Econophysics and the Complexity of Financial Markets
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Mathematical Finance
Mathematical Finance 6.1I nterest and Effective Rates In this section, you will learn about various ways to solve simple and compound interest problems related to bank accounts and calculate the effective rate of interest. Upon completion you will be able to: • Apply the simple interest formula to various financial scenarios. • Apply the continuously compounded interest formula to various financial scenarios. • State the difference between simple interest and compound interest. • Use technology to solve compound interest problems, not involving continuously compound interest. • Compute the effective rate of interest, using technology when possible. • Compare multiple accounts using the effective rates of interest/effective annual yields. Working with Simple Interest It costs money to borrow money. The rent one pays for the use of money is called interest. The amount of money that is being borrowed or loaned is called the principal or present value. Interest, in its simplest form, is called simple interest and is paid only on the original amount borrowed. When the money is loaned out, the person who borrows the money generally pays a fixed rate of interest on the principal for the time period the money is kept. Although the interest rate is often specified for a year, annual percentage rate, it may be specified for a week, a month, or a quarter, etc. When a person pays back the money owed, they pay back the original amount borrowed plus the interest earned on the loan, which is called the accumulated amount or future value. Definition Simple interest is the interest that is paid only on the principal, and is given by I = Prt where, I = Interest earned or paid P = Present value or Principal r = Annual percentage rate (APR) changed to a decimal* t = Number of years* *The units of time for r and t must be the same. -
Math 581/Econ 673: Mathematical Finance
Math 581/Econ 673: Mathematical Finance This course is ideal for students who want a rigorous introduction to finance. The course covers the following fundamental topics in finance: the time value of money, portfolio theory, capital market theory, security price modeling, and financial derivatives. We shall dissect financial models by isolating their central assumptions and conceptual building blocks, showing rigorously how their gov- erning equations and relations are derived, and weighing critically their strengths and weaknesses. Prerequisites: The mathematical prerequisites are Math 212 (or 222), Math 221, and Math 230 (or 340) or consent of instructor. The course assumes no prior back- ground in finance. Assignments: assignments are team based. Grading: homework is 70% and the individual in-class project is 30%. The date, time, and location of the individual project will be given during the first week of classes. The project is mandatory; missing it is analogous to missing a final exam. Text: A. O. Petters and X. Dong, An Introduction to Mathematical Finance with Appli- cations (Springer, New York, 2016) The text will be allowed as a reference during the individual project. The following books are not required and may serve as supplements: - M. Capi´nski and T. Zastawniak, Mathematics for Finance (Springer, London, 2003) - J. Hull, Options, Futures, and Other Derivatives (Pearson Prentice Hall, Upper Saddle River, 2015) - R. McDonald, Derivative Markets, Second Edition (Addison-Wesley, Boston, 2006) - S. Roman, Introduction to the Mathematics of Finance (Springer, New York, 2004) - S. Ross, An Elementary Introduction to Mathematical Finance, Third Edition (Cambrige U. Press, Cambridge, 2011) - P. Wilmott, S. -
Financial Market Modeling with Quantum Neural Networks Arxiv
Financial Market Modeling with Quantum Neural Networks Carlos Pedro Gonçalves August 27, 2015 Instituto Superior de Ciências Sociais e Políticas (ISCSP) - University of Lisbon, E-mail: [email protected] Abstract Econophysics has developed as a research field that applies the for- malism of Statistical Mechanics and Quantum Mechanics to address Economics and Finance problems. The branch of Econophysics that applies of Quantum Theory to Economics and Finance is called Quan- tum Econophysics. In Finance, Quantum Econophysics’ contributions have ranged from option pricing to market dynamics modeling, behav- ioral finance and applications of Game Theory, integrating the empir- ical finding, from human decision analysis, that shows that nonlinear update rules in probabilities, leading to non-additive decision weights, can be computationally approached from quantum computation, with resulting quantum interference terms explaining the non-additive prob- abilities. The current work draws on these results to introduce new tools from Quantum Artificial Intelligence, namely Quantum Artifi- arXiv:1508.06586v1 [q-fin.CP] 26 Aug 2015 cial Neural Networks as a way to build and simulate financial market models with adaptive selection of trading rules, leading to turbulence and excess kurtosis in the returns distributions for a wide range of parameters. Keywords: Finance, Econophysics, Quantum Artificial Neural Networks, Quantum Stochastic Processes, Cognitive Science 1 1 Introduction One of the major problems of financial modeling has been to address com- plex financial returns dynamics, in particular, excess kurtosis and volatility- related turbulence which lead to statistically significant deviations from the Gaussian random walk model worked in traditional Financial Theory (Arthur et al., 1997; Voit, 2001; Ilinsky, 2001; Focardi and Fabozzi, 2004). -
