Uncovering predictability in the evolution of the WTI oil futures curve

Accurately forecasting the price of oil, the world's most actively traded commodity, is of great importance to both academics and practitioners. We contribute by proposing a functional time series based method to model and forecast oil futures. Our approach boasts a number of theoretical and practical advantages including effectively exploiting underlying process dynamics missed by classical discrete approaches. We evaluate the finite-sample performance against established benchmarks using a model confidence set test. A realistic out-of-sample exercise provides strong support for the adoption of our approach with it residing in the superior set of models in all considered instances.Comment: 28 pages, 4 figures, to appear in European Financial Managemen

latent Dirichlet allocation method-based nowcasting approach for prediction of silver price

Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Commodity price volatility, stock market performance and economic growth: evidence from BRICS countries

Abstracts in English, Afrikaans and ZuluThe study investigated the nexus between commodity price volatility, stock market performance, and economic growth in the emerging economies of Brazil, Russia, India, China, and South Africa (the BRICS) predicated on two hypotheses. First, the study hypothesised that in modern integrated financial systems, commodity price volatility predisposes stock market performance to be non-linearly related to economic growth. The second hypothesis was that financial crises are an inescapable feature of modern financial systems. The study used daily data on stock indices and selected commodity prices as well as monthly data on national output proxies and stock indices. The study analysed data for non-linearities, fractality, and entropy behaviour using the spectral causality approach, univariate GARCH, EGARCH, FIGARCH, DCC-GARCH, and Markov Regime Switching (MRS) – GARCH. The four main findings were: first, spectral causality tests signalled dynamic non-linearities in the relationship between the three commodity futures prices and the BRICS stock indices. Second, the predominantly non-linear relationship between commodity prices and stock prices was reflected in the nexus between the national output proxies and the indices of the five main commodity classes. Third, spectral causality analysis revealed that the causal structures between commodity prices and national output proxies were non-linear and dynamic. Fourth, the Nyblom parameter stability tests revealed evidence of structural breaks in the data that was analysed. The DCC-GARCH model uncovered strong evidence of contagion, spillovers, and interdependence. The study added to the body of knowledge in three ways. First, micro and macro levels of commodity price changes were linked with corresponding stock market performance indicator changes. Second, unlike earlier studies on the commodity price – stock market performance – economic growth nexus, the study employed spectral causality analysis, single - regime GARCH analysis, Dynamic Conditional Correlation (DCC) – GARCH and a two-step Markov – Regime – Switching – GARCH as a unified analytical approach. Third, spectral causality graphs depicting relationships between stock indices and national output proxies revealed benign business cycle effects, thus, contributing to broadening the scope of business cycle theoryBusiness ManagementPhD. (Management Studies

A New Theory to Forecast the Price of Non Renewable Energy Resources with Mass and Energy-Capital Conservation Equations.

The mass and energy-capital conservation equations are employed to study the time evolution of mass and price of non-renewable energy resources, extracted and sold to the market, in case of no-accumulation and no-depletion; i.e. when the resources are extracted and sold to the market at the same mass flow rate. The Hotelling rule for non-renewable resources, i.e. an exponential increase of the price at the rate of the current interest multiplied the time, is shown to be a special case of the general energy-capital conservation equation when the mass flow rate of extracted resources is unity. The mass and energy-capital conservation equations are solved jointly to investigated the time evolution of the extracted resources. The parameter PIFE, “Price Increase Factor of Extracted resources”, is the difference between the interest rate of capital, typically the inflation rate, and the mass flow rate of extraction of non-renewable resources. The price of the extracted resources increases if PIFE is greater than zero, i.e. the mass flow rate of extraction is smaller than the inflation rate. The price is constant if PIFE is zero, i.e. the mass flow rate of extraction is equal to the inflation rate. The price is decreasing with time if PIFE is smaller than zero, i.e. the mass flow rate of extraction is greater than the inflation rate. The price of selling resources varies with time according to the relation between the parameters PIFE and PIFS, “Price Increase Factor of Selling resources”, which is the difference between the extraction rate and the interest rates of selling resources, prime or discount rate. The price of selling resources increases with time if the initial price is greater than CIPS, “Critical Initial Price of Sold resources”, which depends on the initial price of extracted resources, the interest rate of non-extracted resources, and the difference between PIFS and PIFE or is greater than CIPES, “Critical Initial Price Extreme of Selling resources”, which depends on the initial price of extracted resources, the interest rate of non-extracted resources, and PIFS. The price of selling resources increases temporarily with time if the interest rates of non-extracted and extracted resources are equal, i.e. PIFE is equal to PIFS, and the initial price is greater than CIPES, “Critical Initial Price Extreme of Selling resources”. The price evolutions of the difference between selling and extracted resources are investigated according to the relation between extraction rate and interest rate of extracted and selling resources. The price difference increases with time if PIFS is greater than PIFE of the extracted resources and the initial price is greater than the critical price of selling resources, which depends on the initial price of extracted resources and the interest rate of non-extracted and extracted resources. The price difference decreases with time if PIFS is greater than PIFE and the initial price is smaller than the critical price of selling resources. The other cases are discussed extensively in the paper. The price evolution of non-renewable resources versus the consumption rate is investigated with the aim of constructing the energy supply curve. The case studied is without accumulation nor depletion of the resources and the mass and energy-capital conservation equations are solved under the condition of the same mass flow rate of extraction and sale. The energy supply curve of extracted resource is dependent on the new parameter, RINE, “Rate of Interest of Non-extracted resources on the Extraction rate”. The energy supply curve of selling resource is dependent on the new parameter, RISE, “Rate of Interest of Sold resources on the Extraction rate”, in case the rate of interest of non-extracted resources, rN, is nil. The energy supply curve of selling resources is dependent also on two dimensionless parameters, “Dimensionless Critical Initial Price of Sold resources”, i.e. DCIPS, and “Dimensionless Critical Initial Price Extreme of Sold resources”, i.e. DCIPES. The energy supply curve of selling resources is investigated under different relations between three parameters, i.e. extraction rate and interest rates of extracted and selling resources. New trends are observed in the economic market of non-renewable energy resources. The energy supply curve of the difference between selling and extracted resource is dependent on two dimensionless parameters, “Critical Initial Price Difference”, i.e. CIPD, and “Critical Extreme of the Initial Price Difference”, i.e. CEIPD. The price difference between selling and extracted resources is investigated versus the dimensionless mass flow rate of extraction. The evolution is dependent on four parameters: RINE, RISE, DCIPS, and DCIPES

Selected Papers from the 8th Annual Conference of Energy Economics and Management

This collection represents successful invited submissions from the papers presented at the 8th Annual Conference of Energy Economics and Management held in Beijing, China, 22–24 September 2017. With over 500 participants, the conference was co-hosted by the Management Science Department of National Natural Science Foundation of China, the Chinese Society of Energy Economics and Management, and Renmin University of China on the subject area of “Energy Transition of China: Opportunities and Challenges”. The major strategies to transform the energy system of China to a sustainable model include energy/economic structure adjustment, resource conservation, and technology innovation. Accordingly, the conference and its associated publications encourage research to address the major issues faced in supporting the energy transition of China. Papers published in this collection cover the broad spectrum of energy economics issues, including building energy efficiency, industrial energy demand, public policies to promote new energy technologies, power system control technology, emission reduction policies in energy-intensive industries, emission measurements of cities, energy price movement, and the impact of new energy vehicle