Pointwise Regularity Exponents and Well-Behaved Residuals in Stock Markets

Bianchi, Sergio; Pantanella, Alexandre

doi:10.7763/ijtef.2011.v2.78

“…Once an extensive empirical evidence will be produced about the consistency between the model and the actual financial time series (as to this, Bianchi and Pianese 2008;Bianchi and Pantanella 2010;Bianchi and Pantanella 2011;Bianchi et al 2013) are promising contributions), the results will concur to model the functional parameter in order to face the problem of the asset pricing in the multifractional framework. from one pricing model to another in (an exogenous) probability, we define a model switching using the actual observed information of a price model at a given time.…”

Section: Further Developmentsmentioning

confidence: 91%

Future Perspectives in Risk Models and Finance

Bensoussan¹,

Guégan

²

,

Tapiero³

2015

International Series in Operations Research &Amp; Management Science

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The book series International Series in Operations Research and Management Science encompasses the various areas of operations research and management science. Both theoretical and applied books are included. It describes current advances anywhere in the world that are at the cutting edge of the field. The series is aimed especially at researchers, doctoral students, and sophisticated practitioners. The series features three types of books:• Advanced expository books that extend and unify our understanding of particular areas.• Research monographs that make substantial contributions to knowledge.• Handbooks that define the new state of the art in particular areas. They will be entitled Recent Advances in (name of the area). Each handbook will be edited by a leading authority in the area who will organize a team of experts on various aspects of the topic to write individual chapters. A handbook may emphasize expository surveys or completely new advances (either research or applications) or a combination of both.The series emphasizes the following four areas: Mathematical Programming: Including linear programming, integer programming, nonlinear programming, interior point methods, game theory, network optimization models, combinatorics, equilibrium programming, complementarity theory, multiobjective optimization, dynamic programming, stochastic programming, complexity theory, etc. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher's location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty...

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“…In order to quantify the four parameters of the stable distribution, we consider N independent variables x i that follow alpha-stable distribution (21) [274,282]. Then, we need to define five empirical quantiles of probability levels 5%, 25%, 50%, 75%, and 95%.…”

Section: Quantiles Methodsmentioning

confidence: 99%

Econophysics of cryptocurrency crashes: an overview

Bielinskyi

¹

,

Serdyuk

²

,

Семеріков

³

et al. 2021

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

show abstract

“…In order to quantify the four parameters of the stable distribution, we consider 𝑁 independent variables 𝑥 𝑖 that follow alpha-stable distribution (21) [283,275]. Then, we need to define five empirical quantiles of probability levels 5%, 25%, 50%, 75%, and 95%.…”

Section: Quantiles Methodsmentioning

confidence: 99%

Econophysics of cryptocurrency crashes: a systematic review

Bielinskyi¹,

Serdyuk²,

et al. 2021

View full text Add to dashboard Cite

Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

show abstract

Pointwise Regularity Exponents and Well-Behaved Residuals in Stock Markets

Cited by 17 publications

References 21 publications

Future Perspectives in Risk Models and Finance

Future Perspectives in Risk Models and Finance

Econophysics of cryptocurrency crashes: an overview

Econophysics of cryptocurrency crashes: a systematic review

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