Read e-book online Advances in Mathematical Finance PDF

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

ISBN-10: 0817645446

ISBN-13: 9780817645441

ISBN-10: 0817645454

ISBN-13: 9780817645458

This self-contained quantity brings jointly a suite of chapters via essentially the most individual researchers and practitioners within the fields of mathematical finance and fiscal engineering. offering state of the art advancements in thought and perform, the Festschrift is devoted to Dilip B. Madan at the celebration of his sixtieth birthday.

Specific themes lined include:

* thought and alertness of the Variance-Gamma process

* Lévy technique 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 cost bubbles

* application valuation for credits derivatives and portfolio management

Advances in Mathematical Finance is a beneficial 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, ok. Shimbo, R. Sircar, J. van der Hoek, M.Yor, T. Zariphopoulou

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Additional info for Advances in Mathematical Finance

Example text

Efficient Monte Carlo and quasi-Monte Carlo option pricing under the Variance-Gamma model. Management Science, 52:1930–1944, 2006. 2. P. Boyle, M. Broadie, and P. Glasserman. Monte Carlo methods for security pricing. Journal of Economic Dynamics and Control, 21:1267–1321, 1997. 3. M. Broadie and P. Glasserman. Estimating security price derivatives using simulation. Management Science, 42:269–285, 1996. 4. P. Carr, H. B. Madan, and M. Yor. The fine structure of asset returns: An empirical investigation.

Random variables is transformed via Ti = ψ(ωXi ), i = 1, 2, . . , 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 , .

A variant of this estimation procedure was to be investigated in the next two papers [16], [18]. This is anticipated in [14] by mentioning the paper [4]. It was remarked that the estimate of θ for the NCP was generally small, thus arguing for a purely compound Poisson process, and against the stable laws. The VG distribution and process were yet to make their appearance. The VG was to share equal standing with the NCP in the next two papers [16] and [18]. An important difference between the VG and NCP models, however, is that the VG turns out to be a pure jump process, and the limit of compound Poisson processes.

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

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