Fourier Neural Network Approximation of Transition Densities in Finance

This paper introduces FourNet, a novel single-layer feed-forward neural network (FFNN) method designed to approximate transition densities for which closed-form expressions of their Fourier transforms, i.e. characteristic functions, are available. A unique feature of FourNet lies in its use of a Gaussian activation function, enabling exact Fourier and inverse Fourier transformations and drawing analogies with the Gaussian mixture model. We mathematically establish FourNet's capacity to approximate transition densities in the $L_2$-sense arbitrarily well with finite number of neurons. The parameters of FourNet are learned by minimizing a loss function derived from the known characteristic function and the Fourier transform of the FFNN, complemented by a strategic sampling approach to enhance training. Through a rigorous and comprehensive error analysis, we derive informative bounds for the $L_2$ estimation error and the potential (pointwise) loss of nonnegativity in the estimated densities. FourNet's accuracy and versatility are demonstrated through a wide range of dynamics common in quantitative finance, including L\'{e}vy processes and the Heston stochastic volatility models-including those augmented with the self-exciting Queue-Hawkes jump process.Comment: 27 pages, 5 figure

Modelo de previsão de Séries Temporais para previsão do preço das ações da Netflix

O mercado de ações é uma parte importante de qualquer economia e, por isso, compreendê-lo é objetivo de vários estudos, pois permite que o investidor tome decisões mais firmes e certeiras. Entretanto, realizar previsões de séries financeiras é uma tarefa difícil, uma vez que são compostas de ruídos e apresentam um comportamento bastante errático. Este trabalho faz o uso dos modelos de média móvel integrada autorregressiva e do modelo de média móvel integrado autorregressiva sazonal, para prever o preço de abertura das ações da Netflix na bolsa de valores norte-americana NASDAQ. O Critério de Informação de Akaike foi usado para selecionar o melhor modelo, e o desempenho dos modelos foi analisado através do erro quadrático médio. Depois de selecionar o modelo mais preciso, realizou-se uma comparação das médias dos períodos antes e durante a pandemia. Os resultados obtidos revelam que o modelo ARIMA (0,1,1) foi o que conseguiu realizar previsões mais precisas, e que a pandemia teve um impacto positivo no preço das ações

Data science in economics: Comprehensive review of advanced machine learning and deep learning methods

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models

Meta-regressão para a previsão de erros em séries temporais

Orientador: Prof. Luiz Eduardo Soares de OliveiraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 16/07/2021Inclui referências: p. 59-65Área de concentração: Ciência da ComputaçãoResumo: A presença de séries temporais em governos, pesquisas e empresas cresceu devido ao aumento da captura, processamento e armazenamento de dados. Por outro lado, existem vários modelos preditivos para cada serie temporal. Assim, a tarefa de previsão e escolha do melhor modelo para uma determinada serie temporal pode ser custosa. O objetivo deste artigo e criar um meta-regressor para prever erros em previsões de series temporais para facilitar a escolha do melhor modelo dada uma serie temporal e um modelo de previsão. Para ajustar este meta-regressor, usamos 60 características de extraídas de cerca de 100.000 series temporais. Nossos resultados experimentais mostram que o método proposto supera todos os quinze regressores usados neste trabalho e produz uma soma de erros 20% menor que o melhor regressor.Abstract: The presence of time series in governments, research, and companies has grown due to increased data capture, processing, and storage. On the other hand, there are several predictive models for every time-series. Thus, the prediction task and choosing the best model for a given time series can be costly. The purpose of this paper is to create a meta-regressor for predicting errors in time series predictions to facilitate the choice of the best model given a time series and a forecast model. To fit this meta-regressor, we have used 60 time-series features extract from about 100,000 time-series. Our experimental results show that the proposed method surpasses all fifteen regressors used in this work and produces a sum of errors 20% smaller than the best regressor

Contribution to Financial Modeling and Financial Forecasting

This thesis consists of three chapters. Each chapter is independent research that is conducted during my study. This research is concentrated on financial time series modeling and forecasting. On first chapter, the research aims to prove that any abnormal behavior in debt level is a signal of future unexpected return for firms that is listed in indexes in this study, hence it is a signal to buy. In order to prove this theory multiple indexes from around the world were taken into consideration. This behavior is consistent in most of indexes around the word. The second chapter investigate the effect of United State president speech on value of United State Currency in Foreign Exchange Rate market. In this analysis it is shown that during the time the president is delivering a speech there is distinctive changes in USD value and volatility in global markets. This chapter implies that this effect cannot be captured by linear models, and the impact of the presidential speech is short term. Finally, the third chapter which is the major research of this thesis, suggest two new methods that potentially enhance the financial time series forecasting. Firstly, the new ARMA-RNN model is presented. The suggested model is inheriting the process of Autoregressive Moving Average model which is extensively studied, and train a recurrent neural network based on it to benefit from unique ability of ARMA model as well as strength and nonlinearity of artificial neural network. Secondly the research investigates the use of different frequency of data for input layer to predict the same data on output layer. In other words, artificial neural networks are trained on higher frequency data to predict lower frequency. Finally, both stated method is combined to achieve more superior predictive model

