Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore

We apply multivariate statistical methods to a large dataset of Singapore’s macroeconomic variables and global economic indicators with the objective of forecasting business cycles in a small open economy. The empirical results suggest that three common factors are present in the time series at the quarterly frequency, which can be interpreted as world, regional and domestic economic cycles. This leads us to estimate a factor-augmented vector autoregressive (FAVAR) model for the purpose of optimally forecasting real economic activity in Singapore. By taking explicit account of the common factor dynamics, we find that iterative forecasts generated by this model are significantly more accurate than direct multi-step predictions based on the identified factors as well as forecasts from univariate and vector autoregressions.business cycles; principal components; dynamic factor model; factor-augmented VAR; forecasting; Singapore

Covid-19 and stock market volatility : a clustering approach for S&P 500 industry indices

Mestrado Bolonha em FinançasThe COVID-19 pandemic was the infectious disease outbreak that has had the strongest impact on the U.S. stock market. In this dissertation, we study how this impact affected some of the conditional volatilities of S&P 500 industries, using a new model feature-based clustering method on a fitted threshold generalised autoregressive conditional heteroscedasticity (TGARCH) specification. Rather than using the estimated model parameters to compute a distance matrix for the stock indices, which cannot capture all the information about the dependence of the time-varying variance, we suggest using a distance based on the autocorrelations of the estimated conditional volatilities. Both hierarchical (complete linkage) and non-hierarchical (k-means) unsupervised machine learning algorithms are used to assign the set of industries into clusters. The results show a clear change in the composition of each cluster between the period before the first U.S. COVID-19 case and the period during the pandemic, leading to the conclusion that the similarities or distances between industries underwent a significant change, with the industries most affected by the pandemic being Hotels, Consumer Durable & Apparel, Automobile, and Airlines. It was also made an analysis regarding the forecast accuracy of simple asymmetric GARCH models applied to S&P 500 industries and use the model forecast errors for different horizons to calculate a distance matrix for the stock indices. A hierarchical clustering algorithm is used to assign the set of industries into clusters. We found homogeneous clusters of industries in terms of the impact of COVID-19 on US stock market volatility. The industries most affected by the pandemic and with less accurate stock market prediction (Hotels, Resorts & Cruise Lines, Airline, Apparel, Accessories & Luxury Goods, and Automobile) are separated in Euclidean distance from those industries that were less impacted by COVID-19 and which had more accurate forecasting (Pharmaceuticals, Internet & Direct Marketing Retail, Data Processing & Outsourcing Services, and Movies & Entertainment).A pandemia do COVID-19 foi o surto com maior impacto de sempre no mercado de ações dos EUA. Nesta dissertação, estudamos como este impacto afetou algumas das volatilidades condicionadas das indústrias do S&P 500, usando métodos de clustering aplicados numa especificação de um modelo derivado (threshold) do generalizado autorregressivo condicional e heterocedástico (TGARCH). OS parâmetros do modelo estimado não capturam toda a informação acerca da dependência da variância não constante ao longo do tempo pelo que usamos distância baseada nas autocorrelações das volatilidades condicionais, para calcular a matriz de distância para os índices de ações. Ambos os algorítmos hierárquicos (complete linkage) e não-hierárquicos (k-means), técnicas de unsupervised machine learning, são usados para agrupar conjuntos de indústrias em clusters. Os resultados mostram uma clara mudança na composição de cada cluster entre o período anterior ao primeiro caso de COVID-19 nos EUA e o período durante a pandemia. As semelhanças ou distâncias entre as indústrias sofreram uma mudança significativa, sendo as indústrias mais afetadas pela pandemia, os Hotéis, Bens de Consumo Duráveis e Vestuário, Automóveis e Companhias Aéreas. Foi feita uma análise quanto à precisão da previsão de modelos GARCH assimétricos simples aplicados a indústrias do S&P 500. Esses erros de previsão calculados em diferentes intervalos de tempo, durante a pandemia, foram usados para calcular a matriz de distâncias entre os indices de mercado. Um algoritmo de hierarchical clustering foi usado para agrupar um grupo de indústrias em clusters. Obtivemos clusters homogéneos em termos do impacto do COVID-19 na volatilidade do mercado de ações nos EUA. As indústrias mais afectadas pela pandemia, nas quais os modelos preditivos se mostraram menos precisos em termos preditivos (Hotels, Resorts & Cruise Lines, Airline, Apparel, Acessories & Luxury Goods, and Automobile) estão separadas por uma distância Euclideana das indústrias que foram menos impactadas pelo COVID-19 e as previsões mais precisas (Pharmaceuticals, Internet, Data Processing, e Movies & Entertainment).info:eu-repo/semantics/publishedVersio

