Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual horizons. The data to be used consists of metal-commodity prices in a monthly frequency from 1957 to 2012 from the International Financial Statistics of the IMF on individual metal series. We will also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009) , which are available for download. Regarding short- and long-run comovement, we will apply the techniques and the tests proposed in the common-feature literature to build parsimonious VARs, which possibly entail quasi-structural relationships between different commodity prices and/or between a given commodity price and its potential demand determinants. These parsimonious VARs will be later used as forecasting models to be combined to yield metal-commodity prices optimal forecasts. Regarding out-of-sample forecasts, we will use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates to forecast the returns and prices of metal commodities. With the forecasts of a large number of models (N large) and a large number of time periods (T large), we will apply the techniques put forth by the common-feature literature on forecast combinations. The main contribution of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding forecasting, we show that models incorporating (short-run) commoncycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation. Still, in most cases, forecast combination techniques outperform individual models.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual frequencies. Data consists of metal-commodity prices at a monthly and quarterly frequencies from 1957 to 2012, extracted from the IFS, and annual data, provided from 1900-2010 by the U.S. Geological Survey (USGS). We also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009). We investigate short- and long-run comovement by applying the techniques and the tests proposed in the common-feature literature. One of the main contributions of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding out-of-sample forecasts, our main contribution is to show the benefits of forecast-combination techniques, which outperform individual-model forecasts - including the random-walk model. We use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates and functional forms to forecast the returns and prices of metal commodities. Using a large number of models (N large) and a large number of time periods (T large), we apply the techniques put forth by the common-feature literature on forecast combinations. Empirically, we show that models incorporating (short-run) common-cycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation.

Urban water demand model: the case study of Emilia Romagna (Italy)

Water is the driving force in nature. We use water for washing cars, doing laundry, cooking, taking a shower, but also to generate energy and electricity. Therefore water is a necessary product in our daily lives (USGS. Howard Perlman, 2013). The model that we created is based on the urban water demand computer model from the Pacific Institute (California). With this model we will forecast the future urban water use of Emilia Romagna up to the year of 2030. We will analyze the urban water demand in Emilia Romagna that includes the 9 provinces: Bologna, Ferrara, Forli-Cesena, Modena, Parma, Piacenza, Ravenna, Reggio Emilia and Rimini. The term urban water refers to the water used in cities and suburbs and in homes in the rural areas. This will include the residential, commercial, institutional and the industrial use. In this research, we will cover the water saving technologies that can help to save water for daily use. We will project what influence these technologies have to the urban water demand, and what it can mean for future urban water demands. The ongoing climate change can reduce the snowpack, and extreme floods or droughts in Italy. The changing climate and development patterns are expected to have a significant impact on water demand in the future. We will do this by conducting different scenario analyses, by combining different population projections, climate influence and water saving technologies. In addition, we will also conduct a sensitivity analyses. The several analyses will show us how future urban water demand is likely respond to changes in water conservation technologies, population, climate, water price and consumption. I hope the research can contribute to the insight of the reader’s thoughts and opinion.

Price risk management in the copper market using commodity derivatives and options strategies

