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Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
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I. Basic math.
II. Pricing and Hedging.
1. Basics of derivative pricing I.
A. Single step binary tree argument. Risk neutral probability. Delta hedging.
B. Why Ito process?
C. Existence of risk neutral measure via Girsanov's theorem.
D. Self-financing strategy.
E. Existence of risk neutral measure via backward Kolmogorov's equation. Delta hedging.
F. Optimal utility function based interpretation of delta hedging.
2. Change of numeraire.
3. Basics of derivative pricing II.
4. Market model.
5. Currency Exchange.
6. Credit risk.
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.

Optimal utility function based interpretation of delta hedging.

e consider choosing optimal trading strategy under the real world probability measure. The optimality is defined in terms of maximization of utility function of final wealth: MATH where the $T$ is the time horizon, $W_{T}$ is the final wealth and $U\,\ $ is a concave function. $U\left( x\right) $ is slightly increasing for large $x$ because we would like to make money but not too much because of risk aversion. Also, $U\,\ $ is sharply decreasing for negative $x$ because we do not like to loose money. We are going to show that such setup leads to delta hedging if the market is complete.

The reference for this section is [Yang] .


We introduce the following notations:

$F_{t}$ is the equilibrium (optimal trading strategy, expected utility maximizing) price of an option,


$S_{t}$ is the stock price,

$M_{t}$ is the amount on the margin account.

The sum MATH is the "wealth".

The pair MATH is trading strategy.

We introduce the lower case notation for all processes as follows: MATH for any $W_{t}$ .




We calculate MATH We substitute upper case values for the lower case values: MATH and MATH We equate MATH and MATH : MATH The $dt$ -terms cancel and we arrive to the claim.


We use the notation MATH The state variable is MATH . MATH MATH




We calculate MATH MATH MATH Hence, MATH MATH The function $u$ does not depend on $n$ directly (because the maximization removes the dependency). However, the $w$ variable depends on $n$ : MATH hence MATH

Therefore, MATH MATH Taking the linear combination MATH Note that MATH hence MATH and the claim MATH follows.

We obtained the Black-Scholes equation.

Notation. Index. Contents.

Copyright 2007