Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes

Mnacho Echenim (1, 2) , Emmanuel Gobet (3) , Anne-Claire Maurice

1 LIG - Laboratoire d'Informatique de Grenoble
2 CAPP - Calculs algorithmes programmes et preuves
3 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique

We design a novel calibration procedure that is designed to handle the specific characteristics of options on cryptocurrency markets, namely large bid-ask spreads and the possibility of missing or incoherent prices in the considered data sets. We show that this calibration procedure is significantly more robust and accurate than the standard one based on trade and mid-prices.

Mots clés: implied volatility; calibration; bid-ask spread; missing data; data augmentation

Domaines: Finance quantitative [q-fin.CP]; Applications [stat.AP]

Mnacho Echenim, Emmanuel Gobet, Anne-Claire Maurice. Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes. Quantitative Finance, 2023, 23 (9), pp.1285-1304. ⟨10.1080/14697688.2023.2229022⟩. ⟨hal-03715921⟩