Lagrange regularisation approach to compare nested data sets and determine objectively financial bubbles' inceptions
Guilherme Demos, Didier Sornette

TL;DR
This paper introduces a Lagrange regularisation method to objectively identify the start times of financial bubbles by optimizing the fitting window in time series analysis, demonstrated on synthetic and real market data.
Contribution
The paper presents a novel Lagrange regularisation approach for endogenously detecting the inception of financial bubbles without external input.
Findings
Effectively identifies bubble start times in historical market data.
Provides a systematic, objective method for regime change detection.
Outperforms traditional residual-based methods in bubble inception detection.
Abstract
Inspired by the question of identifying the start time of financial bubbles, we address the calibration of time series in which the inception of the latest regime of interest is unknown. By taking into account the tendency of a given model to overfit data, we introduce the Lagrange regularisation of the normalised sum of the squared residuals, , to endogenously detect the optimal fitting window size := that should be used for calibration purposes for a fixed pseudo present time . The performance of the Lagrange regularisation of defined as is exemplified on a simple Linear Regression problem with a change point and compared against the Residual Sum of Squares (RSS) := and RSS/(N-p):= , where is the sample size and p is the number…
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