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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.
1. Basics of derivative pricing I.
2. Change of numeraire.
3. Basics of derivative pricing II.
4. Market model.
5. Currency Exchange.
6. Credit risk.
A. Delta hedging in situation of predictable jump I.
B. Delta hedging in situation of predictable jump II.
C. Backward Kolmogorov's equation for jump diffusion.
D. Risk neutral valuation in predictable jump size situation.
E. Examples of credit derivative pricing.
F. Credit correlation.
a. Generic Copula.
b. Gaussian copula.
c. Example: two dimensional Gaussian copula.
d. Simplistic Gaussian copula.
G. Valuation of CDO tranches.
7. Incomplete markets.
III. Explicit techniques.
IV. Data Analysis.
V. Implementation tools.
VI. Basic Math II.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Gaussian copula.


e utilize the statements ( Sklar theorem 1 ),( Sklar theorem 2 ) to execute a program MATH where MATH are jointly standard normal variables. For the first step, correlation matrix of MATH is the input data and we recover correlated uniform variables MATH according to the formula ( Sklar theorem 2 ). For the second step, the input data is a collection MATH of marginal distributions for each variable $\tilde{X}_{i}$ .

Thus, we can equip any collection of random variables MATH given by their marginal distribution with interdependency controlled by a correlation matrix.





Notation. Index. Contents.


















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