Linear regression for currency European call option pricing in incomplete markets

Ahmad W. Bitar ()
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Ahmad W. Bitar: LIST3N - MSAD - LIST3N - Modélisation, stochastique, apprentissage et décision - LIST3N - Laboratoire Informatique et Société Numérique - UTT - Université de Technologie de Troyes, UTT - Université de Technologie de Troyes, Eut+ Data Science Institute, European Union

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Abstract: The least squares is the traditional regression technique for pricing European options in incomplete markets by building a self-financing hedging portfolio that does not perfectly replicate the call option. However, the least squares is quite sensitive to even a single outlier in the data, and thus the predicted option price may potentially deviate from the true unknown one. To alleviate the problem of outliers, this chapter aims to develop two different option pricing prediction strategies based mainly on the idea of robust linear regression. The robust techniques proposed are evaluated on numerical data, the results of which demonstrate their effectiveness for European call option pricing on exchange rates.

Keywords: Least squares LS; Robust linear regression; European option pricing; Incomplete market; Trinomial model; Exchange rates (search for similar items in EconPapers)
Date: 2025
Note: View the original document on HAL open archive server: https://utt.hal.science/hal-04815308v3
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Published in Emerald Publishing Limited. Advances in Pacific Basin Business, Economics and Finance, 14, In press

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04815308

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