Some Essays on models in the Bond and Energy Markets

The term structure of interest rates plays a fundamental role as an indicator of economy and market trends, as well as a supporting tool for macroeconomic strategies, investment choices or hedging practices. Therefore, the availability of proper techniques to model and predict its dynamics is of crucial importance for players in the financial markets. Along this path, the dissertation initially examined the reliability of parametric and neural network models to fit and predict the term structure of interest rates in emerging markets, focusing on the Brazilian, Russian, Indian, Chines and South African (BRICS) bond markets. The focus on the BRICS is straightforward: the dynamics of their term structures make tricky the application of consolidated yield curve models. In this respect, BRICS yield curve act as stress testers. The study then examined how to apply the above cited models to energy derivatives, focusing the attention on the Natural Gas and Electricity futures, motivated by the existence of similarity. The research was carried out using ad hoc routines, such as the R package "DeRezende.Ferreira", developed by the candidate and now freely downloadable at the Comprehensive R Archive Network (CRAN) repository*, as well as by means of code written in MatLab 2021a - 2022a and Python (3.10.10) using the open-source Keras (2.4.3) library with TensorFlow (2.4.0) as backend. The dissertation consists of four chapters based on published and/or under submission materials. Chapter 1 is an excerpt of the paper • Castello, O.; Resta, M. Modeling the Yield Curve of BRICS Countries: Parametric vs. Machine Learning Techniques. Risks 2022 The work firstly offers a comprehensive analysis of the BRICS bond market and then investigates and compares the abilities of the parametric Five–Factor De Rezende–Ferreira model and Feed–Forward Neural Networks to fit the yield curves. Chapter 2 is again focused on the BRICS market but investigates a methodology to identify optimal time–varying parameters for parametric yield curve models. The work then investigates the ability of this method both for in–sample fitting and out–of–sample prediction. Various forecasting methods are examined: the Univariate Autoregressive process AR(1), the TBATS and the Autoregressive Integrated Moving Average (ARIMA) combined to Nonlinear Autoregressive Neural Networks (NAR–NN). Chapter 3 studies the term structure dynamics in the Natural Gas futures market. This chapter represents an extension of the paper • Castello, O., Resta, M. (2022). Modeling and Forecasting Natural Gas Futures Prices Dynamics: An Integrated Approach. In: Corazza, M., Perna, C., Pizzi, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2022. After showing that the natural gas and bond markets share similar stylized facts, we exploit these findings to examine whether techniques conventionally employed on the bonds market can be effectively used also for accurate in–sample fitting and out–of–sample forecast. We worked at first in–sample and we compared the performance of three models: the Four–Factor Dynamic Nelson–Siegel–Svensson (4F-DNSS), the Five–Factor Dynamic De Rezende–Ferreira (5F–DRF) and the B–Spline. Then, we turned the attention on forecasting, and explored the effectiveness of a hybrid methodology relying on the joint use of 4F–DNSS, 5F–DRF and B–Splines with Nonlinear Autoregressive Neural Networks (NAR–NNs). Empirical study was carried on using the Dutch Title Transfer Facility (TTF) daily futures prices in the period from January 2011 to June 2022 which included also recent market turmoil to validate the overall effectiveness of the framework. Chapter 4 analyzes the predictability of the electricity futures prices term structure with Artificial Neural Networks. Prices time series and futures curves are characterized by high volatility which is a direct consequence of an inelastic demand and of the non–storable nature of the underlying commodity. We analyzed the forecasting power of several neural network models, including Nonlinear Autoregressive (NAR–NNs), NAR with Exogenous Inputs (NARX–NNs), Long Short–Term Memory (LSTM–NNs) and Encoder–Decoder Long Short–Term Memory Neural Networks (ED–LSTM–NNs). We carried out an extensive study of the models predictive capabilities using both the univariate and multivariate setting. Additionally, we explored whether incorporating various exogenous components such as Carbon Emission Certificates (CO2) spot prices, as well as Natural Gas and Coal futures prices can lead to improvements of the models performances. The data of the European Energy Exchange (EEX) power market were adopted to test the models. Chapter 4 concludes. ____________________________ * https://cran.r-project.org/web/packages/DeRezende.Ferreira/index.htm

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

The impact of effective factors on the Iranian electricity market in comparison to the Spanish electricity market

