Financial Modelling in Python

1.1 Why Python?

1.2 Common misconceptions about Python.

1.3 Roadmap for this book.

2 The PPF Package.

2.1 PPF topology.

2.2 Unit testing.

2.3 Building and installing PPF.

3 Extending Python from C++.

3.1 Boost.Date Time types.

3.2 Boost.MultiArray and special functions.

3.3 NumPy arrays.

4 Basic Mathematical Tools.

4.1 Random number generation.

4.2 N(.)

4.3 Interpolation.

4.4 Root finding.

4.5 Linear algebra.

4.6 Generalised linear least squares.

4.7 Quadratic and cubic roots.

4.8 Integration.

5 Market: Curves and Surfaces.

5.1 Curves.

5.2 Surfaces.

5.3 Environment.

6 Data Model.

6.1 Observables.

6.2 Flows.

6.3 Adjuvants.

6.4 Legs.

6.5 Exercises.

6.6 Trades.

6.7 Trade utilities.

7 Timeline: Events and Controller.

7.1 Events.

7.2 Timeline.

7.3 Controller.

8 The Hull–White Model.

8.1 A component-based design.

8.2 The model and model factories.

8.3 Concluding remarks.

9 Pricing using Numerical Methods.

9.1 A lattice pricing framework.

9.2 A Monte-Carlo pricing framework.

9.3 Concluding remarks.

10 Pricing Financial Structures in Hull–White.

10.1 Pricing a Bermudan.

10.2 Pricing a TARN.

10.3 Concluding remarks.

11 Hybrid Python/C++ Pricing Systems.

11.1 nth imm of year revisited.

11.2 Exercising nth imm of year from C++.

12 Python Excel Integration.

12.1 Black–scholes COM server.

12.2 Numerical pricing with PPF in Excel.

Appendices.

A Python.

A.1 Python interpreter modes.

A.2 Basic Python.

A.3 Conclusion.

B Boost.Python.

B.1 Hello world.

B.2 Classes, constructors and methods.

B.3 Inheritance.

B.4 Python operators.

B.5 Functions.

B.6 Enums.

B.7 Embedding.

B.8 Conclusion.

C Hull–White Model Mathematics.

D Pickup Value Regression.

Bibliography.

Index.