Bitcoin price power law is rejected on distributional series and not robust to time-origin shifts, but dominates medium-horizon forecasts against baselines because it avoids committing to specific wave shapes.
Giovanni Santostasi and Stephen Perrenod
4 Pith papers cite this work, alongside 3,218 external citations. Polarity classification is still indexing.
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Three infimum-based Engle-Granger tests are proposed for detecting cointegration that holds only over a sub-interval of the sample, with limiting distributions derived and critical values tabulated via response surfaces.
Rule-based model selection in time series forecasting achieves low accuracy and exhibits high ranking instability across data regimes and forecasting horizons.
The authors present an R package providing implementations of four published normality tests for stationary time series, plus diagnostic functions for model residuals.
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Bitcoin's Power Law: Weak Structure, Strong Forecasts
Bitcoin price power law is rejected on distributional series and not robust to time-origin shifts, but dominates medium-horizon forecasts against baselines because it avoids committing to specific wave shapes.
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Cointegration by Parts: Locating Cointegration in Time
Three infimum-based Engle-Granger tests are proposed for detecting cointegration that holds only over a sub-interval of the sample, with limiting distributions derived and critical values tabulated via response surfaces.
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Why Model Selection Fails in Time Series Forecasting: An Empirical Study of Instability Across Data Regimes
Rule-based model selection in time series forecasting achieves low accuracy and exhibits high ranking instability across data regimes and forecasting horizons.
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An R package for Normality in Stationary Processes
The authors present an R package providing implementations of four published normality tests for stationary time series, plus diagnostic functions for model residuals.