Measuring and hedging financial risks in dynamical world
| dc.creator | Karoui, Nicole El | |
| dc.date | 2003-05-01 | |
| dc.date.accessioned | 2026-07-07T12:11:08Z | |
| dc.date.available | 2026-07-07T12:11:08Z | |
| dc.description | Financial markets have developed a lot of strategies to control risks induced by market fluctuations. Mathematics has emerged as the leading discipline to address fundamental questions in finance as asset pricing model and hedging strategies. History began with the paradigm of zero-risk introduced by Black & Scholes stating that any random amount to be paid in the future may be replicated by a dynamical portfolio. In practice, the lack of information leads to ill-posed problems when model calibrating. The real world is more complex and new pricing and hedging methodologies have been necessary. This challenging question has generated a deep and intensive academic research in the 20 last years, based on super-replication (perfect or with respect to confidence level) and optimization. In the interplay between theory and practice, Monte Carlo methods have been revisited, new risk measures have been back-tested. These typical examples give some insights on how may be used mathematics in financial risk management. | |
| dc.identifier | https://arxiv.org/abs/math/0305010 | |
| dc.identifier | http://arxiv.org/abs/math/0305010 | |
| dc.identifier | Proceedings of the ICM, Beijing 2002, vol. 3, 773--784 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/210131 | |
| dc.subject | Probability | |
| dc.subject | Risk Management | |
| dc.subject | 35B37, 60G44, 60G70, 65C30, 91B20 | |
| dc.title | Measuring and hedging financial risks in dynamical world | |
| dc.type | text |