Financial Market Crash Prediction Through Analysis of Stable and Pareto Distributions

Jesus Enrique Molina Muñoz; Andrés Mora Valencia; Javier Perote

doi:DOI https://doi.org/10.1007/978-3-030-78965-7

Financial Market Crash Prediction Through Analysis of Stable and Pareto Distributions

Jesus Enrique Molina Muñoz, Andrés Mora Valencia, Javier Perote

Universidad del Rosario

Producción científica: Capítulo en Libro/Informe › Capítulo (revisado por pares) › revisión exhaustiva

Resumen

This manuscript proposes a market crash forecasting methodology based on the analysis of the shape parameter of the alpha-stable distribution. Moreover, the parameters of Pareto type II distribution are also employed for comparison purposes. The model performance is assessed by Quadratic Probability Score, Logarithmic Probability Score, and Directional Accuracy measures. Our applications for developed and emerging markets show a good accuracy for alpha-stable distribution and replicability of the results. Thus, our model is an adequate early warning system for future crashes.

Idioma original	Inglés estadounidense
Título de la publicación alojada	Mathematical and Statistical Methods for Actuarial Sciences and Finance
Lugar de publicación	Suiza
Editorial	Springer
Capítulo	52
Páginas	1-10
Número de páginas	10
Edición	1
ISBN (versión digital)	978-3-030-78965-7
DOI	https://doi.org/DOI https://doi.org/10.1007/978-3-030-78965-7
Estado	Publicada - dic. 14 2021
Evento	MAF 2020 - 9th International Conference MATHEMATICAL AND STATISTICAL METHODS FOR ACTUARIAL SCIENCES AND FINANCE - https://www.unige.ch/maf2020/, Suiza, Suiza Duración: abr. 15 2020 → abr. 17 2020 https://www.unige.ch/maf2020/

Conferencia

Conferencia	MAF 2020 - 9th International Conference MATHEMATICAL AND STATISTICAL METHODS FOR ACTUARIAL SCIENCES AND FINANCE
País/Territorio	Suiza
Ciudad	Suiza
Período	4/15/20 → 4/17/20
Dirección de internet	https://www.unige.ch/maf2020/

Acceder al documento

DOI https://doi.org/10.1007/978-3-030-78965-7

Citar esto

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abstract = "This manuscript proposes a market crash forecasting methodology based on the analysis of the shape parameter of the alpha-stable distribution. Moreover, the parameters of Pareto type II distribution are also employed for comparison purposes. The model performance is assessed by Quadratic Probability Score, Logarithmic Probability Score, and Directional Accuracy measures. Our applications for developed and emerging markets show a good accuracy for alpha-stable distribution and replicability of the results. Thus, our model is an adequate early warning system for future crashes.",

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Molina Muñoz, JE, Mora Valencia, A & Perote , J 2021, Financial Market Crash Prediction Through Analysis of Stable and Pareto Distributions. En Mathematical and Statistical Methods for Actuarial Sciences and Finance. 1 ed., Springer, Suiza, pp. 1-10, MAF 2020 - 9th International Conference MATHEMATICAL AND STATISTICAL METHODS FOR ACTUARIAL SCIENCES AND FINANCE
, Suiza, Suiza, 4/15/20. https://doi.org/DOI https://doi.org/10.1007/978-3-030-78965-7

Financial Market Crash Prediction Through Analysis of Stable and Pareto Distributions. / Molina Muñoz, Jesus Enrique; Mora Valencia, Andrés; Perote , Javier.
Mathematical and Statistical Methods for Actuarial Sciences and Finance. 1. ed. Suiza: Springer, 2021. p. 1-10.

Producción científica: Capítulo en Libro/Informe › Capítulo (revisado por pares) › revisión exhaustiva

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N2 - This manuscript proposes a market crash forecasting methodology based on the analysis of the shape parameter of the alpha-stable distribution. Moreover, the parameters of Pareto type II distribution are also employed for comparison purposes. The model performance is assessed by Quadratic Probability Score, Logarithmic Probability Score, and Directional Accuracy measures. Our applications for developed and emerging markets show a good accuracy for alpha-stable distribution and replicability of the results. Thus, our model is an adequate early warning system for future crashes.

AB - This manuscript proposes a market crash forecasting methodology based on the analysis of the shape parameter of the alpha-stable distribution. Moreover, the parameters of Pareto type II distribution are also employed for comparison purposes. The model performance is assessed by Quadratic Probability Score, Logarithmic Probability Score, and Directional Accuracy measures. Our applications for developed and emerging markets show a good accuracy for alpha-stable distribution and replicability of the results. Thus, our model is an adequate early warning system for future crashes.

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