hurst-exponent-dynamics-crisis-prediction

Wavelet denoising removes high-frequency noise from financial time series before Hurst exponent estimation, ensuring nonlinearity-robust analysis. This cascadic filtering isolates true fractal trends and prevents false crisis signals in rolling-window calculations.

Yes, bootstrap significance testing with 50,000 surrogates validates whether detected fractal trends violate the Efficient Market Hypothesis. This statistical verification confirms that identified momentum bursts and crisis transitions are not random artifacts.

Continuous wavelet transform heatmaps visualize crisis detection across time-frequency domains in daily price series. These heatmaps reveal regime shifts and momentum bursts across rolling windows of 100, 1000, and 2500 days for adaptive trading strategies.

Hurst exponent analysis requires sufficient daily price history for rolling windows up to 2500 days and assumes fractal trends persist. It may lag during sudden non-fractal market shocks and needs wavelet denoising to avoid noise-driven false regime shift signals.