| dc.description.abstract | We replicate (in some parts) and extend Tompaidis and Yang’s (2014) analysis by
comparing the performance of Ordinary Least-Squares (OLS) Regression to
Tikhonov Regularization and Classification & Regression Trees (CART), and
study whether any polynomial among Chebyshev, Hermite, Laguerre, Legendre
and Powers perform superiorly when used in the pricing function. We analyze
each method’s performance by testing five option types (of which two barrier
option types are new research in this thesis) in-the-money, at-the-money and outof-
the-money, and by varying the polynomial degree between zero and five. We
find no evidence of superiority among the tested polynomials. Like Tompaidis
and Yang (2014), we find that OLS regression tend to underperform when the
number of simulation paths is small. Despite this issue, we find that OLS
regression performs best among the methods tested – which is also observable for
one of the tested barrier options. | nb_NO |
