Market-crash forecasting based on the dynamics of the alpha-stable distribution

Jesús Molina-Muñoz, Andrés Mora-Valencia and Javier Perote

Physica A: Statistical Mechanics and its Applications, 2020, vol. 557, issue C

Abstract: This paper investigates on the alpha-stable distribution capacity to capture the probability of market crashes by means of the dynamic forecasting of its alpha and beta parameters. On the basis of the GARCH-stable model, we design a market crash forecasting methodology that involves three-stepwise procedure: (i) Recursively estimation the GARCH-stable parameters through a rolling window; (ii) alpha-stable parameters forecasting according to a VAR model; and (iii) Crash probabilities forecasting and analysis. The model performance for alternative crash definitions is assessed in terms of different accuracy criteria, and compared with a random walk model as benchmark. Our applications to a wide variety of stock indexes for developed and emerging markets reveals a high degree of accuracy and replicability of the results. Hence the model represents an interesting tool for risk management and the design of early warning systems for future crashes.

Keywords: Crash probability; Alpha-stable; Stock market indexes; VAR model; Tail index (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:557:y:2020:i:c:s0378437120304532

DOI: 10.1016/j.physa.2020.124876

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