Electricity market analysis is important to access strategic market information which can be further employed to pass energy policies. Due to the advantages of privatization, the Iranian government has taken certain fundamental steps in order to construct a competitive market, after passing the pertinent laws in its parliament as to the privatization of the electricity market. This PhD thesis presents a detailed econometric analysis of the Iranian electricity market by means of various approaches of time series analysis. The main idea of this thesis rests on the investigation of the state and degree of competition in the Iranian electricity market using the time series analysis approach. This research explains Iranian electricity market mechanisms with linear and nonlinear time series statistical approaches. Mechanisms that were previously developed in the Spanish electricity market provide an opportunity to employ time series modeling to further compare the two markets as a benchmark. This study examines the two indices-price and load-of these markets via time series analysis. In following, it compares these time series analysis in order to present separate estimation models for each index price and load time series (for each market). Implemented models include: linear models (ARIMA), conditional heteroskedastic models (ARMA-GARCH) and nonlinear models (SETAR and ARMA-TGARCH). To assess the best fitted model, MSE and residual volatility analysis tests were implemented. Assuming the conditional variance of our data, the researcher propose the ARMA-TGARCH model as the best suited model for the Iranian electricity market price, ARMA-GARCH model for Iranian electricity load and also for Spanish electricity price and load. Finally, this research explored the role of load in each market using specific statistical methods such as scatter plots, etc. This study will be quite helpful to establish the state of the Iranian electricity market and how exactly to stimulate its degree of competition. The researcher further suggested that at current state, no significant relationship between price and load in the Iranian electricity market exists. This result led the researcher to examine the impact of other macro and microeconomic factors and indices on the electricity prices in the Iranian market. The most important of these factors have been selected through the study and research of energy markets; the most significant include the Henry Hub Natural Gas Spot Price, Europe Brent Crude Oil Spot Price, the US dollar/Iranian Rial foreign exchange rate, and the Iranian (Tehran) Stock Exchange, specifically the TEPIX. Here, the goal was to survey the potential relationship between these factors and Iranian electricity prices via time series correlation analysis. The researcher also clarified that no significant relationship exists between price and these macro and microeconomic factors in the Iranian electricity market. The researcher also assembled forecast from the best estimates derived from the study models and carry out simulations to develop forecasting models. This short-term forecasting is applied to both Iranian and Spanish electricity prices and their respective loads. These predictions also clearly showed the different patterns between these indices¿price and load¿in the Iranian electricity market. Finally, considering the results obtained through the tests and data analysis which examined the Iranian electricity market, it is concluded that the Iranian electricity market could be still recognized as a non-free/centralized market questioning the claimed policies thus far implemented toward decentralizing and privatizing the Iranian marketNasrazadaniAnalizar el mercado de la electricidad es muy importante para acceder a la información estratégica de dicho mercado que además puede ser empleado para aprobar las políticas energéticas. Debido a las ventajas de la privatización, el gobierno iraní ha tomado ciertas medidas fundamentales para construir un mercado competitivo, después de aprobar las leyes fundamentales en su parlamento que permiten la privatización del mercado eléctrico. Esta tesis doctoral presenta un análisis econométrico detallado del mercado eléctrico iraní, mediante diversos enfoques de análisis de series temporales. La idea principal de esta tesis se basa en la investigación así como el grado de consecución en el mercado eléctrico de Irán utilizando el enfoque de análisis de series temporales. En esta investigación se explican los mecanismos de mercado de la electricidad iraní mediante enfoques de series temporales lineales y no lineales. Los mecanismos que se han desarrollado con anterioridad en el mercado eléctrico español ofrecen la oportunidad de emplear el modelado de series temporales para comparar los dos mercados analizados como punto de referencia.Este estudio examina los dos índices –precio y potencia– de estos mercados mediante series temporales. A continuación, se comparan estas series temporales con el fin de presentar modelos para cada precio y potencia de dichas series temporales. Los modelos implementados incluyen: modelos lineales (ARIMA), modelos heterocedásticos condicionales (ARMA-GARCH) y modelos no lineales (SETAR y ARMA-TGARCH). Para evaluar el mejor modelo ajustado se calcula el error cuadrático medio (ECM) y se implementan los tests que permiten analizar la volatilidad residual. Suponiendo que nuestros datos detectan varianza condicional, la investigadora propone el modelo ARMA-TGARCH como el modelo más apropiado para el precio de mercado de la electricidad de Irán, modelo ARMA-GARCH para la potencia iraní y también para los precios y potencia de la electricidad española. Por último, esta investigación explora el papel de la potencia en cada mercado usando métodos estadísticos específicos, tales como gráficos de dispersión, etc. Este estudio será de gran ayuda para establecer el estado del mercado de la electricidad de Irán y cómo exactamente se puede estimular su grado de competencia. La investigadora sugiere, además, que en el estado actual, no existe una relación significativa entre el precio y la potencia en el mercado eléctrico iraní. Este resultado ha llevado a la investigadora a examinar el impacto de otros factores e índices macro y microeconómicos sobre los precios de la electricidad en el mercado de Irán. Los factores más importante han sido seleccionados a través del estudio y la investigación de los mercados energéticos; los más significativos incluyen el precio “Spot del Henry Hub Natural Gas”, “Precio Spot del Petróleo Brent Europeo”, “Dólar estadounidense / Rial iraní tipo de cambio”, y la Bolsa de Valores (Teherán), en concreto el TEPIX. En este caso, el objetivo ha sido estudiar la posible relación entre estos factores y precios de la electricidad de Irán a través de la correlación de series temporales. La investigadora también ha reunido las predicciones de las mejores estimaciones derivadas de los modelos estudiados y ha llevado a cabo simulaciones para desarrollar modelos de predicción. Finalmente, considerando los resultados obtenidos a través de los testes y análisis de datos que examinó el mercado de la electricidad de Irán, se concluye que el mercado de la electricidad de Irán podría ser aún reconocido como un mercado no libre / centralizado cuestionando las políticas reclamadas hasta ahora implementadas hacia la descentralización y la privatización del mercado iraní

Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models

This work is a comparative study of different univariate and multivariate time series predictive models as applied to Bitcoin, other cryptocurrencies, and other related financial time series data. ARIMA models, long regarded as the gold standard of univariate financial time series prediction due to both its flexibility and simplicity, are used a baseline for prediction. Given the highly correlative nature amongst different cryptocurrencies, this work aims to show the benefit of forecasting with multivariate time series models—primarily focusing on a novel parameter optimization of VARIMA models outlined in this paper. These models are trained on 3 years of historical data, aggregated from different cryptocurrency exchanges by Coinmarketcap.com, which includes: daily average prices and trading volume. Historical time series data of traditional market data, including the stock Nvidia, the de facto leading manufacture of gaming GPU’s, is also analyzed in conjunction with cryptocurrency prices, as gaming GPU’s have played a significant role in solving the profitable SHA256 hashing problems associated with cryptocurrency mining and have seen equivalently correlated investor attention as a result. Models are trained on this historical data using moving window subsets, with window lengths of 100, 200, and 300 days and forecasting 1 day into the future. Validation of this prediction against the actually price from that day are done with following metrics: Directional Forecasting (DF), Mean Absolute Error (MAE), and Mean Squared Error (MSE)

Artificial intelligence for decision making in energy demand-side response

This thesis examines the role and application of data-driven Artificial Intelligence (AI) approaches for the energy demand-side response (DR). It follows the point of view of a service provider company/aggregator looking to support its decision-making and operation. Overall, the study identifies data-driven AI methods as an essential tool and a key enabler for DR. The thesis is organised into two parts. It first provides an overview of AI methods utilised for DR applications based on a systematic review of over 160 papers, 40 commercial initiatives, and 21 large-scale projects. The reviewed work is categorised based on the type of AI algorithm(s) employed and the DR application area of the AI methods. The end of the first part of the thesis discusses the advantages and potential limitations of the reviewed AI techniques for different DR tasks and how they compare to traditional approaches. The second part of the thesis centres around designing machine learning algorithms for DR. The undertaken empirical work highlights the importance of data quality for providing fair, robust, and safe AI systems in DR — a high-stakes domain. It furthers the state of the art by providing a structured approach for data preparation and data augmentation in DR to minimise propagating effects in the modelling process. The empirical findings on residential response behaviour show better response behaviour in households with internet access, air-conditioning systems, power-intensive appliances, and lower gas usage. However, some insights raise questions about whether the reported levels of consumers’ engagement in DR schemes translate to actual curtailment behaviour and the individual rationale of customer response to DR signals. The presented approach also proposes a reinforcement learning framework for the decision problem of an aggregator selecting a set of consumers for DR events. This approach can support an aggregator in leveraging small-scale flexibility resources by providing an automated end-to-end framework to select the set of consumers for demand curtailment during Demand-Side Response (DR) signals in a dynamic environment while considering a long-term view of their selection process

Examining the customer journey of solar home system users in Rwanda and forecasting their future electricity demand

Globally, 771 million people lack access to electricity, out of which 75% live in Sub-Saharan Africa (IEA, 2020b). Electricity grid expansion can be costly in rural areas, which often have low population densities. Solar home systems (SHS) have provided people worldwide an alternative option to gain electricity access. A SHS consists of a solar panel, battery and accompanying appliances. This research aims to advance the understanding of the SHS customer journey using a case study of SHS customers in Rwanda. This study developed a framework outlining households’ pre- to post-purchase experiences, which included awareness and purchase, both current and future SHS usage and finally customers’ upgrade, switching and retention preferences. A mixed methods approach was utilised to examine these steps, including structured interviews with the SHS providers’ customers (n=100) and staff (n=19), two focus groups with customers (n=24), as well as a time series analysis and descriptive statistics of database customers (n=63,299). A convolutional neural network (CNN) was created to forecast individual SHS users’ future electricity consumption in the next week, month and three months based on their previous hourly usage. Despite the volatility of SHS usage data, the CNN was able to forecast individual users’ future electricity more accurately than the naïve baseline, which assumes a continuation of previous usage. The time series analysis revealed an evening usage peak for non-television users, whilst customers with a television experienced an additional peak around midday. SHS recommendations prior and post-purchase were common, highlighting the circular nature of the customer journey. The main purchase reason and usage activity were having a clean energy source and phone charging respectively. A better understanding of the SHS customer journey may increase the number of households with electricity access, as companies can better address the purchase barriers and tap into the power of customer recommendations