Python for Finance: Analyze Big Financial Data

This mainly completes the discussion of the theoretical background for what follows. For a detailed exposition ...

Risk-Neutral Discounting Obviously, risk-neutral discounting is central to the risk-neutral valuation approach. W ...

(to account for different conventions) Results ======= delta_ ...

get_discount_factors : get discount factors given a list/array of datetime ...

Market Environments Market environment is “just” a name for a collection of other data and Python objects. How ...

def add_constant(self, key, constant): self.constants[key] = constant def get_constant(self, key): return self.constants[ ...

applications, market data and other data as well as Python objects are first collected, then a market_enviro ...

Conclusions This chapter provides the framework for the larger project of building a Python library to value opt ...

import dx ...

Further Reading Useful references in book form for the topics covered in this chapter are: Delbaen, Freddy and W ...

Chapter 16. Simulation of Financial Models The purpose of science is not to analyze or describe but to make u ...

Random Number Generation Random number generation is a central task of Monte Carlo simulation. [ 71 ] Chapter ...

...: fixed_se ...

Generic Simulation Class Object-oriented modeling — as introduced in Chapter 13 — allows inheritance of attribut ...

generate_time_grid : returns time grid for simulation get_instrument_ ...

sanity checks implemented. For example, the following line of code is considered a “success,” no matter if ...

Geometric Brownian Motion Geometric Brownian motion is a stochastic process as described in Equation 16-1 ...

returns Monte Carlo paths given the market environment ”’ def init(self, name, ...

In [ 1 ]: from dx import * In [ 2 ]: me_gbm = market_environment(‘me_gbm’, dt.datetime( 2015 , 1 , 1 )) In [ 3 ...

p2 = plt.plot(gbm.time_grid, paths_2[:, : 10 ], ‘r-.’) plt.grid(True) l1 = plt.legend([p1[ 0 ], p2[ 0 ]], [‘low vola ...