Abstract

The advantage of quantum mechanics to shift up the ability to econometrically understand extreme tail losses in financial data has become more desirable, especially in cases of Value at Risk (VaR) and Expected Shortfall (ES) predictions. Behind the non-novel quantum mechanism, it does interestingly connect with the distributional signals of humans’ brainstorms. The highlighted purpose of this article is to devise a quantum-wave distribution methodically to analyze better risks and returns for stock markets in The Association of Southeast Asian Nations (ASEAN) countries, including Thailand (SET), Singapore (STI), Malaysia (FTSE), Philippines (PSEI), and Indonesia (PCI). Data samples were observed as quarterly trends between 1994 and 2019. Bayesian statistics and simulations were applied to present estimations’ outputs. Empirically, quantum distributions are remarkable for providing “real distributions”, which computationally conform to Bayesian inferences and crucially contribute to the higher level of extreme data analyses in financial economics.

Highlights

  • Physicists’ interest in the social sciences is not novel

  • The power of quantum physics substantially existed in the chaotic period of World War II—reasonably called the “beginning” of modern physics—by Werner Heisenberg, who was a founder of quantum mechanics and a significant contributor to the physics of fluids and elementary particles (Saperstein 2010)

  • To compute the Value at Risk (VaR) model at 99% confidence and the corresponding expected shortfall, Table 2 represents the comparative outcome that indicates the modified distribution by applied quantum computing for Bayesian extreme value forecasting can capture missing information more efficiently than the traditional econometrics (Gaussian random walk process) because every Deviation Information Criteria (DIC) values indicate that the quantum distribution of five stock markets in five Association of Southeast Asian Nations (ASEAN) countries is appropriate with the model of Bayesian extreme value prediction compared with data distribution based on the Gaussian random walk process

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Summary

Introduction

Physicists’ interest in the social sciences is not novel. The word “econophysics” is the perspective applied to economic computational models and concepts associated with the “physics” of systematical complexity—e.g., statistical mechanics (quantum mechanics), self-organized criticality, microsimulation, etc. (Hooker 2011). The power of quantum physics substantially existed in the chaotic period of World War II—reasonably called the “beginning” of modern physics—by Werner Heisenberg, who was a founder of quantum mechanics and a significant contributor to the physics of fluids and elementary particles (Saperstein 2010). With this great exploration, modern quantum physics has brought people to distinctly shift the standard of living through numerous inventions such as microwaves, fiber optic telecommunications, super computers, etc. The reason to decide to select F is the origin of numerous latencies, which are attitudes, perspectives, morals, etc TThhiiss rreesseeaarrcchh ttrriieess ttoo ffiillll tthhee rreesseeaarrcchh ggaappss bbeettwweeeenn tthhee ttrraaddiittiioonnaall eeccoonnoommeettrriiccss mmeetthhoodd aanndd mmooddeerrnn eeccoonnoopphhyyssiicciissttss ((QQuuaannttuumm EEccoonnoommeettrriiccss)),,wwhhiicchhaappppllyyiinnffiinnaanncciiaall mmaarrkkeettss,, eessppeecciiaallllyy tthhee eexxttrreemmee vvaalluueepprreeddiiccttiioonniinntthheeAASSEEAANNssttoocckkeexxcchhaannggee. .HHoowweevveer,r, tthhiiss rreesseeaarrcchh iiss oorrggaanniizzeedd,, aass ffoolllloowwss,, bbyy eexxppllaaiinniinngg tthhee ccoonncceeppttuuaall ffrraammeewwoorrkkbbeettwweeeenn traditional eecoconnoommetertirciscsanadnQduQanutaunmtuEmconEocmoneotrmicest(rMicsod(eMrnodEecronnoEpchoynsoicpishty).sTichiset)s.ecTohned pseacrot nisdhpowarttoisaphpolwy tthoisacpopnlcyepthtuisalcforanmceepwtuoarlk firnamexetrwemorekvianlueextprreemdiectvioanlu, espreecdiailcltyiotnh,e exsptreecmiaellyvatlhuee eoxftVreamlueevaatlRuieskof(VVaaRlu) eanadt REixspke(cVteadRS) haonrdtfaElxlp(EecSt)eodfSfihvoertsftaolclk(EmSa) rokfeftisvien AstSoEckAmNacrokuetnstrinieAs.STEhAeNlacsot upnatrrtioesf.tThhiserleassetaprachrtiosfatnhiesxrcelsuesairvcehsiusmanmeaxrcylufsoirvceosmumpamriasroyn bfoertwcoemenpatwrisoomn betehtwodesentotwfooremcaestht othdes etoxtfroermeceavstatlhueeeoxftrVeamlueevaatluReisokf (VVaalRu)e aant dRiEskxp(VecatRed) SanhdortEfxaplle(cEteSd) iSnhofirvtfealslto(EckS)minarfkiveetssotofcAk SmEaArNketcsouofntAriSeEsAbNasecdouonntriReisskbamseadnaognemRiesnkt amnaanlyasgiesm. ent analysis

Literature and Criittiiccaall TThhiinnkkiinngg
The Origin of Quantum Distributions
Quantum Computing in Financial Econometrics
The Objective and Scope of Research
The Prior Density for Parameters at the Threshold
Posterior Density Estimations
Risk Measurement
The Distribution Outlook Comparison
Risk Measures by the Quantum Distribution
Conclusions
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