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Conformal prediction of option prices
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Autores
Orientador(es)
Resumo(s)
The uncertainty associated with option price predictions has largely been overlooked
in the literature. This paper aims to fill this gap by quantifying such uncertainty using
conformal prediction. Conformal prediction is a model-agnostic procedure that
constructs prediction intervals, ensuring valid coverage in finite samples without
relying on distributional assumptions. Through the simulation of synthetic option
prices, we find that conformal prediction generates prediction intervals for gradient
boosting machines with an empirical coverage close to the nominal level. Conversely,
non-conformal prediction intervals exhibit empirical coverage levels that
fall short of the nominal target. In other words, they fail to contain the actual option
price more frequently than expected for a given coverage level. As anticipated,
we also observe a decrease in the width of prediction intervals as the size of the
training data increases. However, we uncover significant variations in the width of
these intervals across different options. Specifically, out-of-the-money options and
those with a short time-to-maturity exhibit relatively wider prediction intervals.
Then, we perform an empirical study using American call and put options on individual
stocks. We find that the empirical results replicate those obtained in the
simulation experiment.
Descrição
Palavras-chave
Conformal prediction Machine learning Option price Quantile regression American options
Contexto Educativo
Citação
Bastos, João A. (2023). "Conformal prediction of option prices". REM Working paper series, nº 0304/2023
Editora
ISEG - REM - Research in Economics and Mathematics