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

TL;DR

This paper introduces a new calibration method for cryptocurrency options that effectively manages large bid-ask spreads and missing data, resulting in more reliable implied volatility estimates.

Contribution

The paper presents a novel calibration approach tailored for cryptocurrency options, improving robustness and accuracy over traditional methods.

Findings

Calibration method outperforms standard approaches in robustness.

Improved implied volatility estimates in cryptocurrency markets.

Handles large bid-ask spreads and missing quotes effectively.

Abstract

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.