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| Format: | Recurso digital |
| Language: | English |
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2025
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| Online Access: | https://doi.org/10.5281/zenodo.18018292 |
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| _version_ | 1866902319570878464 |
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| author | Chechelnitsky, Igor |
| author_facet | Chechelnitsky, Igor |
| contents | <p>LRD v6.0.1 is a reproducible, audit-driven empirical suite for long-range dependence / fractal memory in real-world data, centered on Bitcoin volatility as a benchmark for AI forecasting and risk systems.</p> <p> </p> <p>The pipeline estimates scaling via DFA-2, quantifies uncertainty via block bootstrap confidence intervals, and applies phase-randomized surrogate tests as a falsification step. If scaling is not distinguishable from surrogates or collapses under scale-range sensitivity checks, the "LRD" claim is rejected.</p> <p> </p> <p>What's new in v6.0.1: Two short addenda link the macroscopic volatility-memory signal to modern market microstructure universality results:</p> <p> </p> <p>Square-root law (SRL) of price impact — Sato & Kanazawa (Phys. Rev. Lett. 135, 257401, 2025) provide high-precision evidence that the SRL holds with exponent δ ≈ 1/2 across all liquid stocks and traders on the Tokyo Stock Exchange over eight years.</p> <p>Theoretical framework — As Bouchaud argues in an APS Physics Viewpoint, universal laws can emerge in financial markets from the aggregation of diverse microscopic behaviors, analogous to critical phenomena in physics.</p> <p>This provides a mechanistic anchor for interpreting cross-market fractal memory while maintaining the falsifiable empirical protocol.</p> <p> </p> <p>Domains covered: Bitcoin/crypto volatility, earthquake activity, HRV (physiology), genomics.</p> <p> </p> <p>Audit protocol: DFA-2 + block-bootstrap CI + phase-randomized surrogate tests + scale-range sensitivity.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18018292 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | LRD v6.0.1: Long-Range Dependence and Microstructural Universality Chechelnitsky, Igor long-range dependence fractal memory Hurst exponent DFA detrended fluctuation analysis Bitcoin volatility clustering earthquakes HRV genomics time series analysis econophysics, financial econometrics long memory surrogate data phase randomization bootstrap confidence interval volatility forecasting risk modeling nonstationary time series scaling laws AI benchmarks regime detection market microstructure square-root law econophysics <p>LRD v6.0.1 is a reproducible, audit-driven empirical suite for long-range dependence / fractal memory in real-world data, centered on Bitcoin volatility as a benchmark for AI forecasting and risk systems.</p> <p> </p> <p>The pipeline estimates scaling via DFA-2, quantifies uncertainty via block bootstrap confidence intervals, and applies phase-randomized surrogate tests as a falsification step. If scaling is not distinguishable from surrogates or collapses under scale-range sensitivity checks, the "LRD" claim is rejected.</p> <p> </p> <p>What's new in v6.0.1: Two short addenda link the macroscopic volatility-memory signal to modern market microstructure universality results:</p> <p> </p> <p>Square-root law (SRL) of price impact — Sato & Kanazawa (Phys. Rev. Lett. 135, 257401, 2025) provide high-precision evidence that the SRL holds with exponent δ ≈ 1/2 across all liquid stocks and traders on the Tokyo Stock Exchange over eight years.</p> <p>Theoretical framework — As Bouchaud argues in an APS Physics Viewpoint, universal laws can emerge in financial markets from the aggregation of diverse microscopic behaviors, analogous to critical phenomena in physics.</p> <p>This provides a mechanistic anchor for interpreting cross-market fractal memory while maintaining the falsifiable empirical protocol.</p> <p> </p> <p>Domains covered: Bitcoin/crypto volatility, earthquake activity, HRV (physiology), genomics.</p> <p> </p> <p>Audit protocol: DFA-2 + block-bootstrap CI + phase-randomized surrogate tests + scale-range sensitivity.</p> |
| title | LRD v6.0.1: Long-Range Dependence and Microstructural Universality |
| topic | long-range dependence fractal memory Hurst exponent DFA detrended fluctuation analysis Bitcoin volatility clustering earthquakes HRV genomics time series analysis econophysics, financial econometrics long memory surrogate data phase randomization bootstrap confidence interval volatility forecasting risk modeling nonstationary time series scaling laws AI benchmarks regime detection market microstructure square-root law econophysics |
| url | https://doi.org/10.5281/zenodo.18018292 |