2021/06/09 by Jarret Petrillo, Petrillo, Jarret
Computer Science · Economics, Econometrics and Finance · Mathematics · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Methodology (stat.ME) #Time Series Analysis and Forecasting #stat.ME
paper · pdf · doi:10.48550/arxiv.2106.05116
arxiv created 2021/06/09 · openalex publication_date 2021/06/09 · arxiv updated 2021/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose and implement a nonlinear Verification and Validation (V&V) methodology to test two fitting procedures for the log-periodic power law model (LPPL), a model that has diverse applications across data analysis, but known estimation issues. Prior studies have focused on ex-post analyses of rare events: Earthquakes, glacial break-off events, and financial crashes. Or, on non-dynamical simulations such as additive noise or resampling. Our results reject an estimation scheme that pre-conditions observed data by fitting and removing an exponential trend. We validate a subordinated algorithm, and confirm that it passes Feigenbaum's criticism, which articulates a broad hurdle for ex-post statistical learning from rare events.