El transporte de gas natural en Argentina : análisis de la ruptura contractual y sus alcances

En el transporte de gas natural el rol de la incertidumbre desde el punto de vista económico-financiero es de gran interés dado que los costos de construcción de gasoductos representan una erogación significativa y, a su vez, estos activos presentan un alto riesgo por carecer de usos alternativos. Entonces, las características específicas de los activos y el poder de mercado de los distintos participantes en el sector petrolero y gasífero motivan la intervención regulatoria del sector. Las medidas adoptadas por las agencias regulatorias que son distintas a las previstas inicialmente en el contrato de concesión pueden ocasionar consecuencias inciertas en el flujo de fondos. En Argentina, la sanción en 2002 de la Ley de Emergencia Pública y Reforma del Régimen Cambiario (L. 25.561) implicó el cambio del esquema tarifario en los medios de transporte de gas, alterando significativamente la ecuación económica de los prestadores del servicio. Debe resaltarse que, bajo estas condiciones, las compañías se vieron forzadas a ajustar su estructura de costos operativos producto del proceso inflacionario que se ha desencadenado por más de una década. Por ende, en esta tesis se aborda la ruptura contractual en el sector argentino de transporte de gas natural. Para ello, se expone la evolución histórica del mercado, se elabora un modelo teórico que describe los incentivos gubernamentales que promueven la ruptura y se miden las consecuencias económico-financieras en la eficiencia del sistema. Luego de un primer capítulo introductorio donde se plasman los objetivos de la tesis, se presenta la evolución del mercado del gas natural en Argentina. A partir de la dinámica observada en las últimas dos décadas, el estudio se enfocó en el cambio de las condiciones contractuales luego de la derogación de la Ley de Convertibilidad. En el capítulo 3 se analizan los acuerdos regulados entre agentes (v.g. Estado, operadores) mediante el tratamiento que propone la teoría de contratos. El desarrollo formal es novedoso pues apunta a modelizar los factores que llevan a los gobiernos a incumplir los contratos de concesión o privatización. El modelo desarrollado es contrastado con el caso argentino del transporte de gas natural. En el capítulo 4 se estima un modelo econométrico de frontera estocástica basado en costos para empresas del sector. La coexistencia entre la falta de inversión y la restricción de ingresos invita a evaluar la relación entre la ruptura contractual y la performance operativa. Principalmente, fue posible identificar que si hubiera ahorro en costos, este hecho no ha afectado la eficiencia operativa de las compañías. Luego, el quinto capítulo complementa el análisis de eficiencia desde el punto de vista técnico. Mediante una aplicación original de Redes Neuronales Artificiales en el estudio de gasoductos, se desarrolla un modelo perceptrón multicapa para evidenciar cambios en la eficiencia técnica luego del congelamiento tarifario implantado en 2002. De este modo se pudo detectar que la ineficiencia ha sido reducida a partir de la restricción de ingresos. Finalmente, se presentan las conclusiones generales de esta tesis y se delinean tópicos de desarrollo futuro para completar el análisis propuesto.The role of economic uncertainty in the natural gas transport market is of great interest since pipeline construction costs involve significant expenditures, while at the same time these assets are highly risky due to the fact that they lack of alternative uses. Therefore, both this specificity and the market power of oil and gas companies justify the introduction of regulation in the market. Measures adopted by regulatory authorities that are different from the initially agreed ones may bring uncertain consequences on the cash flow stream. In Argentina the Public Emergency Law (L. 25.561) voted in 2002 implied a modification in the pricing scheme, deeply affecting the economic balance of natural gas transport firms since then. It should be noted that, under these circumstances, companies were forced to adjust their cost structure due to the inflation process unfolding for more than a decade. Consequently, this thesis addresses the contract break in the Argentinean natural gas transport system. In order to achieve this goal, the historical development of the market is described, a model is developed where governmental incentives to breach contracts are depicted, and economic consequences on the system efficiency are measured. After an introductory chapter where the thesis objectives are outlined, the evolution of the natural market in Argentina is presented. From the events observed in the last two decades, the analysis is focused on the contractual modifications after the Convertibility Law repeal. In the third chapter regulated contracts are analyzed through game theory. This theoretical approach is innovative since it aims at modelling critical factors that provide incentives for governments to break concessions or privatized contracts. This model is illustrated with the case of natural gas transport market in Argentina. In the fourth chapter, a cost stochastic frontier model based on costs from transport firms worldwide is estimated. The coexistence of income restriction and the lack of investment encourages to evaluate the relationship between contract breach and operative performance. In particular, it was possible to identify that, when there had been cost savings, this fact could have positively affected firms as regards costefficiency. Then, the fifth chapter supplements the efficiency analysis from a technical perspective. Through an original application of Artificial Neural Networks in the field of pipelines, a multilayer perceptron model was developed to detect changes in the technical efficiency after the rate constraint applied in 2002. In this respect, it was possible to detect that inefficiency was reduced due to income restriction. Finally, the main conclusions of this thesis are presented and future research topics are outlined to complete the proposed study.Fil: de Meio Reggiani, Martín Carlos. Universidad Nacional del Sur. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentin