Advances in Mathematical Finance by Michael C. Fu, Robert A. Jarrow, Ju-Yi Yen, Robert J Elliott

By Michael C. Fu, Robert A. Jarrow, Ju-Yi Yen, Robert J Elliott

This self-contained quantity brings jointly a set of chapters by way of probably the most special researchers and practitioners within the fields of mathematical finance and fiscal engineering. proposing cutting-edge advancements in idea and perform, the Festschrift is devoted to Dilip B. Madan at the party of his sixtieth birthday.

Specific subject matters coated include:

* thought and alertness of the Variance-Gamma process

* Lévy approach pushed fixed-income and credit-risk types, together with CDO pricing

* Numerical PDE and Monte Carlo methods

* Asset pricing and derivatives valuation and hedging

* Itô formulation for fractional Brownian motion

* Martingale characterization of asset expense bubbles

* software valuation for credits derivatives and portfolio management

Advances in Mathematical Finance is a necessary source for graduate scholars, researchers, and practitioners in mathematical finance and monetary engineering.

Contributors: H. Albrecher, D. C. Brody, P. Carr, E. Eberlein, R. J. Elliott, M. C. Fu, H. Geman, M. Heidari, A. Hirsa, L. P. Hughston, R. A. Jarrow, X. Jin, W. Kluge, S. A. Ladoucette, A. Macrina, D. B. Madan, F. Milne, M. Musiela, P. Protter, W. Schoutens, E. Seneta, okay. Shimbo, R. Sircar, J. van der Hoek, M.Yor, T. Zariphopoulou

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Additional resources for Advances in Mathematical Finance

Sample text

Springer, 1996. 8. C. Fu. Stochastic gradient estimation. Chapter 19 in Handbooks in Operations Research and Management Science: Simulation, eds. G. L. Nelson, Elsevier, 2006. 9. C. Q. Hu, Sensitivity analysis for Monte Carlo simulation of option pricing. Probability in the Engineering and Informational Sciences, 9:417–446, 1995. 10. C. Q. Hu. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Kluwer Academic, 1997. 11. C. B. B. Madan, Y. Su, and R. Wu. Pricing American options: A comparison of Monte Carlo simulation approaches.

Wu. Pricing American options: A comparison of Monte Carlo simulation approaches. Journal of Computational Finance, 4:39–88, 2001. 12. C. B. Madan and T. Wang. Pricing continuous Asian options: A comparison of Monte Carlo and Laplace transform inversion methods. Journal of Computational Finance, 2:49–74, 1999. 13. P. Glasserman. Gradient Estimation Via Perturbation Analysis. Kluwer Academic, 1991. 14. P. Glasserman. Monte Carlo Methods in Financial Engineering. Springer, 2003. 15. A. B. Madan. Pricing American options under variance gamma.

N, where ω > 0 is a parameter chosen at will to control the loss of information in going to the transformed sample. ), if the function ψ is an even (or odd) function, the random variable T will be symmetrically distributed on (−b/ω, b/ω]. f. depends on the same parameters as the distribution of X, if it is explicitly available, these parameters may be estimated by maximum likelihood procedures from the transformed observations T1 , T2 , . . , Tn . In Madan and Seneta [18] the choice ψ(v) = cos v, −∞ < v < ∞ is made, so Ti = cos ωXi , i = 1, .

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