Financial Modeling A Backward Stochastic Differential Equations Pers
Backward stochastic differential equations (BSDEs) provide a general mathematical framework for solving pricing and risk management questions of financial derivatives. They are of growing importance for nonlinear pricing problems such as CVA computations
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1. Achdou, Y., & Pironneau, O. (2005). Computational methods for option pricing. Philadelphia: SIAM. 2. Albanese, C., Campolieti, G., Carr, P., & Lipton, A. (2001). Black–Scholes goes hypergeometric. Risk, December 2001, 99–103. 3. Alvarez, O., & Tourin, A. (1996). Viscosity solutions of nonlinear integro-differential equations. Annales de l’Institut Henri Poincaré (C) Analyse non linéaire, 13(3), 293–317. 4. Amadori, A. L. (2003). Nonlinear integro-differential evolution problems arising in option pricing: a viscosity solutions approach. Differential and Integral Equations, 16(7), 787–811. 5. Amadori, A. L. (2007). The obstacle problem for nonlinear integro-differential operators arising in option pricing. Ricerche Di Matematica, 56(1), 1–17. 6. Andersen, L., & Brotherton-Ratcliffe, R. (1996). Exact exotics. Risk, October 1996, 85–89. 7. Andersen, L., & Sidenius, J. (2004). Extensions to the Gaussian copula: random recovery and random factor loadings. The Journal of Credit Risk, 1(1), 29–70. 8. Assefa, S., Bielecki, T., Crépey, S., & Jeanblanc, M. (2011). CVA computation for counterparty risk assessment in credit portfolios. In T. Bielecki, D. Brigo, & F. Patras (Eds.), Credit risk frontiers (pp. 397–436). New York: Wiley. 9. Avellaneda, M. (1999). Minimum-relative entropy calibration of asset-pricing models. International Journal of Theoretical and Applied Finance, 1(4), 447–472. 10. Avellaneda, M., Bu, R., Friedman, C., Grandchamp, N., Kruk, L., & Newman, J. (2001). Weighted Monte Carlo: a new technique for calibrating asset-pricing models. International Journal of Theoretical and Applied Finance, 4(1), 91–119. 11. Avellaneda, M., Friedman, C., Holmes, R., & Samperi, D. (1997). Calibrating volatility surfaces via relative-entropy minimization. Applied Mathematical Finance, 41, 37–64. 12. Avellaneda, M., & Laurence, P. (2000). Quantitative modeling of derivative securities from theory to practice. London: Chapman & Hall. 13. Avellaneda, M., Levy, A., & Paras, A. (1995). Pricing and hedging derivative securities in markets with uncertain volatilities. Applied Mathematical Finance, 2(2), 73–88. 14. Bachelier, L. (1900). Théorie de la spéculation. Annales Scientifiques de L’École Normale Supérieure, 17, 21–86. 15. Balland, P. (2002). Deterministic implied volatility models. Quantitative Finance, 2(1), 31– 44. 16. Bally, V., Caballero, E., Fernandez, B., & El-Karoui, N. (2002). Reflected BSDE’s PDE’s and variational inequalities (INRIA Technical Report No. 4455). 17. Bally, V., & Matoussi, A. (2001). Weak solutions for SPDEs and backward doubly stochastic differential equations. Journal of Theoretical Probability, 14(1), 125–164. 18. Bally, V., & Pagès, G. (2003). A quantization algorithm for solving multi-dimensional discrete-time optimal stopping problems. Bernoulli, 9(6), 1003–1049. S. Crépey, Financial Modeling, Springer Finance, DOI 10.1007/978-3-642-37113-4, © Springer-Verlag Berlin Heidelberg 2013
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References
19. Bally, V., Pagès, G., & Printems, J. (2001). A stochastic quantization method for nonl
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