Metals price risk management is a key issue related to financial risk in metal markets because of uncertainty of commodity price fluctuation, exchange rate, interest rate changes and huge price risk either to metals’ producers or consumers. Thus, it has been taken into account by all participants in metal markets including metals’ producers, consumers, merchants, banks, investment funds, speculators, traders and so on. Managing price risk provides stable income for both metals’ producers and consumers, so it increases the chance that a firm will invest in attractive projects. The purpose of this research is to evaluate risk management strategies in the copper market. The main tools and strategies of price risk management are hedging and other derivatives such as futures contracts, swaps and options contracts. Hedging is a transaction designed to reduce or eliminate price risk. Derivatives are financial instruments, whose returns are derived from other financial instruments and they are commonly used for managing financial risks. Although derivatives have been around in some form for centuries, their growth has accelerated rapidly during the last 20 years. Nowadays, they are widely used by financial institutions, corporations, professional investors, and individuals. This project is focused on the over-the-counter (OTC) market and its products such as exotic options, particularly Asian options. The first part of the project is a description of basic derivatives and risk management strategies. In addition, this part discusses basic concepts of spot and futures (forward) markets, benefits and costs of risk management and risks and rewards of positions in the derivative markets. The second part considers valuations of commodity derivatives. In this part, the options pricing model DerivaGem is applied to Asian call and put options on London Metal Exchange (LME) copper because it is important to understand how Asian options are valued and to compare theoretical values of the options with their market observed values. Predicting future trends of copper prices is important and would be essential to manage market price risk successfully. Therefore, the third part is a discussion about econometric commodity models. Based on this literature review, the fourth part of the project reports the construction and testing of an econometric model designed to forecast the monthly average price of copper on the LME. More specifically, this part aims at showing how LME copper prices can be explained by means of a simultaneous equation structural model (two-stage least squares regression) connecting supply and demand variables. A simultaneous econometric model for the copper industry is built: {█(Q_t^D=e^((-5.0485))∙P_((t-1))^((-0.1868) )∙〖GDP〗_t^((1.7151) )∙e^((0.0158)∙〖IP〗_t ) @Q_t^S=e^((-3.0785))∙P_((t-1))^((0.5960))∙T_t^((0.1408))∙P_(OIL(t))^((-0.1559))∙〖USDI〗_t^((1.2432))∙〖LIBOR〗_((t-6))^((-0.0561))@Q_t^D=Q_t^S )┤ P_((t-1))^CU=e^((-2.5165))∙〖GDP〗_t^((2.1910))∙e^((0.0202)∙〖IP〗_t )∙T_t^((-0.1799))∙P_(OIL(t))^((0.1991))∙〖USDI〗_t^((-1.5881))∙〖LIBOR〗_((t-6))^((0.0717) Where, Q_t^D and Q_t^Sare world demand for and supply of copper at time t respectively. P(t-1) is the lagged price of copper, which is the focus of the analysis in this part. GDPt is world gross domestic product at time t, which represents aggregate economic activity. In addition, industrial production should be considered here, so the global industrial production growth that is noted as IPt is included in the model. Tt is the time variable, which is a useful proxy for technological change. A proxy variable for the cost of energy in producing copper is the price of oil at time t, which is noted as POIL(t ) . USDIt is the U.S. dollar index variable at time t, which is an important variable for explaining the copper supply and copper prices. At last, LIBOR(t-6) is the 6-month lagged 1-year London Inter bank offering rate of interest. Although, the model can be applicable for different base metals' industries, the omitted exogenous variables such as the price of substitute or a combined variable related to the price of substitutes have not been considered in this study. Based on this econometric model and using a Monte-Carlo simulation analysis, the probabilities that the monthly average copper prices in 2006 and 2007 will be greater than specific strike price of an option are defined. The final part evaluates risk management strategies including options strategies, metal swaps and simple options in relation to the simulation results. The basic options strategies such as bull spreads, bear spreads and butterfly spreads, which are created by using both call and put options in 2006 and 2007 are evaluated. Consequently, each risk management strategy in 2006 and 2007 is analyzed based on the day of data and the price prediction model. As a result, applications stemming from this project include valuing Asian options, developing a copper price prediction model, forecasting and planning, and decision making for price risk management in the copper market.

Advanced Nanostructured Electro-Catalysts for Electricity Generation and Biorenewable Alcohol Conversion

