.: Optimal investment with counterparty risk: a default-density model approach. Finance Stoch. 15, 725–753 (2011)
Kharroubi, I., Lim, T.: Progressive enlargement of filtrations and backward stochastic differential equations with jumps. J. Theor. Probab. 27, 683–724 (2014)
Merton, R.: Lifetime portfolio selection under uncertainty: the continuous time case. Rev. Econ. Stat. 51, 239–265 (1969)
Merton, R.: Optimal consumption and portfolio rules in a continuous-time model. J. Econ. Theory 3, 373–413 (1971)
Song, S.: Optional splitting formula in a progressively enlarged filtration. ESAIM Probab. Stat. (2014, to appear). Available at arXiv:1208.4149v2
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.
Author information
Authors and Affiliations
Corresponding author
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
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
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
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.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00780-014-0246-7
Keywords
- Asymmetric information
- Enlargement of filtrations
- Counterparty risk
- Optimal investment
- Duality
- Dynamic programming