Agent-based computational model for crude oil futures market Jo˜ao Miguel Pereira Abrantes Thesis to obtain the Master of Science Degree in Engineering Physics Supervisors: Prof. Tiago Morais Delgado Domingos Prof. Carlos Augusto Santos Silva Examination Comitee Chairperson: Prof. Lu´ıs Filipe Moreira Mendes Supervisor: Prof. Tiago Morais Delgado Domingos Members of the Committee: Prof. Joa˜o Carlos de Carvalho Sa´ Seixas Prof. Miguel St. Aubyn June, 2014 Acknowledgments This project marks the end of a journey that would not be possible without the invaluable help of some people whom I have the pleasure to thank. First, I would like to thank all the people involved in this project. Thanks to Prof. Tiago Domingos and Prof. Tˆania Sousa for their guidance in the preparation of this thesis, as well as Jo˜ao Magalh˜aes for hissuggestionsandpatienceinthecomputationalpartofthework. IwouldalsoliketothankProf. Carlos Silva for being available to be my co-advisor and for his corrections. I am also grateful to everybody in Environment and Energy Scientific Area of IN+ who welcomed me so well. In particular, I would specially like to thank Nuno Sarmento for always being available to solve the logistic issues. Last, but certainly not least, I would like to thank my father Jos´e, my mother Lu´ısa and my brothers Filipe and Pedro. All the support they have provided me over the years was the greatest gift anyone has ever given me. Finally, I would like to send a special greeting to my friends, especially to Condesso and Tiago who accompanied me on this Physics adventure. To all, my sincere thanks. This work is supported by project “Energy Wars” (QREN 7929), funded by QREN/FEDER. i Abstract The sharp rise in crude oil prices over the last decade has reinforced the interest of the scientific community in understanding the major causes of price movements. In this sense, the increasing finan- cialization of commodity markets as well as the existence of speculators in the crude oil market can be two possible reasons for high price volatility and financial instability. The purpose of this thesis was to develop and test an agent-based computational model for the crude oil futures market, which simulates the crude oil price evolution through speculative behaviour of the marketparticipants. Thisspeculativecomponentwasmodelledbytheinteractionofheterogeneousagents (fundamentalists, chartists, contrariansandnoisetraders)withlearningability, whichadapttheirbeliefs and change their investment strategies over time according to their past performance, measured by the number of winning trades. Finally, each agent decides the number of investment positions and submits orderstobuyorsellfuturescontracts. Thecrudeoilpriceincreases(decreases)whetherthereisanexcess of demand (supply). The results revealed that the intermittent behaviour that characterizes the oscillations of agents’ strategies as well as the non stationarity of market activity are crucial to the emergence of stylized facts, namely the absence of autocorrelations in returns, heavy-tailed distribution of returns and volatility clustering. This model is presented as a starting point to further research about the key factors that replicate the oil price as well as the importance of speculation on its formation. Keywords Agent-based model, Behavioural Finance, Computational Finance, Crude oil price, Heterogeneous agents. iii Resumo O aumento do pre¸co do petr´oleo na u´ltima d´ecada tem vindo a reforc¸ar o interesse cient´ıfico em perceberasmaiorescausasdassuasfluctua¸c˜oes. Nestesentido,afinanceirizac¸˜aodosmercadosbemcomo a existˆencia de especuladores podem explicar os per´ıodos de alta volatilidade e instabilidade financeira. O principal objectivo da tese consistiu no desenvolvimento de um modelo computacional baseado em agentes, que simula a evolu¸c˜ao do pre¸co do petr´oleo atrav´es do comportamento especulativo dos agentes financeiros. Esta componente especulativa foi modelada atrav´es da interac¸c˜ao de agentes heterog´eneos (fundamentais, t´ecnicos, contr´arios e investidores ing´enuos) com capacidade de aprendizagem, que adap- tam sucessivamente as suas expectativas tendo em conta as performances passadas das suas estrat´egias. Finalmente, cada agente decide o nu´mero de posic¸˜oes de investimento e submete ordens de compra ou venda de contractos de futuros de petr´oleo. O pre¸co do petr´oleo aumenta (diminui) se existir um excesso de procura (oferta). Os resultados revelaram que a intermitˆencia que caracteriza a varia¸c˜ao da frac¸c˜ao de cada tipo de estrat´egia, bem como a n˜ao estacionariedade do nu´mero de agentes no mercado s˜ao cruciais para o aparecimento dos factos estilizados, nomeadamente a ausˆencia de autocorrela¸c˜ao nas s´eries de retornos, a rejei¸ca˜o da hip´otese de normalidade dos retornos di´arios e o facto de eventos extremos de magnitude semelhante estarem agrupados ao longo do tempo. Este modelo apresenta-se como um ponto de partida para pesquisa futura sobre os os factores fun- damentais que permitem a replicac¸˜ao do prec¸o do petr´oleo e sobre a importˆancia da especulac¸˜ao na sua forma¸c˜ao. Palavras Chave Agentes heterog´eneos, Finan¸cas Comportamentais, Financ¸as Computacionais, Modelos baseados em agentes, Prec¸o do petr´oleo. v Contents 1 Introduction 1 2 What drives crude oil prices? 7 2.1 The Market Oil Pricing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Historical Analysis of Crude Oil Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 World Oil Consumption and Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.4 The Role of Market Fundamentals and Speculation . . . . . . . . . . . . . . . . . . . . . . 14 2.4.1 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4.2 Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4.3 Speculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3 Crude Oil Financial Markets 23 3.1 The Pricing of Commodity Contracts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2 Stylized Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2.1 Absence of Autocorrelations in Returns . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.2 Heavy-tailed Distribution of Returns . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2.3 Volatility Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4 Agent-based Modelling 31 4.1 Foundations of Agent-based Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2 Agent-based Modelling in Financial Markets . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.3.1 SFI Artificial Stock Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.3.2 Lux and Marchesi Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.3.3 Adaptive Belief Systems Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.4 ABMs for Crude Oil Financial Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5 ABM for the crude oil futures market 49 5.1 Description and Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.1.1 Economic Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.1.2 Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.1.3 Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5.1.4 Market Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 vii 5.2 Simulation Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2.1 Simulation Settings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.2.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 5.3 Stylized Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6 Conclusions and Future Work 65 Bibliography 69 viii