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

European soybean to benefit people and the environment

Europe imports large amounts of soybean that are predominantly used for livestock feed, mainly sourced from Brazil, USA and Argentina. In addition, the demand for GM-free soybean for human consumption is project to increase. Soybean has higher protein quality and digestibility than other legumes, along with high concentrations of isoflavones, phytosterols and minerals that enhance the nutritional value as a human food ingredient. Here, we examine the potential to increase soybean production across Europe for livestock feed and direct human consumption, and review possible effects on the environment and human health. Simulations and field data indicate rainfed soybean yields of 3.1 +/- 1.2 t ha-1 from southern UK through to southern Europe (compared to a 3.5 t ha-1 average from North America). Drought-prone southern regions and cooler northern regions require breeding to incorporate stress-tolerance traits. Literature synthesized in this work evidenced soybean properties important to human nutrition, health, and traits related to food processing compared to alternative protein sources. While acknowledging the uncertainties inherent in any modelling exercise, our findings suggest that further integrating soybean into European agriculture could reduce GHG emissions by 37-291 Mt CO2e year-1 and fertiliser N use by 0.6-1.2 Mt year-1, concurrently improving human health and nutrition

Leapfrogging into hydrogen technology: China's 1990-2000 energy balance

As a country beginning its motorization process, China must confront the problems attached to an oil-based car society. In adopting conventional automobile technology, the country would aggravate an already unstable oil balance while pushing up carbon dioxide levels. Not only would domestic problems emerge, but international concerns regarding oil shortage, global pollution, and the energy security balance would also result from erecting a traditional automotive infrastructure. One viable alternative the country can consider is a leapfrog towards hydrogen technology. By using hydrogen as the fuel source and investing in a hydrogen-based car society, China could overstep the problems created by an oil-based infrastructure. When examining China's potential for undertaking this technology leapfrog, China's energy past and future must be considered. China's energy balance and energy resources play a crucial role in determining the country's leapfrogging possibility. This paper analyzes one facet in China's energy balance by scrutinizing energy expenditures between 1990-2000. By looking at data compiled from major international and academic sources, an overview of China's past energy consumption and production activities is presented. Patterns and discrepancies in Chinese coal, oil, natural gas, and nuclear production are unveiled and the trends considered in relation to the country's energy balance. Each energy sector is analyzed separately for consumption and production trends. Because implementation of hydrogen technology is governed by energy resource availability and energy use patterns, such an energy analysis provides an appropriate background from which China's leapfrogging potential can be evaluated. -- Der in China beginnende Motorisierungsprozess führt zwangsläufig zu einer Konfrontation mit den Umwelt- und Ressourcenkonflikten, die mit der erdölbasierten Automobiltech-nologie des vergangenen Jahrhunderts Hand in Hand gehen. Dadurch würde sich zum einen die gegenwärtig problematische Erdölenergiebilanz des Landes durch erhöhten CO2 Ausstoß verschärfen. Zum Anderen würde der Aufbau einer chinesischen automobilen Infrastruktur auf Erdölbasis, die Ressourcenknappheit in diesem Sektor, die globalen Umweltprobleme und die Sicherung der weltweiten Energiebilanz negativ beeinflussen. Eine realistische Alternative für China ist ein Quantensprung auf dem Gebiet der Auto-mobiltechnologie zu vollziehen und in Wasserstoffmotoren zu investieren. China kann mit einer Automobilwirtschaft auf der Basis einer Wasserstofftechnologie die durch Erdöl-verbrennung geschaffenen Umweltprobleme umgehen. Dieser Artikel untersucht das Potential Chinas zu einem solchen Quantensprung in der Automobiltechnologie. Ein wesentlicher Aspekt ist die Prüfung der Energiebilanz Chinas in den Jahren 1990-2000, die es ermöglicht den Energieverbrauch und die Produktion auf Quellenbasis internationaler Organisationen und wissenschaftlicher Arbeiten, zueinander in ein Verhältnis zu setzen. Die Energiebilanz der einzelnen Sektoren Atomkraft, Kohle, Erdöl und Erdgas wird analysiert und vergleichend werden die zukünftigen Trends prognostiziert. Für die Implementation von Wasserstofftech-nologie ist die Verfügbarkeit und Nutzung von Energieressourcen von zentraler Bedeutung. Eine Analyse der Energiebilanz ist daher die Grundlage für eine wissenschaftlich fundierte Einschätzung des Potentials Chinas zu einem solchen technologischen Quantensprung.

Data Challenges and Data Analytics Solutions for Power Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen