[pt] A modelagem do mercado financeiro requer uma descrição completa da estatística dos preços assim como de sua dinâmica. Analisamos as flutuações de preço do mercado de ações brasileiro (IBOVESPA) em escala de tempo intradiária, no período 2002-2004, considerando distribuições q- Gaussianas P(q) (x,t) provenientes da estatística não-extensiva de Tsallis. Estas distribuições são soluções de uma equação de Fokker-Planck (EFP) não-linear, que permite modelar a difusão anômala observada na série temporal de preços de alta freqüência a partir de mecanismos de feedback estatístico na dinâmica de formação de preços. Nossos resultados mostram que, quando retornos de preços são medidos em escalas temporais de até 30 minutos, as distribuições empíricas são bem descritas por q-Gaussianas, com parâmetro não- extensivo q estacionário e com truncamento exponencial das caudas. Através da análise das propriedades de escala temporal dos primeiros momentos das distribuições empíricas, analisamos a consistência entre a evolução temporal observada e a prevista pela EFP não- linear e obtemos os parâmetros do modelo que caracterizam a dinâmica de nosso mercado. A presença de correlação temporal retarda a convergência das distribuições de retornos de preços para o regime Gaussiano de acordo com o T.L.C., surgindo assim um novo regime q-Gaussiano para escalas de tempo curtas, cujo comportamento superdifusivo é regido pela EFP considerada. Nossos resultados indicam que esta modelagem fornece uma descrição adequada para a dinâmica das flutuações de preços intradiárias do IBOVESPA. / [en] The stock market modeling requires a complete statistical description of the price and its dynamics. We analyze the intra-day Brazilian stock market price fluctuations (IBOVESPA), in the period 2002-2004, considering q-Gaussians distributions P(q) (x,t) derived from Tsallis non- extensive statistics. Such distributions are solutions of a non-linear Fokker-Planck equation (F.P.E.), allowing to model the anomalous diffusion found at high frequency price time series from statistical feedback mechanisms in the dynamics of price formation. Our results show that, when returns are measured over intervals less than 30 minutes, the empirical distributions are well fitted by q- Gaussians, with stationary non-extensive parameter q and exponential damped tails. From the time scale properties of the first moments of the empirical distributions, we analyze the consistency between the observed time evolution and the foreseen behavior within the non-linear F.P.E. and get the model parameters that characterize our high frequency market dynamics. The presence of time correlation slows down the convergence of the price return distributions to a Gaussian regime according to C.L.T., giving rise to a new q-Gaussian regime for very short time scales, with super diffusive behavior driven by the considered F.P.E. Our results show that this modeling provides an adequate description of the dynamics of the Brazilian stock market intra-day price fluctuations.

Thesis (MBA (Business Management))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: Econophysics is a relatively new branch of physics. It entails the use of models in physics applied to economics. The distributions of financial time series are the aspect most intensely studied by physicists. This study is based on a study by Kleinert and Chen who applied the Boltzmann distribution to stock exchange data to define a market temperature that may be used by investors to indicate an impending stock market crash. Most econophysicists’ analysed the tail regions of the distributions as the tails represent risk in financial data. This study’s focus of analysis, on the other hand is the characterisation of the central portion of the probability distribution. The Boltzmann distribution, a cornerstone in statistical physics, yields an exponential distribution. The objective of this study is to investigate the suitability of using a market volatility forecasting method from econophysics, namely the Boltzmann/market temperature method. As econometric benchmark the ARCH/GARCH method is used. Stock market indices are known to be non-normally (non-Gaussian) distributed. The distribution pattern of a stock market index of reasonable high sampling frequency (typically interday or intraday) is leptokurtic with heavy tails. Mesoscopic (interday) distributions of financial time series have been found to be exponential distributions. If the empirical exponential distribution is therefore interpreted as a Boltzmann distribution, then a market temperature can be calculated from the exponential distribution. Empirical data for this study is in the form of daily closing values of the