On the existence of an optimal regression complexity in the Least-Squares Monte Carlo (LSM) framework for options pricing
نویسنده
چکیده
In this paper, we illustrate how to value American-style options using the Least-Squares Monte Carlo (LSM) approach proposed by Longstaff and Schwartz (2001) and investigate whether there exists an optimal regression complexity in the LSM framework for options pricing. In particular, we use the smoothing spline in the regression step, which allows us to control the regression complexity on a continuous scale with just one tuning parameter. Numerical results on American put options indicate that we need to use more than a linear regression, but as the regression becomes more complex, the accuracy of the LSM method quickly deteriorates.
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