Mathematics and Financial Economics Editor-In-Chief: Elyès Jouini, CEREMADE, Université Paris-Dauphine, Paris, France; [email protected]
ABCD springer.com 2nd Announcement and Call for Papers Mathematics and Financial Economics Editor-in-Chief: Elyès Jouini, CEREMADE, Université Paris-Dauphine, Paris, France; [email protected] New from Springer 1st issue in July 2007 NEW JOURNAL Submit your manuscript online springer.com Mathematics and Financial Economics In the last twenty years mathematical finance approach. When quantitative methods useful to has developed independently from economic economists are developed by mathematicians theory, and largely as a branch of probability and published in mathematical journals, they theory and stochastic analysis. This has led to often remain unknown and confined to a very important developments e.g. in asset pricing specific readership. More generally, there is a theory, and interest-rate modeling. need for bridges between these disciplines. This direction of research however can be The aim of this new journal is to reconcile these viewed as somewhat removed from real- two approaches and to provide the bridging world considerations and increasingly many links between mathematics, economics and academics in the field agree over the necessity finance. Typical areas of interest include of returning to foundational economic issues. foundational issues in asset pricing, financial Mainstream finance on the other hand has markets equilibrium, insurance models, port- often considered interesting economic folio management, quantitative risk manage- problems, but finance journals typically pay ment, intertemporal economics, uncertainty less -
The Law and Economics of Hedge Funds: Financial Innovation and Investor Protection Houman B
digitalcommons.nyls.edu Faculty Scholarship Articles & Chapters 2009 The Law and Economics of Hedge Funds: Financial Innovation and Investor Protection Houman B. Shadab New York Law School Follow this and additional works at: http://digitalcommons.nyls.edu/fac_articles_chapters Part of the Banking and Finance Law Commons, and the Insurance Law Commons Recommended Citation 6 Berkeley Bus. L.J. 240 (2009) This Article is brought to you for free and open access by the Faculty Scholarship at DigitalCommons@NYLS. It has been accepted for inclusion in Articles & Chapters by an authorized administrator of DigitalCommons@NYLS. The Law and Economics of Hedge Funds: Financial Innovation and Investor Protection Houman B. Shadab t Abstract: A persistent theme underlying contemporary debates about financial regulation is how to protect investors from the growing complexity of financial markets, new risks, and other changes brought about by financial innovation. Increasingly relevant to this debate are the leading innovators of complex investment strategies known as hedge funds. A hedge fund is a private investment company that is not subject to the full range of restrictions on investment activities and disclosure obligations imposed by federal securities laws, that compensates management in part with a fee based on annual profits, and typically engages in the active trading offinancial instruments. Hedge funds engage in financial innovation by pursuing novel investment strategies that lower market risk (beta) and may increase returns attributable to manager skill (alpha). Despite the funds' unique costs and risk properties, their historical performance suggests that the ultimate result of hedge fund innovation is to help investors reduce economic losses during market downturns. -
Arxiv:1509.06955V1 [Physics.Optics]
Statistical mechanics models for multimode lasers and random lasers F. Antenucci 1,2, A. Crisanti2,3, M. Ib´a˜nez Berganza2,4, A. Marruzzo1,2, L. Leuzzi1,2∗ 1 NANOTEC-CNR, Institute of Nanotechnology, Soft and Living Matter Lab, Rome, Piazzale A. Moro 2, I-00185, Roma, Italy 2 Dipartimento di Fisica, Universit`adi Roma “Sapienza,” Piazzale A. Moro 2, I-00185, Roma, Italy 3 ISC-CNR, UOS Sapienza, Piazzale A. Moro 2, I-00185, Roma, Italy 4 INFN, Gruppo Collegato di Parma, via G.P. Usberti, 7/A - 43124, Parma, Italy We review recent statistical mechanical approaches to multimode laser theory. The theory has proved very effective to describe standard lasers. We refer of the mean field theory for passive mode locking and developments based on Monte Carlo simulations and cavity method to study the role of the frequency matching condition. The status for a complete theory of multimode lasing in open and disordered cavities is discussed and the derivation of the general statistical models in this framework is presented. When light is propagating in a disordered medium, the system can be analyzed via the replica method. For high degrees of disorder and nonlinearity, a glassy behavior is expected at the lasing threshold, providing a suggestive link between glasses and photonics. We describe in details the results for the general Hamiltonian model in mean field approximation and mention an available test for replica symmetry breaking from intensity spectra measurements. Finally, we summary some perspectives still opened for such approaches. The idea that the lasing threshold can be understood as a proper thermodynamic transition goes back since the early development of laser theory and non-linear optics in the 1970s, in particular in connection with modulation instability (see, e.g.,1 and the review2). -
Lecture 13: Financial Disasters and Econophysics
