Spotlighting crypto market abuse with data.
Quantitative White Papers.
These research white papers are detailed analyses that inform various elements of Kaiko’s data offerings. They’re independently published and backed by scientifically rigorous testing and peer-reviewed methodologies.
Data Feeds Solutions
Thorough mathematical modelling and analysis of Uniswap v3
A thorough analysis of Uniswap v3, forms part of the methodology for our Uniswap v3 data in our Level 1 & Level 2 Data subscriptions.
Analytics Solutions
Statistical error bounds for weighted mean and median, with application to robust aggregation of cryptocurrency data
An analysis produced as the foundation for our Fair Market Value solution.
Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes
Used as part of the underlying methodology for the IV calculation offered as part of our Derivatives Risk Indicators solution.
Kaiko-facilitated papers
The following Kaiko-facilitated papers ensure we stay at the forefront of innovation by engaging with leading academic and quantitative research circles. While not part of our products yet, this work keeps us connected to cutting-edge methodologies and emerging trends in cryptocurrency analytics, ensuring we’re better informed when making decisions on product strategy and development.
On the simulation of extreme events with neural networks
M.Allouche, S.Girard and E.Gobet. Under review, 2025.
ExcessGAN: simulation above extreme thresholds using Generative Adversarial Networks
M.Allouche, S.Girard and E.Gobet. Under review, 2025.
Learning extreme Expected Shortfall and Conditional Tail Moments with neural networks. Application to cryptocurrency data
M.Allouche, S.Girard and E.Gobet. Neural Networks, 2024.
Reduced-bias estimation of the extreme conditional tail expectation for Box-Cox transforms of heavy-tailed distributions
M.Allouche, J. El Methni and S.Girard. Journal of Statistical Planning and Inference, 2024.
Statistical error bounds for weighted mean and median, with application to robust aggregation of cryptocurrency data
M.Allouche, M. Echenim, E.Gobet and A.C. Maurice. Mathematical Finance, 2023.
Thorough mathematical modelling and analysis of Uniswap v3
M. Echenim, E. Gobet and A.C. Maurice. Under review, 2023.
Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes
M. Echenim, E. Gobet and A.C. Maurice. Quantitative Finance, 2023.