December 2018
Beginner
720 pages
15h 32m
English
Content preview from Python for Finance, 2nd Edition
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Chapter 20. Portfolio Valuation
Price is what you pay. Value is what you get.
Warren Buffet
By now, the whole approach for building the DX derivatives analytics package—and its associated benefits—should be clear. By strictly relying on Monte Carlo simulation as the only numerical method, the approach accomplishes an almost complete modularization of the analytics package:
- Discounting
-
The relevant risk-neutral discounting is taken care of by an instance of the
dx.constant_short_rateclass. - Relevant data
-
Relevant data, parameters, and other input are stored in (several) instances of the
dx.market_environmentclass. - Simulation objects
-
Relevant risk factors (underlyings) are modeled as instances of one of three simulation classes:
-
dx.geometric_brownian_motion -
dx.jump_diffusion -
dx.square_root_diffusion
-
- Valuation objects
-
Options and derivatives to be valued are modeled as instances of one of two valuation classes:
-
dx.valuation_mcs_european -
dx.valuation_mcs_american
-
One last step is missing: the valuation of possibly complex portfolios of options and derivatives. To this end, the following requirements shall be satisfied:
- Nonredundancy
-
Every risk factor (underlying) is modeled only once and potentially used by multiple valuation objects.
- Correlations
-
Correlations between risk factors have to be accounted for.
- Positions
-
An option position, for example, consists of a certain number of option contracts.
However, although it is in principle allowed (it ...
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