Python for Finance: Analyze Big Financial Data

Conclusions

This chapter deals with methods and techniques important to the application of Monte

Carlo simulation in finance. In particular, it shows how to generate (pseudo)random

numbers based on different distribution laws. It proceeds with the simulation of random

variables and stochastic processes, which is important in many financial areas. Two

application areas are discussed in some depth in this chapter: valuation of options with

European and American exercise and the estimation of risk measures like value-at-risk and

credit value adjustments.

The chapter illustrates that Python in combination with NumPy is well suited to

implementing even such computationally demanding tasks as the valuation of American

options by Monte Carlo simulation. This is mainly due to the fact that the majority of

functions and classes of NumPy are implemented in C, which leads to considerable speed

advantages in general over pure Python code. A further benefit is the compactness and

readability of the resulting code due to vectorized operations.