Quantitative Analysis.
Trading Platform.
Python for Excel.
Author.

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I.Basic math.
II.Pricing and Hedging.
III.Explicit techniques.
IV.Data Analysis.
V.Implementation tools.
VI.Basic Math II.
VII.Implementation tools II.
1.Calculational Linear Algebra.
2.Wavelet Analysis.
3.Finite element method.
4.Construction of approximation spaces.
5.Time discretization.
6.Variational inequalities.
7.Lattice approach to derivative pricing.
A.Basic definitions of the lattice approach.
B.Markov Generator for smooth process. Dyson decomposition. Kolmogorov equations.
C.Path-Integral representation of Markov generator.
D.Markov generator for piecewise smooth process.
E.Fast exponentiation.
Bibliography.
Forum Notation Index Contents

Lattice approach to derivative pricing.


he reference is [AlbaneseOnline]. The presentation here is partial. I (Konstantin Aslanidi) discontinued my investigation of that source because I was unable to find advantages of presented technique over the finite element method. I hesitated to remove this chapter from the notes and decided to keep it. There are few tricks and examples in the material that improve generic understanding of the subject. On personal level, I feel that studying the original source is too hard for the benefit. It is also avoidable because this is another solution for a solved problem. One would hope that in such situation the original articles would contain detailed derivations. Unfortunately, this is not the case. Some main stream motivational results are given with little or no proof.

The original's author did not respond to my e-mail.

Perhaps, with the partial help below the balance of effort versus benefit might be different for another reader.




A.Basic definitions of the lattice approach.
B.Markov Generator for smooth process. Dyson decomposition. Kolmogorov equations.
C.Path-Integral representation of Markov generator.
D.Markov generator for piecewise smooth process.
E.Fast exponentiation.

Forum Notation Index Contents


















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