Lecture 13: Financial Disasters and Econophysics Big problem: Power laws in economy and finance vs Great Moderation: (Source: http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/power-curves-what- natural-and-economic-disasters-have-in-common Analysis of big data, discontinuous change especially of financial sector, where efficient market theory missed the boat has drawn attention of specialists from physics and mathematics. Wall Street“quant”models may have helped the market implode; and collapse spawned econophysics work on finance instability. NATURE PHYSICS March 2013 Volume 9, No 3 pp119-197 : “The 2008 financial crisis has highlighted major limitations in the modelling of financial and economic systems. However, an emerging field of research at the frontiers of both physics and economics aims to provide a more fundamental understanding of economic networks, as well as practical insights for policymakers. In this Nature Physics Focus, physicists and economists consider the state-of-the-art in the application of network science to finance.” The financial crisis has made us aware that financial markets are very complex networks that, in many cases, we do not really understand and that can easily go out of control. This idea, which would have been shocking only 5 years ago, results from a number of precise reasons. What does physics bring to social science problems? 1- Heterogeneous agents – strange since physics draws strength from electron is electron is electron but STATISTICAL MECHANICS --- minority game, finance artificial agents, 2- Facility with huge data sets – data-mining for regularities in time series with open eyes. 3- Network analysis 4- Percolation/other models of “phase transition”, which directs attention at boundary conditions AN INTRODUCTION TO ECONOPHYSICS Correlations and Complexity in Finance ROSARIO N. -
Conclusion: the Impact of Statistical Physics
Conclusion: The Impact of Statistical Physics Applications of Statistical Physics The different examples which we have treated in the present book have shown the role played by statistical mechanics in other branches of physics: as a theory of matter in its various forms it serves as a bridge between macroscopic physics, which is more descriptive and experimental, and microscopic physics, which aims at finding the principles on which our understanding of Nature is based. This bridge can be crossed fruitfully in both directions, to explain or predict macroscopic properties from our knowledge of microphysics, and inversely to consolidate the microscopic principles by exploring their more or less distant macroscopic consequences which may be checked experimentally. The use of statistical methods for deductive purposes becomes unavoidable as soon as the systems or the effects studied are no longer elementary. Statistical physics is a tool of common use in the other natural sciences. Astrophysics resorts to it for describing the various forms of matter existing in the Universe where densities are either too high or too low, and tempera tures too high, to enable us to carry out the appropriate laboratory studies. Chemical kinetics as well as chemical equilibria depend in an essential way on it. We have also seen that this makes it possible to calculate the mechan ical properties of fluids or of solids; these properties are postulated in the mechanics of continuous media. Even biophysics has recourse to it when it treats general properties or poorly organized systems. If we turn to the domain of practical applications it is clear that the technology of materials, a science aiming to develop substances which have definite mechanical (metallurgy, plastics, lubricants), chemical (corrosion), thermal (conduction or insulation), electrical (resistance, superconductivity), or optical (display by liquid crystals) properties, has a great interest in statis tical physics. -
Research Statement Statistical Physics Methods and Large Scale
Research Statement Maxim Shkarayev Statistical physics methods and large scale computations In recent years, the methods of statistical physics have found applications in many seemingly un- related areas, such as in information technology and bio-physics. The core of my recent research is largely based on developing and utilizing these methods in the novel areas of applied mathemat- ics: evaluation of error statistics in communication systems and dynamics of neuronal networks. During the coarse of my work I have shown that the distribution of the error rates in the fiber op- tical communication systems has broad tails; I have also shown that the architectural connectivity of the neuronal networks implies the functional connectivity of the afore mentioned networks. In my work I have successfully developed and applied methods based on optimal fluctuation theory, mean-field theory, asymptotic analysis. These methods can be used in investigations of related areas. Optical Communication Systems Error Rate Statistics in Fiber Optical Communication Links. During my studies in the Ap- plied Mathematics Program at the University of Arizona, my research was focused on the analyti- cal, numerical and experimental study of the statistics of rare events. In many cases the event that has the greatest impact is of extremely small likelihood. For example, large magnitude earthquakes are not at all frequent, yet their impact is so dramatic that understanding the likelihood of their oc- currence is of great practical value. Studying the statistical properties of rare events is nontrivial because these events are infrequent and there is rarely sufficient time to observe the event enough times to assess any information about its statistics. -
From Big Data to Econophysics and Its Use to Explain Complex Phenomena