Johannesburg Stock Exchange (JSE) All Share Index (ALSI) and the Standard & Poor 500 (S & P 500) index for the period 1995 through to 2008. The Kleinert and Chen study made use of intraday data obtained from established markets. This study differs from the Kleinert and Chen study in that interday data obtained from an emerging market, namely the South African stock market is used. Neither of the aforementioned two differences had a significant influence on the results of this study. The JSE ALSI log-return data displays non-Gaussian properties and the Laplace (double exponential) distribution fit the data well. A plot of the market temperature provided a clear indication of when stock market crashes occurred. Results of the econophysical (Boltzmann/market temperature) method compared well to results of the econometric (ARCH/GARCH) method and subject to certain improvements can be utilised successfully. A leptokurtic, non-Gaussian nature was established for daily log-returns of the JSE ALSI and the S & P 500 index. The Laplace (double exponential) distribution fit the annual logreturns of the JSE ALSI and S & P 500 index well. As a result of the good Laplace fit, annual market temperatures could be calculated for the JSE ALSI and the S & P 500 index. The market temperature method was effective in identifying market crashes for both indices, but a limitation of the method is that only annual market temperatures can be determined. The availability of intraday stock index data should improve the interval for which market temperature can be determined. / AFRIKAANSE OPSOMMING: Ekonofisika is ‘n relatiewe nuwe studieveld. Dit behels die toepassing van fisiese modelle op finansiële data. Die waarskynlikheidsversdelings van finansiële tydreekse is die aspek wat meeste deur fisisie bestudeer word. Hierdie studie is gebaseer op ‘n studie deur Kleinert en Chen. Hulle het die Boltzmann-verspreiding op ‘n aandele-indeks toegepas en ‘n mark-temperatuur bepaal. Hierdie mark-temperatuur kan deur ontleders gebruik word as waarskuwingsmeganisme teen moontlike aandelebeurs ineenstortings. Die meeste fisisie het die uiterste areas van die verspreidingskurwes geanaliseer omdat hierdie uiterste area risiko in finansiële data verteenwoordig. Die analitiese fokus van hierdie studie, aan die ander kant, is die karakterisering van die die sentrale areas van die waarskeinlikheidsverdeling. Die Boltzmann verspreiding, die hoeksteen van Statistiese Fisika lewer ‘n eksponensiële waarskynlikheidsverdeling. Die doel van hierdie studie is om ‘n ondersoek te doen na die geskiktheid van die gebruik van ‘n ekonofisiese, vooruitskattingsmetode, naamlik die Boltzmann/mark-temperatuur model. As ekonometriese verwysing is die “ARCH/GARCH” metode toegepas. Aandelemark indekse is bekend vir die nie-Gaussiese verspreiding daarvan. Die verspreidingspatroon van ‘n aandelemark indeks met‘n redelike hoë steekproef frekwensie (in die orde van ‘n dag of minder) is leptokurties met breë stert-dele. Mesoskopiese (interdag) verspreidings van finansiële tydreekse is getipeer as eksponensieël. Indien die empiriese eksponensiële-verspreiding as ‘n Boltzmann-verspreiding geinterpreteer word, kan ‘n mark-temperatuur daarvoor bereken word. Empiriese data vir die gebruik in hierdie studie is in die vorm van daaglikse sluitingswaardes van die Johannesburgse Effektebeurs (JSE) se Alle Aandele Indeks (ALSI) en die Standard en Poor 500 (S & P 500) indeks vir die periode 1995 tot en met 2008. Die Kleinert en Chen studie het van intradag data vanuit ‘n ontwikkelde mark gebruik gemaak. Hierdie studie verskil egter van die Kleinert en Chen studie deurdat van interdag data vanuit ‘n opkomende mark, naamlik die Suid-Afrikaanse aandelemark, gebruik is. Nie een van die twee voorafgaande verskille het ‘n beduidende invloed op die resultate van hierdie studie gehad nie. Die JSE ALSI se logaritmiese opbrengs data vertoon nie-Gaussiese eienskappe en die Laplace (dubbeleksponensiële) verspreiding beskryf die data goed. ‘n Grafiek van die mark-temperatuur vertoon duidelik wanneer aandelemarkineenstortings plaasgevind het. Resultate van die ekonofisiese (Boltzmann/mark-temperatuur) metode vergelyk goed met resultate van die ekonometriese (“ARCH/GARCH”) metode en onderhewig aan sekere verbeteringe kan dit met sukses toegepas word. ‘n Leptokurtiese, nie-Gaussiese aard is vir daaglike opbrengswaardes vir die JSE ALSI en die S & P 500 indeks vasgestel. ‘n Laplace (dubbel-eksponensiële) verspreiding kan goed op die jaarlikse logaritmiese opbrengste van die JSE ALSI en die S & P 500 indeks toegepas word. As gevolg van die goeie aanwending van die Laplace-verspreiding kan ‘n jaarlikse mark-temperatuur vir die JSE ALSI en die S & P 500 indeks bereken word. Die mark-temperatuur metode is effektief in die identifisering van aandelemarkineenstorings vir beide indekse, hoewel daar ‘n beperking is op die aantal mark-temperature wat bereken kan word. Die beskikbaarheid van intradag aandele indekswaardes behoort die interval waarvoor mark-temperature bereken kan word te verbeter.

