2026/01/30 by Samuel W. Akingbade · 1 voice
Economics, Econometrics and Finance · Mathematics · #Complex Systems and Time Series Analysis #Economic theories and models #Mathematical Dynamics and Fractals #math.DS #math.ST #msc:37M10 #msc:55N31 #msc:62R40 #q-fin.ST #stat.TH
paper · pdf · doi:10.1016/j.cnsns.2026.110630
26 pages, 14 figures
arxiv published 2026/01/30 · openalex publication_date 2026/07/29 · openalex created_date 2026/07/30 · arxiv created 2026/07/31 · arxiv updated 2026/07/31 · openalex updated_date 2026/08/03
Financial markets exhibit temporal organization that is not fully captured by volatility measures or linear correlation structure. We study a null-validated topological approach for quantifying financial market complexity using Bitcoin daily log returns and the S&P 500 index as examples of cryptocurrency and broad U.S. equity market dynamics. The analysis uses the L1 norm of the persistence landscapes computed from sliding-window delay embeddings. This quantity co-moves strongly with stochastic volatility during periods of market stress, but the strength and form of this relationship vary over time and differ between the two markets. Surrogate-based null models provide statistical validation of these observations. Rejection of shuffle surrogates rules out explanations based on marginal distributions alone, while departures from phase randomized surrogates indicate sensitivity to nonlinear and phase-dependent temporal organization beyond linear correlations. These results demonstrate that persistence landscape norms provide complementary information about market dynamics across market conditions.