4 papers in the archive (2023–2025), average empirical rigor 7.4/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×3, Lab Rats ×1.
Works mostly in Crypto Markets & DeFi, Market Microstructure, High-Frequency Trading & Optimal Execution, Commodities & Energy Markets.
Frequent co-authors: Stanisław Drożdż (4), Marcin Wątorek (4), Robert Kluszczyński (1), Marija Bezbradica (1), Martin Crane (1), Paweł Jarosz (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the $q$-dependent detrended cross-correlation coefficient ρ(q,s). By extending traditional metrics, this approach captures correlations at var
Correlations in complex systems are often obscured by nonstationarity, long-range memory, and heavy-tailed fluctuations, which limit the usefulness of traditional covariance-based analyses. To address these challenges, we construct scale and fluctuation-dependent correlation matrices using the multi
In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the oth
Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency markets, multifractal properties arise due to the interp