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Conditional Moments of Noncausal Alpha-Stable Processes and the Prediction of Bubble Crash Odds

Bibliographic Data

ID19419877
AuthorsSébastien Fries (0000-0003-1204-5532, Vrije Universiteit Amsterdam, corresponding author)
Year2022
Volume40
Issue4
Pages1596-1616
Publication date2022-10-02
Open AccessYes
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2021.1953508
OpenAlexW2992057657
LanguageEN
Citations received1
References cited35

Noncausal, or anticipative, heavy-tailed processes generate trajectories featuring locally explosive episodes akin to speculative bubbles in financial time series data. For (Xt) a two-sided infinite α-stable moving average (MA), conditional moments up to integer order four are shown to exist provided (Xt) is anticipative enough, despite the process featuring infinite marginal variance. Formulas of these moments at any forecast horizon under any admissible parameterization are provided. Under the assumption of errors with regularly varying tails, closed-form formulas of the predictive distribution during explosive bubble episodes are obtained and expressions of the ex ante crash odds at any horizon are available. It is found that the noncausal autoregression of order 1 (AR(1)) with AR coefficient ρ and tail exponent α generates bubbles whose survival distributions are geometric with parameter ρ^α. This property extends to bubbles with arbitrarily shaped collapse after the peak, provided the inflation phase is noncausal AR(1)-like. It appears that mixed causal–noncausal processes generate explosive episodes with dynamics à la Blanchard and Watson which could reconcile rational bubbles with tail exponents greater than 1

Bubble · Conditional probability distribution · Economics · Explosive material · Exponent · Mechanics · Physics · Statistical physics · Statistics · Complex Systems and Time Series Analysis · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Mathematics · Applied Mathematics · Finance

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Citations per year1
Citation span2025 - 2025 (1)
Highly citedNo

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