Dans ce travail, nous nous sommes intéressés d'une part à la théorie du chaos multiplicatif Gaussien introduite par Kahane en 1985 et d'autre part à la théorie des matrices aléatoires dont les pionniers sont Wigner, Wishart et Dyson. La première partie de ce manuscrit contient une brève introduction à ces deux théories ainsi que les contributions personnelles de ce manuscrit expliquées rapidement. Les parties suivantes contiennent les textes des articles publiés [1], [2], [3], [4], [5] et pré-publiés [6], [7], [8] sur ces résultats dans lesquels le lecteur pourra trouver des développements plus détaillés / In this thesis, we are interested on the one hand in the theory of Gaussian multiplicative chaos introduced by Kahane in 1985 and on the other hand in random matrix theory whose pioneers are Wigner, Wishart and Dyson. The first part of this manuscript constitutes a brief introduction to those two theories and also contains the personal contributions of this work rapidly explained. The following parts contain the texts of the published articles [1], [2], [3], [4], [5] and pre-prints [6], [7], [8] on those results where the reader can find more detailed developments

This paper studies the topological properties of the International Trade Network (ITN) among world countries using a network analysis. We explore the distribu- tions of the most important network statistics measuring connectivity, assortativ- ity and clustering. We show that the topological properties of the weighted rep- resentation of the ITN are very different from those obtained by a binary network approach. In particular, we find that: (i) the majority of countries are character- ized by weak trade relationships, (ii) well connected countries tend to trade with poorly connected partners and (iii) countries holding more intense trade relation- ships are more clustered. Finally, we display that all structural properties of the ITN have remained remarkably stable over time.

This thesis is concerned with the analysis of economic network formation. There are three novel sections to this thesis (Chapters 5, 6 and 8). In the first, the non-cooperative communication network formation model of Bala and Goyal (2000) (BG) is re-assessed under conditions of no inertia. It is found that the Strict Nash circle (or wheel) structure is still the equilibrium outcome for n = 3 under no inertia. However, a counter-example for n = 4 shows that with no inertia infinite cycles are possible, and hence the system does not converge. In fact, cycles are found to quickly dominate outcomes for n > 4 and further numerical simulations of conditions approximating no inertia (probability of updating > 0.8 to 1) indicate that cycles account for a dramatic slowing of convergence times. These results, together with the experimental evidence of Falk and Kosfeld (2003) (FK) motivate the second contribution of this thesis. A novel artificial agent model is constructed that allows for a vast strategy space (including the Best Response) and permits agents to learn from each other as was indicated by the FK results. After calibration, this model replicates many of the FK experimental results and finds that an externality exploiting ratio of benefits and costs (rather than the difference) combined with a simple altruism score is a good proxy for the human objective function. Furthermore, the inequity aversion results of FK are found to arise as an emergent property of the system. The third novel section of this thesis turns to the nature of network formation in a trust-based context. A modified Iterated Prisoners' Dilemma (IPD) model is developed which enables agents to play an additional and costly network forming action. Initially, canonical analytical results are obtained despite this modification under uniform (non-local) interactions. However, as agent network decisions are 'turned on' persistent cooperation is observed. Furthermore, in contrast to the vast majority of non-local, or static network models in the literature, it is found that a-periodic, complex dynamics result for the system in the long-run. Subsequent analysis of this regime indicates that the network dynamics have fingerprints of self-organized criticality (SOC). Whilst evidence for SOC is found in many physical systems, such dynamics have been seldom, if ever, reported in the strategic interaction literature.

