Conclusions

This chapter develops all the tools and classes needed for the simulation of the three

stochastic processes of interest: geometric Brownian motions, jump diffusions, and

square-root diffusions. The chapter presents a function to conveniently generate standard

normally distributed random numbers. It then proceeds by introducing a generic model

simulation class. Based on this foundation, the chapter introduces three specialized

simulation classes and presents use cases for these classes.

To simplify future imports, we can again use a wrapper module called dx_simulation.py,

as presented in Example 16-6.

Example 16-6. Wrapper module for simulation components

DX Library Simulation

dx_simulation.py

import numpy as np
import pandas as pd

from dx_frame import *
from sn_random_numbers import sn_random_numbers
from simulation_class import simulation_class
from geometric_brownian_motion import geometric_brownian_motion
from jump_diffusion import jump_diffusion
from square_root_diffusion import square_root_diffusion

As with the first wrapper module, dx_frame.py, the benefit is that a single import

statement makes available all simulation components in a single step:

from dx_simulation import   *

Since dx_simulation.py also imports everything from dx_frame.py, this single import in

fact exposes all functionality developed so far. The same holds true for the enhanced init

file in the dx directory, as shown in Example 16-7.

Example 16-7. Enhanced Python packaging file

DX Library

packaging file

init.py

import numpy as np
import pandas as pd
import datetime as dt

frame

from get_year_deltas import get_year_deltas
from constant_short_rate import constant_short_rate
from market_environment import market_environment

simulation

from sn_random_numbers import sn_random_numbers
from simulation_class import simulation_class
from geometric_brownian_motion import geometric_brownian_motion
from jump_diffusion import jump_diffusion
from square_root_diffusion import square_root_diffusion