By Michael C. Fu, Robert A. Jarrow, Ju-Yi Yen, Robert J Elliott
This self-contained quantity brings jointly a set of chapters through the most amazing researchers and practitioners within the fields of mathematical finance and monetary engineering. featuring cutting-edge advancements in idea and perform, the Festschrift is devoted to Dilip B. Madan at the celebration of his sixtieth birthday.
Specific themes coated include:
* conception 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 important source for graduate scholars, researchers, and practitioners in mathematical finance and fiscal 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
The remaining classes, including the NCP, each had three parameters. f. estimation methods. f. of the symmetric VG and the associated stochastic process. This introduction of the VG material is expressed, verbatim, as follows in both  and : The fourth parametric class is motivated by the derivation of the t distribution proposed by Praetz (1972). Praetz took the variance of the normal to be uncertain with reciprocal of the variance distributed as a gamma variable. The characteristic function of this distribution is not known in closed form, nor is it known what continuous time stochastic process gives rise to such a period-one distribution.
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  the choice ψ(v) = cos v, −∞ < v < ∞ is made, so Ti = cos ωXi , i = 1, . . , n. f. of T is given by ∞ g(t) = 2n φX (nω)qn (t)(π(1 − t2 )1/2 )−1 , (13) n=0 where q0 (y) = 1 and qn (y), n ≥ 1, is the nth Chebyshev polynomial.
World Scientiﬁc, 2000. 3. F. Fama. The behaviour of stock-market prices. J. Business, 38:34–105,1965. 4. A. Feuerverger and P. McDunnough. On the eﬃciency of empirical characteristic function procedures. J. R. Statist. , Ser. B, 43:20–27, 1981. 5. R. Finlay and E. Seneta. Stationary-increment Student and Variance-Gamma processes. J. Appl. , 43:441–453, 2006. 6. T. Fung and E. Seneta. Operations Research Letters, in press, 2006. 7. W. Harrar, E. K. Gupta. G. distributions. J. Multivariate Analysis, 97:1467–1475,2006.