In this thesis we examine the ability of the log-periodic power law model to accurately predict the end of speculative bubbles on financial markets through modeling of asset price dynamics on a selection of historical bubbles. The methods we use are based on a nonlinear least squares estimation which yields predictions of when the bubble will change regime.We find evidence which support the occurrence of LPPL-patterns leading up to the change in regime; asset prices during bubble periods seem to oscillate around a faster-than-exponential growth. In most cases the estimation yields accurate predictions, although we conclude that the predictions are quite dependent on at which point in time the prediction is conducted. We also find that the end of a speculative bubble seems to be influenced by both endogenous speculative growth and exogenous factors. For this reason we propose a new way of interpreting the predictions of the model, where the end dates should be interpreted as the start of a time period where the asset prices are especially sensitive to exogenous events. We propose that negative news during this time period results in a regime shift of the bubble. This study is the first to address both the possibilities and the limitations of the LPPL-model, and should therefore be considered as a contribution to the academia.

In the present paper, we identify several distributions from Physics and study their applicability to phenomena such as distribution of income, wealth, and expenditure. Firstly, we apply logistic distribution to these data and we find that it fits very well the annual data for the entire income interval including for upper income segment of population. Secondly, we apply Fermi-Dirac distribution to these data. We seek to explain possible correlations and analogies between economic systems and statistical thermodynamics systems. We try to explain their behaviour and properties when we correlate physical variables with macroeconomic aggregates and indicators. Then we draw some analogies between parameters of the Fermi-Dirac distribution and macroeconomic variables. Thirdly, as complex systems are modelled using polynomial distributions, we apply polynomials to the annual sets of data and we find that it fits very well also the entire income interval. Fourthly, we develop a new methodology to approach dynamically the income, wealth, and expenditure distribution similarly with dynamical complex systems. This methodology was applied to different time intervals consisting of consecutive years up to 35 years. Finally, we develop a mathematical model based on a Hamiltonian that maximises utility function applied to Ramsey model using Fermi-Dirac and polynomial utility functions. We find some theoretical connections with time preference theory. We apply these distributions to a large pool of data from countries with different levels of development, using different methods for calculation of income, wealth, and expenditure.

[pt] Consideramos o oscilador harmônico com amortecimento aleatório em presença de ruído externo. Os ruídos, representando perturbações externas e internas, são modelados pelo processo de Ornstein-Uhlenbeck ou ruído branco e pelo processo dicotômico ou ruído branco, respectivamente. Usando técnicas de sistemas dinâmicos, analisamos o valor médio e a dispersão da posição e da velocidade do oscilador harmônico estocástico, apresentando resultados analíticos e numéricos. Em particular, obtemos expressões para a expansão de baixa-ordem em relação ao tempo de correlação da perturbação interna, no caso da atuação do ruído dicotômico. Finalmente, usando o modelo de oscilador harmônico com amortecimento aleatório como referência, investigamos a série intradiária de preços do mercado brasileiro. / [en] We consider the random damping harmonic oscillator in presence of external noise. The noises, representing external and internal perturbations, are modeled as an Ornstein-Uhlenbeck process or a white noise and as a dichotomous process or a white noise, respectively. Using dynamical systems tools, we analyze the expected value as well as the dispersion of the stochastic harmonic oscillator s position and velocity, presenting analytical and numerical results. In particular, we also provide expressions for the low-order expansion in the correlation time of the internal perturbation, in the case the dichotomous noise is at play. Using random damped harmonic oscillator model as a reference, we conclude by investigating the intra-day Brazilian stock price series.

Die folgende Arbeit befasst sich mit den Untersuchungen von Problemen der Optimierung von Ressourcen in Umgebungen mit unvorhersehbarem Verhalten, wo: (i) nicht alle Informationen verfügbar sind, und (ii) die Umgebung unbekannte zeitliche Veränderungen aufweist. Diese Dissertation ist folgendermaßen gegliedert: Teil I stellt das Investitionsmodell vor. Es wird sowohl eine analytische als auch eine numerische Analyse der Dynamik dieses Modells für feste Investitionsstrategien in verschiedenen zufälligen Umgebungen vorgestellt. In diesem Investitionsmodell hängt die Dynamik des Budgets des Agenten x(t) von der Zufälligkeit der exogenen Rendite r(t) ab, wofür verschiedene Annahmen diskutiert wurden. Die Heavy-tailed Verteilung des Budgets wurde numerisch untersucht und mit theoretischen Vorhersagen verglichen. In Teil II wurde ein Investitionsszenario mit stilisierten exogenen Renditen untersucht, das durch eine periodische Funktion mit verschiedenen Arten und Stärken von Rauschen charakterisiert ist. In diesem Szenario wurden unterschiedliche Strategien, Agenten-Verhalten und Agenten Fähigkeiten zur Vorhersage der zukünftigen r(t) untersucht. Hier wurden Null-intelligenz-Agenten, die über technischen Analysen verfügen, mit Agenten, die über genetischen Algorithmen verfügen, verglichen. Umfangreiche Ergebnisse von Computersimulationen wurden präsentiert, in denen nachgewiesen wurde, dass für exogene Renditen mit Periodizität: (i) das wagemutige das vorsichtige Verhalten überbietet, und (ii) die genetischen Algorithmen in der Lage sind, die optimalen Investitionsstrategien zu finden und deshalb die anderen Strategien überbieten. Obwohl der Schwerpunkt dieser Dissertation im Zusammenhang mit dem Gebiet der Informatik präsentiert wurde, können die hier vorgestellten Ergebnisse auch in Szenarien angewendet werden, in denen der Agent anderere Arten von Ressourcen steuern muss, wie z.B. Energie, Zeitverbrauch, erwartete Lebensdauer, etc. / The main goal of this PhD thesis is to investigate some of the problems related to optimization of resources in environments with unpredictable behavior where: (i) not all information is available and (ii) the environment presents unknown temporal changes. The investigations in this PhD thesis are divided in two parts: Part I presents the investment model and some analytical as well as numerical analysis of the dynamics of this model for fixed investment strategies in different random environments. In this investment model, the dynamics of the investor''s budget x(t) depend on the stochasticity of the exogenous return on investment r(t) for which different model assumptions are discussed. The fat-tail distribution of the budget is investigated numerically and compared with theoretical predictions. Part II investigates an investment scenario with stylized exogenous returns characterized by a periodic function with different types and levels of noise. In this scenario, different strategies, agent''s behaviors and agent''s capacities to predict the future r(t) are investigated. Here, ''zero-intelligent'' agents using technical analysis (such as moving least squares) are compared with agents using genetic algorithms to predict r(t). Results are presented for extensive computer simulations, which shows that for exogenous returns with periodicity: (i) the daring behavior outperforms the cautious behavior and (ii) the genetic algorithm is able to find the optimal investment strategy by itself, thus outperforming the other strategies considered. Finally, the investment model is extended to include the formation of common investment projects between agents. Although the main focus of this PhD thesis is more related to the area of computer science, the results presented here can be also applied to scenarios where the agent has to control other kinds of resources, such as energy, time consumption, expected life time, etc.

