The study of Thai stock market across the 2008 financial crisis

Kanjamapornkul, Kabin; Pinčák, Richard; Bartoš, Erik

doi:10.1016/j.physa.2016.06.078

Cited by 22 publications

(22 citation statements)

References 43 publications

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“…The stock and commodity markets were in particular peril as they exhibit a long-range dependence (Lahmiri [7]). However, the impact of the GFC was not limited only to stock markets of large economies as the Eurozone (Anagnostidis et al [8]), the U.S. (Chen et al [3]), or China (Yang et al [9]; Chen et al [3]) but it also spread to relatively smaller, yet important, economies in Asia (Yim et al [10]; Kantar et al [11]; Nobi et al [12]; Hui and Chen [13]; Kuzubaş et al [14]; Kanjamapornkul et al [15]), Africa (Majapa and Gossel [16]) and Latin America (Güloğlu et al [17]). …”

Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

Networks of volatility spillovers among stock markets

Baumöhl

Kočenda

Lyócsa

et al. 2018

Physica A: Statistical Mechanics and its Applications

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We show significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity marketvolatility spillovers decrease when markets are characterized by greater temporal proximity. Terms of use: Documents inVolatility spillovers also present a high degree of interconnectedness. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.JEL-Codes: C310, C580, F010, G010, G150.Keywords: volatility spillovers, stock markets, shock transmission, Granger causality network, spatial regression, financial crisis. We benefited from helpful comments we received from two anonymous referees and participants at several presentations. Eduard Baumöhl University of Economics in Bratislava

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Section: Introduction: Motivation Related Literature and Contributionmentioning

confidence: 99%

Networks of volatility spillovers among stock markets

Baumöhl

Kočenda

Lyócsa

et al. 2018

Physica A: Statistical Mechanics and its Applications

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“…We use Keras R language package based on TensorFlow computational tool for deep learning developed by Google team. The geometry of learning algorithm in TensorFlow system, specifically CNN, is based on tensor network [42] of control flow graph implemented by using tensor network of big data technology.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Laurent Polynomial in the Chern–Simons Current of V3 Loop Time Series

2019

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The genetic variation in the V3 loop obstruction component of gp120 HIV glycoprotein is defined as a solitary standing wave function with the transition state between the interaction of wave function in the DNA and RNA hidden states. It is a parasitism co-state generated from the coupling between two pairs of hidden 8 states of Laurent polynomial in the Chern-Simons current of HIV genotypes. A modified Nahm equation for a biology with Lax pairs is defined as the pairs of viral glycoprotein states with the transition between DNA and RNA in the reverse transcription process. The direction in the spinor field with genetic variation in 30 samples of the V3 loop time series with hidden 8 states of Laurent polynomial over the Chern-Simons current is predicted using a support spinor machine and convolutional neural network based on the imaging technique generated from tensor correlation network.We assume that the transcription process from the DNA to RNA and to a protein is a chain with the exact sequence in cohomology theory for biology. We cannot separate the DNA from RNA and the protein. It also happens in the transition state, which means that every state of an amino acid is a mixed state. One amino acid is linked with the other amino acids with an affine Poincare group with modulo SO(3, 1). [40] The derivation of a distribution in gene expression shows us the coupling transition protein states as an interaction of

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“…The results are confirmed by the application of Chern-Simons current for the detection of similarities in gene expression. We observed the antigene drift in time series of Chern-Simons current of V3 loop by using tensor correlation analysis [53] and also antigene shift in CD4 of 3 samples from rabbits. The evidence of antigene shift and antigene drift can be modeled by using the BV-cohomogy theory.…”

Section: Discussionmentioning

confidence: 99%

“…We use the (ITD−IMF)chain 1 (1) transformation. The algorithm can be find in [27,53] to detect the spinor field in time series data of genetic code.The main tool is the so called (ITD − IMF)chain 1 (n) in time series prediction with time series of superspace of genetic code for real application with viral gene expression. We compare the result with traditional bioinformation string matching the representation with our new methodology of time series expression as the main result of our work.…”

Section: Methodsmentioning

confidence: 99%

The Chern‐Simons Current in Systems of DNA‐RNA Transcriptions

et al. 2018

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A Chern-Simons current, coming from ghost and anti-ghost fields of supersymmetry theory, can be used to define a spectrum of gene expression in new time series data where a spinor field, as alternative representation of a gene, is adopted instead of using the standard alphabet sequence of bases A, T, C, G, U . After a general discussion on the use of supersymmetry in biological systems, we give examples of the use of supersymmetry for living organism, discuss the codon and anti-codon ghost fields and develop an algebraic construction for the trash DNA, the DNA area which does not seem active in biological systems. As a general result, all hidden states of codon can be computed by Chern-Simons 3 forms. Finally, we plot a time series of genetic variations of viral glycoprotein gene and host T-cell receptor gene by using a gene tensor correlation network related to the Chern-Simons current. An empirical analysis of genetic shift, in host cell receptor genes with separated cluster of gene and genetic drift in viral gene, is obtained by using a tensor correlation plot over time series data derived as the empirical mode decomposition of Chern-Simons current.

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The study of Thai stock market across the 2008 financial crisis

Cited by 22 publications

References 43 publications

Networks of volatility spillovers among stock markets

Networks of volatility spillovers among stock markets

Forecasting Laurent Polynomial in the Chern–Simons Current of V3 Loop Time Series

The Chern‐Simons Current in Systems of DNA‐RNA Transcriptions

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