Quantitative Analysis
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
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.
A. Option pricing formula for an economy with stochastic riskless rate.
B. T-forward measure.
C. HJM.
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.

HJM.


e introduce the forward rate $f(t,s)$ connected to the defaultless bond price MATH by the relationship MATH where the $t$ is the observation time and $T$ is the maturity of the bond. We are given a reference filtration $\QTR{cal}{F}_{t}$ and the SDE MATH in the real world, where the MATH and MATH are $\QTR{cal}{F}_{t}$ -adapted vector and matrix valued processes.

Our intention is to compute an SDE for the MATH by direct differentiation of (*), to produce a risk neutral measure from Girsanov's theorem and a requirement that MATH would drift with riskless rate MATH in the risk neutral world and finally, to compute the risk neutral world version of the (**).

We introduce the convenience notations MATH and MATH for any function of two variables $h$ . We have MATH MATH MATH MATH Hence, MATH MATH We introduce MATH for some $\QTR{cal}{F}_{t}$ -adapted process $\theta_{t}$ and continue MATH MATH By existence of the risk neutral measure ( Risk neutral Brownian motion ) there has to be a $\theta_{t}$ such that MATH We differentiate the above relationship with respect to the variable $T$ : MATH and substitute (***) and (****) into (**): MATH MATH

Summary

If the riskless bond and the forward rate are given by the real world SDEs (*) and (**) then in the risk neutral world the bond and forward rates evolve according to MATH

MATH (Bond SDE)





Notation. Index. Contents.


















Copyright 2007