In my Ph.D research, a wet chemistry-based organic solution phase reduction method was developed, and was successfully applied in the preparation of a series of advanced electro-catalysts, including 0-dimensional (0-D) Pt, Pd, Au, and Pd-Ni nanoparticles (NPs), 1-D Pt-Fe nanowires (NWs) and 2-D Pd-Fe nanoleaves (NLs), with controlled size, shape, and morphology. These nanostructured catalysts have demonstrated unique electro-catalytic functions towards electricity production and biorenewable alcohol conversion. The molecular oxygen reduction reaction (ORR) is a long-standing scientific issue for fuel cells due to its sluggish kinetics and the poor catalyst durability. The activity and durability of an electro-catalyst is strongly related with its composition and structure. Based on this point, Pt-Fe NWs with a diameter of 2 - 3 nm were accurately prepared. They have demonstrated a high durability in sulfuric acid due to its 1-D structure, as well as a high ORR activity attributed to its tuned electronic structure. By substituting Pt with Pd using a similar synthesis route, Pd-Fe NLs were prepared and demonstrated a higher ORR activity than Pt and Pd NPs catalysts in the alkaline electrolyte. Recently, biomass-derived alcohols have attracted enormous attention as promising fuels (to replace H2) for low-temperature fuel cells. From this point of view, Pd-Ni NPs were prepared and demonstrated a high electro-catalytic activity towards ethanol oxidation. Comparing to ethanol, the biodiesel waste glycerol is more promising due to its low price and high reactivity. Glycerol (and crude glycerol) was successfully applied as the fuel in an Au-anode anion-exchange membrane fuel cell (AEMFC). By replacing Au with a more active Pt catalyst, simultaneous generation of both high power-density electricity and value-added chemicals (glycerate, tartronate, and mesoxalate) from glycerol was achieved in an AEMFC. To investigate the production of valuable chemicals from glycerol electro-oxidation, two anion-exchange membrane electro-catalytic reactors were designed. The research shows that the electro-oxidation product distribution is strongly dependent on the anode applied potential. Reaction pathways for the electro-oxidation of glycerol on Au/C catalyst have been elucidated: continuous oxidation of OH groups (to produce tartronate and mesoxalate) is predominant at lower potentials, while C-C cleavage (to produce glycolate) is the dominant reaction path at higher potentials.

CAN CHINA USE ALTERNATIVE ENERGIES INSTEAD OF COAL TO PROVIDE MORE ELECTRICITY BY 2030?

Following the rapid growth of China's economy, energy consumption, especially electricity consumption of China, has made a huge increase in the past 30 years. Since China has been using coal as the major energy source to produce electricity during these years, environmental problems have become more and more serious. The research question for this paper is: "Can China use alternative energies instead of coal to produce more electricity in 2030?" Hydro power, nuclear power, natural gas, wind power and solar power are considered as the possible and most popular alternative energies for the current situation of China. To answer the research question above, there are two things to know: How much is the total electricity consumption in China by 2030? And how much electricity can the alternative energies provide in China by 2030? For a more reliable forecast, an econometric model using the Ordinary Least Squares Method is established on this paper to predict the total electricity consumption by 2030. The predicted electricity coming from alternative energy sources by 2030 in China can be calculated from the existing literature. The research results of this paper are analyzed under a reference scenario and a max tech scenario. In the reference scenario, the combination of the alternative energies can provide 47.71% of the total electricity consumption by 2030. In the max tech scenario, it provides 57.96% of the total electricity consumption by 2030. These results are important not only because they indicate the government's long term goal is reachable, but also implies that the natural environment of China could have an inspiring future.

Forecasting Intraday Stock Price Trends with Text Mining Techniques

In this paper, we describe NewsCATS (news categorization and trading system), a system implemented to predict stock price trends for the time immediately after the publication of press releases. NewsCATS consists mainly of three components. The first component retrieves relevant information from press releases through the application of text preprocessing techniques. The second component sorts the press releases into predefined categories. Finally, appropriate trading strategies are derived by the third component by means of the earlier categorization. The findings indicate that a categorization of press releases is able to provide additional information that can be used to forecast stock price trends, but that an adequate trading strategy is essential for the results of the categorization to be fully exploited.

Strategic Behavior in Day-Ahead and Real-Time Markets for Electricity: Offer Cost or Payment Cost Minimization?

This study compares the procurement cost-minimizing and productive efficiency performance of the auction mechanism used by independent system operators (ISOs) in wholesale electricity auction markets in the U.S. with that of a proposed alternative. The current practice allocates energy contracts as if the auction featured a discriminatory final payment method when, in fact, the markets are uniform price auctions. The proposed alternative explicitly accounts for the market clearing price during the allocation phase. We find that the proposed alternative largely outperforms the current practice on the basis of procurement costs in the context of simple auction markets featuring both day-ahead and real-time auctions and that the procurement cost advantage of the alternative is complete when we simulate the effects of increased competition. We also find that a trade-off between the objectives of procurement cost minimization and productive efficiency emerges in our simple auction markets and persists in the face of increased competition.

