Generic Valuation Class

As with the generic simulation class, we instantiate an object of the valuation class by

providing only a few inputs (in this case, four):

name

A string object as a name for the model simulation object

underlying

An instance of a simulation class representing the underlying

mar_env

An instance of the market_environment class

payoff_func

A Python string containing the payoff function for the option/derivative

The generic class has three methods:

update

This method updates selected valuation parameters (attributes).

delta

This method calculates a numerical value for the Delta of an option/derivative.

vega

This method calculates the Vega of an option/derivative.

Equipped with the background knowledge from the previous chapters about the DX library,

the generic valuation class as presented in Example 17-1 should be almost self-

explanatory; where appropriate, inline comments are also provided. We again present the

class in its entirety first and highlight selected topics immediately afterward and in the

subsequent sections.

Example 17-1. Generic valuation class

DX Library Valuation

valuation_class.py

class valuation_class(object):
”’ Basic class for single-factor valuation.

            Attributes

            ==========

            name    :   string

                            name    of  the object

            underlying  :

                            instance    of  simulation  class

            mar_env :   instance    of  market_environment

                            market  environment data    for valuation

            payoff_func :   string

                            derivatives payoff  in  Python  syntax

                            Example:    ‘np.maximum(maturity_value  -   100,    0)’

                            where   maturity_value  is  the NumPy   vector  with

                            respective  values  of  the underlying

                            Example:    ‘np.maximum(instrument_values   -   100,    0)’

                            where   instrument_values   is  the NumPy   matrix  with

                            values  of  the underlying  over    the whole   time/path   grid