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Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
Services
Author
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I. Basic math.
II. Pricing and Hedging.
III. Explicit techniques.
IV. Data Analysis.
V. Implementation tools.
1. Finite differences.
2. Gauss-Hermite Integration.
3. Asymptotic expansions.
4. Monte-Carlo.
A. Generation of random samples.
a. Uniform [0,1] random variable.
b. Inverting cumulative distribution.
c. Accept/reject procedure.
d. Normal distribution. Box-Muller procedure.
e. Gibbs sampler.
B. Acceleration of convergence.
C. Longstaff-Schwartz technique.
D. Calculation of sensitivities.
5. Convex Analysis.
VI. Basic Math II.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Generation of random samples.


eneration of random variables is well covered in boost (C++) and scipy (python) libraries.




a. Uniform [0,1] random variable.
b. Inverting cumulative distribution.
c. Accept/reject procedure.
d. Normal distribution. Box-Muller procedure.
e. Gibbs sampler.

Notation. Index. Contents.


















Copyright 2007