Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
Services
Author
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I. Basic math.
1. Conditional probability.
2. Normal distribution.
3. Brownian motion.
4. Poisson process.
5. Ito integral.
6. Ito calculus.
7. Change of measure.
8. Girsanov's theorem.
9. Forward Kolmogorov's equation.
10. Backward Kolmogorov's equation.
11. Optimal control, Bellman equation, Dynamic programming.
A. Deterministic optimal control problem.
B. Stochastic optimal control problem.
C. Optimal stopping time problem. Free boundary problem.
II. Pricing and Hedging.
III. Explicit techniques.
IV. Data Analysis.
V. Implementation tools.
VI. Basic Math II.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Stochastic optimal control problem.


e extend the results of the previous section ( Deterministic optimal control problem ) to stochastic situation.

Proposition

(Optimal strategy with accrual) Let MATH be MATH where the process $X_{t}$ is given by the SDE MATH Then MATH satisfies MATH

Proof

We calculate MATH MATH Hence, MATH MATH MATH





Notation. Index. Contents.


















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