O dilema do prisioneiro (DP) é um dos principais jogos da teoria dos jogos. No dilema do prisioneiro discreto (DPD), dois prisioneiros têm as opções de cooperar ou desertar. Um jogador cooperador não delata seu comparsa, já um desertor delata. Se um cooperar e o outro desertar, o cooperador fica preso por cinco anos e o desertor fica livre. Se ambos cooperarem, ficam presos por um ano e, se ambos desertarem, ficam presos por três anos. Quando o DP é repetido, a cooperação pode emergir entre agentes egoístas. Realizamos um estudo analítico para o DPD, que produziu uma formulação da evolução do nível médio de cooperação e da tentação crítica (valor de tentação que causa mudança abrupta do nível de cooperação). No dilema do prisioneiro contínuo (DPC), cada jogador apresenta um nível de cooperação que define o grau de cooperação. Utilizamos o DPC para estudar o efeito da personalidade dos jogadores sobre a emergência da cooperação. Para isso, propusemos novas estratégias: uma baseada na personalidade dos jogadores e outras duas baseadas na comparação entre o ganho obtido e a aspiração do jogador. Todas as estratégias apresentavam algum mecanismo de cópia do estado do vizinho com maior ganho na vizinhança, mecanismo este, herdado da estratégia darwiniana. Os resultados mostraram que o DPC aumenta o nível médio de cooperação do sistema, quando comparado ao DPD. No entanto, as diferentes estratégias não aumentaram a cooperação comparado à cooperação obtida com a estratégia darwiniana. Então propusemos o uso do coeficiente de agrupamentos, coeficiente de Gini e entropias de Shannon, Tsallis e Kullback-Leibler para classificar os sistemas, em que os agentes jogam o DPD com a estratégia darwiniana, quanto ao nível de cooperação. Como analisamos valores de médias configuracionais, tais classificadores não foram eficientes ao classificar os sistemas. Isso é consequência da existência de distribuições de extremos nos resultados que compõem as médias. As distribuições de extremos suscitaram uma discussão acerca da definição do regime de cooperação no dilema do prisioneiro. Discutimos também as consequências de utilizar apenas valores médios nos resultados ignorando seus desvios e as distribuições. / Prisoner\'s dilemma (PD) is one of the main games of game theory. In discrete prisoner\'s dilemma (DPD), two prisoners have the options to cooperate or to defect. A cooperator player does not defect his accomplice, while a defector does. If one player cooperates and the other defects, the cooperator gets jailed for five years and the defector goes free. If both cooperate, they get jailed during one year and if both defect, they get jailed during three years. When this game is repeated, cooperation may emerge among selfish individuals. We perform an analytical study for the DPD, that produced a formulation for the evolution of the mean cooperation level and for the critical temptation values (temptation values that promote abrupt modifications in the cooperation level). In continuous prisoner\'s dilemma (CPD), each player has a level of cooperation that defines his/her degree of cooperation. We used the CPD to study the effect of the players\' personality on the emergence of cooperation. For this, we propose new strategies: one based on the players\' personality and two others based on the comparison between the player\'s obtained payoff and the desire one. All strategies present some mechanism that copies the state of the neighbor with the highest payoff in the neighborhood, mechanism inherited from the Darwinian strategy. The results showed that the CPD increases the average cooperation level of the system when compared to DPD. However, different strategies do not increased the cooperation compared to cooperation obtained with the Darwinian strategy. So, we propose the use of cluster coefficient, Gini coefficient and entropy of Shannon, Tsallis and Kullback-Leibler as classifiers to classify systems, in which the individuals play DPD with Darwinian strategy, by the cooperation level. As configurational averages were analyzed, such classifiers were not efficient in classifying the systems. This is due to the existence of distributions with extreme values of the results that compose the means. Distributions with extremes values emerged a discussion about the definition of the cooperation state in the prisoner\'s dilemma. We also discussed the consequences of using only average results in the analysis ignoring their deviations and distributions.