Python for Finance: Analyze Big Financial Data

(Elle) #1

the expected payoff of not exercising (continuation value).


Equation 17-5. Value of American option at any given date


In Equation 17-5, the inner value is of course easily calculated. The continuation value is


what makes it a bit trickier. The Longstaff-Schwartz (2001) model approximates this value


by a regression, as presented in Equation 17-6. There, i stands for the current simulated


path, D is the number of basis functions for the regression used,


*

are the optimal


regression parameters, and bd is the regression function numbered d.


Equation 17-6. Regression-based approximation of continuation value


The optimal regression parameters are the result of the solution of the least-squares


regression problem presented in Equation 17-7. Here, is the actual


continuation value at date tm for path i (and not a regressed/estimated one).


Equation 17-7. Ordinary least-squares regression


This completes the basic (mathematical) tool set to value an American option by MCS.


The Valuation Class


Example 17-4 presents the class for the valuation of options and derivatives with


American exercise. There is one noteworthy step in the implementation of the LSM


algorithm in the present_value method (which is also commented on inline): the optimal


decision step. Here, it is important that, based on the decision that is made, the LSM


algorithm takes either the inner value or the actual continuation value — and not the

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