Identifying long-term precursors of financial market crashes using correlation patterns


Por: Pharasi, Hirdesh K., Sharma, Kiran, Chatterjee, Rakesh, Chakraborti, Anirban, Leyvraz, Francois, Seligman, Thomas H.

Publicada: 31 oct 2018
Categoría: Physics and astronomy (miscellaneous)

Resumen:
The study of the critical dynamics in complex systems is always interesting yet challenging. Here, we choose financial markets as an example of a complex system, and do comparative analyses of two stock markets - the S&P 500 (USA) and Nikkei 225 (JPN). Our analyses are based on the evolution of cross-correlation structure patterns of short-time epochs for a 32 year period (1985-2016). We identify 'market states' as clusters of similar correlation structures, which occur more frequently than by pure chance (randomness). The dynamical transitions between the correlation structures reflect the evolution of the market states. Power mapping method from the random matrix theory is used to suppress the noise on correlation patterns, and an adaptation of the intra-cluster distance method is used to obtain the 'optimum' number of market states. We find that the S&P 500 is characterized by four market states and Nikkei 225 by five. We further analyze the co-occurrence of paired market states; the probability of remaining in the same state is much higher than the transition to a different state. The transitions to other states mainly occur among the immediately adjacent states, with a few rare intermittent transitions to the remote states. The state adjacent to the critical state (market crash) may serve as an indicator or a 'precursor' for the critical state and this novel method of identifying the long-term precursors may be helpful for constructing the early warning system in financial markets, as well as in other complex systems. © 2018 The Author(s). Published by IOP Publishing Ltd on behalf of Deutsche Physikalische Gesellschaft.

Filiaciones:
Pharasi, Hirdesh K.:
 Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca, 62210, Mexico

 Univ Nacl Autonoma Mexico, Inst Ciencias Fis, Cuernavaca 62210, Morelos, Mexico

Sharma, Kiran:
 School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

 Jawaharlal Nehru Univ, Sch Computat & Integrat Sci, New Delhi 110067, India

Chatterjee, Rakesh:
 Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca, 62210, Mexico

 School of Mechanical Engineering, Sackler Center for Computational Molecular and Materials Science, Tel Aviv University, Tel Aviv, 6997801, Israel

 Univ Nacl Autonoma Mexico, Inst Ciencias Fis, Cuernavaca 62210, Morelos, Mexico

 Tel Aviv Univ, Sch Mech Engn, IL-6997801 Tel Aviv, Israel

 Tel Aviv Univ, Sackler Ctr Computat Mol & Mat Sci, IL-6997801 Tel Aviv, Israel

Chakraborti, Anirban:
 School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

 Jawaharlal Nehru Univ, Sch Computat & Integrat Sci, New Delhi 110067, India

Leyvraz, Francois:
 Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca, 62210, Mexico

 Centro Internacional de Ciencias, Cuernavaca, 62210, Mexico

 Univ Nacl Autonoma Mexico, Inst Ciencias Fis, Cuernavaca 62210, Morelos, Mexico

 Ctr Int Ciencias, Cuernavaca 62210, Morelos, Mexico

Seligman, Thomas H.:
 Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca, 62210, Mexico

 Centro Internacional de Ciencias, Cuernavaca, 62210, Mexico

 Univ Nacl Autonoma Mexico, Inst Ciencias Fis, Cuernavaca 62210, Morelos, Mexico

 Ctr Int Ciencias, Cuernavaca 62210, Morelos, Mexico
ISSN: 13672630
Editorial
Institute of Physics Publishing, DIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLAND, Reino Unido
Tipo de documento: Article
Volumen: 20 Número: 10
Páginas:
WOS Id: 000449178200001

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