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Quantitative Analysis
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I. Basic math.
II. Pricing and Hedging.
III. Explicit techniques.
IV. Data Analysis.
V. Implementation tools.
VI. Basic Math II.
1. Real Variable.
A. Operations on sets and logical statements.
B. Fundamental inequalities.
C. Function spaces.
D. Measure theory.
E. Various types of convergence.
a. Uniform convergence and convergence almost surely. Egorov's theorem.
b. Convergence in probability.
c. Infinitely often events. Borel-Cantelli lemma.
d. Integration and convergence.
e. Convergence in Lp.
f. Vague convergence. Convergence in distribution.
F. Signed measures. Absolutely continuous and singular measures. Radon-Nikodym theorem.
G. Lebesgue differentiation theorem.
H. Fubini theorem.
I. Arzela-Ascoli compactness theorem.
J. Partial ordering and maximal principle.
K. Taylor decomposition.
2. Laws of large numbers.
3. Characteristic function.
4. Central limit theorem (CLT) II.
5. Random walk.
6. Conditional probability II.
7. Martingales and stopping times.
8. Markov process.
9. Levy process.
10. Weak derivative. Fundamental solution. Calculus of distributions.
11. Functional Analysis.
12. Fourier analysis.
13. Sobolev spaces.
14. Elliptic PDE.
15. Parabolic PDE.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Various types of convergence.


e study various convergence concepts for sequences of random variables MATH where $\Omega$ is an event space often identified with $\QTR{cal}{R}^{n}$ .

We will be frequently using the notation and techniques of the section ( operations on sets ).

The random variables are always assumed to be $\QTR{cal}{F-}$ measurable for some $\sigma$ -algebra $\QTR{cal}{F}$ : MATH or, equivalently, MATH In practical language these mean the following. The complete description of the world is given by the set $\Omega$ . The statements about the world are given by subsets of $\Omega$ . Some amount of information (unknown at present but will be known in the future) is represented by the algebra $\QTR{cal}{F}$ . This information $\QTR{cal}{F}$ has to be an algebra of sets because we will be making logical statements and derivations about our knowledge, see the section ( operations on sets ). We restrict our attention to the random variables that become deterministic if the information $\QTR{cal}{F}$ is given. Equivalently, $\QTR{cal}{F}$ contains all the randomness in the variables under consideration.




a. Uniform convergence and convergence almost surely. Egorov's theorem.
b. Convergence in probability.
c. Infinitely often events. Borel-Cantelli lemma.
d. Integration and convergence.
e. Convergence in Lp.
f. Vague convergence. Convergence in distribution.

Notation. Index. Contents.


















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