Previsão dos preços do petróleo Brent com modelos univariados

O objetivo deste estudo é a previsão da série temporal de preços do petróleo Brent através de diversos modelos econométricos e de Machine Learning de forma a identificar qual é o que obtém um melhor ajuste e performance. As métricas de avaliação da performance que auxiliaram na comparação entre os modelos foram o Mean Absolute Error (MAE), o Mean Absolute Percentage Error (MAPE), o Mean Squared Error (MSE) e o Root Mean Squared Error (RMSE). Com isto, conseguiu-se perceber que existe uma tendência, sendo o modelo ARIMA (Auto Regressive Integrated Moving Average) e o modelo LSTM (Long short-term memory) os mais estudados na literatura e os que obtiveram melhores previsões em todas as métricas utilizadas. Em suma, verificou-se que tem existido um crescimento gradual da literatura desta temática, que a presente dissertação vai ao encontro dos resultados obtidos por outros autores e que ainda há caminho para novas investigações.This study aims to forecast the Brent crude oil price series over time using various forecasting models belonging to classical econometrics and Machine Learning to determine which model produces the best adjustment. The performance metrics that aided in the model comparison were the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) (RMSE). As a result, it was possible to detect a trend, with the models ARIMA (Auto Regressive Integrated Moving Average) and LSTM (Long Short-Term Memory) being the most studied in the literature and yielding the best predictions across all metrics. Overall, it was concluded that there had been a gradual growth in the literature on this topic, that this dissertation results agree with the one found in the recent literature, and that there is still room for developing new research

Nature inspired computational intelligence for financial contagion modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market