Efficiency in Deregulated Electricity Markets: Offer Cost Minimization vs. Payment Cost Minimization Auction

This study of the wholesale electricity market compares the efficiency performance of the auction mechanism currently in place in U.S. markets with the performance of a proposed mechanism. The analysis highlights the importance of considering strategic behavior when comparing different institutional systems. We find that in concentrated markets, neither auction mechanism can guarantee an efficient allocation. The advantage of the current mechanism increases with increased price competition if market demand is perfectly inelastic. However, if market demand has some responsiveness to price, the superiority of the current auction with respect to efficiency is not that obvious. We present a case where the proposed auction outperforms the current mechanism on efficiency even if all offers reflect true production costs. We also find that a market designer might face a choice problem with a tradeoff between lower electricity cost and production efficiency. Some implications for social welfare are discussed as well.

Simple-Offer vs. Complex-Offer Auctions in Deregulated Electricity Markets

In my recent experimental research of wholesale electricity auctions, I discovered that the complex structure of the offers leaves a lot of room for strategic behavior, which consequently leads to anti- competitive and inefficient outcomes in the market. A specific feature of these complex-offer auctions is that the sellers submit not only the quantities and the minimum prices at which they are willing to sell, but also the start-up fees that are designed to reimburse the fixed start-up costs of the generation plants. In this paper, using the experimental method I compare the performance of two complex-offer auctions (COAs) against the performance of a simple-offer auction (SOA), in which the sellers have to recover all their generation costs --- fixed and variable ---through a uniform market-clearing price. I find that the SOA significantly reduces consumer prices and lowers price volatility. It mitigates anti-competitive effects that are present in the COAs and achieves allocative efficiency more quickly.

Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework

This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.

Can a Newly Proposed Mechanism for Allocating Contracts in U.S. Electricity Wholesale Markets Lead to Lower Prices? A Game Theoretic Analysis

This study of the wholesale electricity market compares the cost-minimizing performance of the auction mechanism currently in place in U.S. markets with the performance of a proposed replacement. The current mechanism chooses an allocation of contracts that minimizes a fictional cost calculated using pay-as-offer pricing. Then suppliers are paid the market clearing price. The proposed mechanism uses the market clearing price in the allocation phase as well as in the payment phase. In concentrated markets, the proposed mechanism outperforms the current mechanism even when strategic behavior by suppliers is taken into account. The advantage of the proposed mechanism increases with increased price competition.

The first renewable forward market mechanisms in the Iberian Electricity Market

A description of the first renewable forward market mechanisms in the Iberian Electricity Market is provided. A contract for difference mechanism is available in Spain since March 2011between the last resort suppliers and the special regime (renewables and cogeneration) settling the price differences between the equilibrium price of the forward regulated auctions for the last resort supply and the spot price of the corresponding delivery period. Regulated auctions of baseload futures of the Portuguese zone in which the Portuguese last resort supplier sells the special regime production exist since December 2011. The experience gained from renewables auctions in Latin America could be used in the Iberian Electricity market, complementing these first market mechanisms. Introduction of renewable auctions at least for the most mature technologies (i.e. wind) in Spain and Portugal providing a fair price for the renewable generation will be of utmost importance in the short term to diminish the tariff deficit caused by the massive deployment of the feed-in-tariff scheme. Liquidity in the forward markets will also increase as a result of the entry of renewable generation companies intending to maximize their profits due to gradual suppression of feed in tariff schemes.

Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.

An Empirical Analysis of the Spanish Gas Price Structure

We can say without hesitation that in energy markets a throughout data analysis is crucial when designing sophisticated models that are able to capture most of the critical market drivers. In this study we will attempt to investigate into Spanish natural gas prices structure to improve understanding of the role they play in the determination of electricity prices and decide in the future about price modelling aspects. To further understand the potential for modelling, this study will focus on the nature and characteristics of the different gas price data available. The fact that the existing gas market in Spain does not incorporate enough liquidity of trade makes it even more critical to analyze in detail available gas price data information that in the end will provide relevant information to understand how electricity prices are affected by natural gas markets. In this sense representative Spanish gas prices are typically difficult to explore given the fact that there is not a transparent gas market yet and all the gas imported in the country is negotiated and purchased by private companies at confidential terms.