Market-Crash Forecasting Based on The Dynamics of The Alpha-Stable Distribution

Publicado en

  • Physica A Statistical Mechanics and its Applications

Resumen

  • 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.

fecha de publicación

  • 2020

Líneas de investigación

  • Alpha-Stable
  • Crash Probability
  • Stock Market Indexes
  • Tail Index
  • VAR Model

Volumen

  • 557

Issue

  • C