=Paper=
{{Paper
|id=Vol-1774/MIDAS2016_paper4
|storemode=property
|title=A general framework for building machine learning models for pricing american index options with no-arbitrage and its limitation
|pdfUrl=https://ceur-ws.org/Vol-1774/MIDAS2016_paper4.pdf
|volume=Vol-1774
|authors=Huisu Jang,Jaewook Lee
|dblpUrl=https://dblp.org/rec/conf/pkdd/JangL16
}}
==A general framework for building machine learning models for pricing american index options with no-arbitrage and its limitation==
https://ceur-ws.org/Vol-1774/MIDAS2016_paper4.pdf
A general framework for building machine
learning models for pricing american index
options with no-arbitrage and its limitation
Huisu Jang and Jaewook Lee
Department of Industrial Engineering, Seoul National University
1 Gwanak-ro, Gwanak-gu, Seoul 151-744, South Korea
{gmltn7798,jaewook}@snu.ac.kr
Abstract. Since the seminar work by Black and Sholes on option pric-
ing early 1970’s, many alternative option pricing models have appeared
to address some key stylized facts for option markets such as volatil-
ity smile, fat-tail, volatility clustering, and so on. Most of the success-
ful option models are parametric models based on diffusion processes
with jumps usually called the Lévy processes and the parameters of the
models can be calibrated to fit the model to the market option data.
Recently nonparametric models have attracted lots of attention to many
researchers for their improved prediction accuracy on pricing financial
derivatives mostly for the European options which can be exercised only
at its maturities. In the financial market, however, the most frequently
traded options are usually of American type, which can be exercised
anytime before their maturities and machine learning models suffer from
arbitrage opportunities when they are directly applied to pricing real
American options. In the present study, we propose a general framework
for building a machine learning model that not only satisfies no-arbitrage
constraints for pricing American options, but also is stable in its predic-
tion to a specified range of time-varying daily options. We conduct a
comprehensive study to verify the predictive performance of the pro-
posed models by applying them to one-year S&P 100 daily American
put options and show that the proposed method is significantly better
than the state-of arts machine learning models. Also we compare the
prediction performance of the machine learning models with parametric
jump models when the domain of the in-sample option data is different
from the domain of the out-of-sample option data and discuss about their
limitations.
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