Journal of Risk and Financial Management Review From Big Data to Econophysics and Its Use to Explain Complex Phenomena Paulo Ferreira 1,2,3,* , Éder J.A.L. Pereira 4,5 and Hernane B.B. Pereira 4,6 1 VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal 2 Department of Economic Sciences and Organizations, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal 3 Centro de Estudos e Formação Avançada em Gestão e Economia, Instituto de Investigação e Formação Avançada, Universidade de Évora, Largo dos Colegiais 2, 7000 Évora, Portugal 4 Programa de Modelagem Computacional, SENAI Cimatec, Av. Orlando Gomes 1845, 41 650-010 Salvador, BA, Brazil; [email protected] (É.J.A.L.P.); [email protected] (H.B.B.P.) 5 Instituto Federal do Maranhão, 65075-441 São Luís-MA, Brazil 6 Universidade do Estado da Bahia, 41 150-000 Salvador, BA, Brazil * Correspondence: [email protected] Received: 5 June 2020; Accepted: 10 July 2020; Published: 13 July 2020 Abstract: Big data has become a very frequent research topic, due to the increase in data availability. In this introductory paper, we make the linkage between the use of big data and Econophysics, a research field which uses a large amount of data and deals with complex systems. Different approaches such as power laws and complex networks are discussed, as possible frameworks to analyze complex phenomena that could be studied using Econophysics and resorting to big data. Keywords: big data; complexity; networks; stock markets; power laws 1. Introduction Big data has become a very popular expression in recent years, related to the advance of technology which allows, on the one hand, the recovery of a great amount of data, and on the other hand, the analysis of that data, benefiting from the increasing computational capacity of devices. -
The Physical Modelling of Society: a Historical Perspective
Physica A 314 (2002) 1–14 www.elsevier.com/locate/physa The physical modelling ofsociety: a historical perspective Philip Ball Nature, 4-6 Crinan St, London N1 9XW, UK Abstract By seeking to uncover the rules ofcollective human activities, today’s statistical physicists are aiming to return to their roots. Statistics originated in the study ofsocial numbers in the 17th century, and the discovery ofstatistical invariants in data on births and deaths, crimes and marriages led some scientists and philosophers to conclude that society was governed by immutable “natural” laws beyond the reach ofgovernments, ofwhich the Gaussian “error curve” became regarded as the leitmotif. While statistics .ourished as a mathematical tool of all the sciences in the 19th century, it provoked passionate responses from philosophers, novelists and social commentators. Social statistics also guided Maxwell and Boltzmann towards the utilization ofprobability distributions in the development ofthe kinetic theory ofgases, the foundationof statistical mechanics. c 2002 Elsevier Science B.V. All rights reserved. Keywords: Statistics; History ofphysics; Statistical mechanics; Social science; Gaussian 1. Introduction The introduction ofprobability into the fundamentalnature ofthe quantum world by Bohr, Born and Schr8odinger in the 1920s famously scandalized some scholars ofscience’s philosophical foundations.But arguments about chance, probability and determinism were no less heated in the mid-19th century, when statistical ideas entered classical physics. James Clerk Maxwell (1878) let probabilistic physics bring him to the verge of mysticism: “it is the peculiar function of physical science to lead us to the conÿnes of the incomprehensible, and to bid us behold and receive it in faith, till such time as the E-mail address: [email protected] (P. -
John Von Neumann Between Physics and Economics: a Methodological Note
Review of Economic Analysis 5 (2013) 177–189 1973-3909/2013177 John von Neumann between Physics and Economics: A methodological note LUCA LAMBERTINI∗y University of Bologna A methodological discussion is proposed, aiming at illustrating an analogy between game theory in particular (and mathematical economics in general) and quantum mechanics. This analogy relies on the equivalence of the two fundamental operators employed in the two fields, namely, the expected value in economics and the density matrix in quantum physics. I conjecture that this coincidence can be traced back to the contributions of von Neumann in both disciplines. Keywords: expected value, density matrix, uncertainty, quantum games JEL Classifications: B25, B41, C70 1 Introduction Over the last twenty years, a growing amount of attention has been devoted to the history of game theory. Among other reasons, this interest can largely be justified on the basis of the Nobel prize to John Nash, John Harsanyi and Reinhard Selten in 1994, to Robert Aumann and Thomas Schelling in 2005 and to Leonid Hurwicz, Eric Maskin and Roger Myerson in 2007 (for mechanism design).1 However, the literature dealing with the history of game theory mainly adopts an inner per- spective, i.e., an angle that allows us to reconstruct the developments of this sub-discipline under the general headings of economics. My aim is different, to the extent that I intend to pro- pose an interpretation of the formal relationships between game theory (and economics) and the hard sciences. Strictly speaking, this view is not new, as the idea that von Neumann’s interest in mathematics, logic and quantum mechanics is critical to our understanding of the genesis of ∗I would like to thank Jurek Konieczny (Editor), an anonymous referee, Corrado Benassi, Ennio Cavaz- zuti, George Leitmann, Massimo Marinacci, Stephen Martin, Manuela Mosca and Arsen Palestini for insightful comments and discussion.