Handbook of Energy Finance

The following sections are included:

• Preface
• About the Editors
• About the Contributors
• Contents

We are in a very dynamic period in our domestic and global energy markets. Market forces are at work in both the shale oil and gas revolution and the development of renewable energy, which are dramatically impacting the energy industry. Transformation of global energy markets highlights the importance of understanding various forecasting methods and technical methods to accurately forecast in such a dynamic market structure. This chapter provides a comprehensive review of the forecasting techniques for energy markets and illustrates them through a case study. This chapter can potentially be utilized by academics and industry practitioners for understanding the evolution of the energy market dynamics.

The decisions made by petroleum producers in the world oil market are both dynamic and strategic, and are thus best modeled as a dynamic game. In this chapter, we review the literature on the world oil market and discuss our research on econometric modeling of the world oil market as a dynamic game. Our research builds on the previous literature by combining three erstwhile separate dimensions of modeling the world oil market: dynamic optimization, game theory, and econometrics. Our results show that dynamic behavior and strategic interactions are important aspects of the world oil market that must be accounted for in empirical analyses of the world oil market.

This chapter offers an extension to the literature on energy prices by forecasting the return volatility of these prices using the GARCH-MIDAS approach. In addition to the realized volatility, it also evaluates the predictability of relevant macroeconomic information such as industrial growth and consumer prices (with and without energy components) in the predictive model for the return volatility of energy prices. The analyses are distinctly conducted for full sample, pre-GFC and post-GFC periods. On average, the findings support the inclusion of these macroeconomic information, particularly output growth and realized volatility, as they yield good in-sample and out-of-sample predictability results for the return volatility. However, the study finds contrasting evidence between the pre-GFC and post-GFC periods.

Short- and long-term forecasts have become increasingly important since the rise of highly competitive electricity markets. The latter point is particularly evident within recently liberalized frameworks, as it has happened, e.g. in some European countries like Germany, and it is going to happen in Italy.

Lately, forecasting of possible future loads turned out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. In fact, latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected.

During last years, the most promising results related to energy-based time series and their forecasting were obtained using machine learning algorithms, particularly with respect to the realization of ad hoc developed, deep neural networks (NNs) approaches. It is worth to mention that deep NNs architectures as, e.g. convolutional NNs and recurrent NNs have shown their power in handling complex temporal data. Nevertheless, mostly because of the large number of parameters these models are based on, such NNs are often hard to regularize, difficult to manage for out-of-sample data as well as outliers, and, moreover, they often suffer from lack of meaning.

In what follows, we propose a novel approach to energy load time series forecasting, which is based on tailored realized combination of deep learning NNs techniques and probabilistic programming. Properly merging such approaches, we can include uncertainty components both in predictions and representations. This leads to efficient regularization procedures, with priors, also allowing for a more powerful way to build complex NNs for forecasting data of interest.

In electricity markets globally, market participants and policymakers increasingly focus on integrating adjacent, yet separate market areas via cross-border trade in electricity. Based on a discussion of the institutional framework for organizing cross-border trade, this chapter analyzes how spot and futures prices for wholesale electricity are affected by different degrees of market integration. We first contrast the two main mechanisms to allocate transmission capacity between neighboring markets: explicit and implicit auctions. Subsequently, we study the impact of these allocation schemes on the empirical price dynamics of major electricity markets in Europe. Our empirical analysis thereby confirms that under market coupling, economically inefficient cross-border flows in the wrong direction can be avoided. From a policy point of view, however, we show that further market integration can be hindered by individual energy market regulation on a national level, which may be opposed to supra-national frameworks such as market coupling.

This chapter outlines the theoretical foundations and empirical applications of spatial econometrics in energy markets research. Spatial econometrics addresses spatial autocorrelation and spatial heterogeneity, two key issues of energy finance that have emerged during the low carbon transition. Indeed, with congested grids and a high penetration of geographically heterogeneous intermittent renewables, electricity markets can be more accurately modeled and forecasted by considering spatial effects. After illustrating the main spatial aspects of electricity pricing, production, and consumption, the chapter summarizes the theory of spatial econometrics and highlights its advantages over alternative estimation methods. Existing applications of spatial econometrics to electricity markets are thus reviewed (e.g. forecasting electricity demand and wholesale electricity prices and assessing the diffusion of photovoltaics). Methodological implications and ongoing issues are discussed in the final section.

The role of price in determining the level of demand for conventional forms of energy, and how this impacts the development of renewable energy markets, has been widely debated in the literature. In this chapter, we explore these issues using an econometric model of energy demand to investigate short-run dynamics and long-run cointegrating relationships, as well as evidence of price and income asymmetry, for the US residential natural gas energy market. We use this same model to produce estimates of long-run price and income elasticity of demand.

It is well established that energy demand estimation must allow for the dynamic behavior and decisions of energy consumers. Dynamics are important to understanding demand behavior because the long-run response of consumers to price changes is likely to differ from the short-run response. Specifically, long-run elasticities of demand are expected to be larger than short-run elasticities, which is unsurprising given that long-run adjustment of energy demand involves changes in long life assets such as cars and buildings. The lower short-run elasticities of demand makes sense because of the lack of available substitutes in the short-run.

In this chapter, we show the reader one approach to estimating price and income elasticity, and how to test for short-run dynamics, long-run cointegrating relationships and evidence of price and income asymmetry. The information presented here is a valuable resource for market participants, market analysts and policy makers.

Energy prices are characterized by distributions that are asymmetric and that have heavier than Gaussian tails. Yet, many researchers continue to employ statistical methods that do not explicitly account for heavy tails and skewness in energy prices. In this chapter, we explicitly account for heavy tails and skewness with an application to electricity price risk using the Generalized Pareto Distribution and the Generalized Extreme Value Distribution. Specifically, we model value-at-risk (VaR) which is a widely-used measure of the maximum potential change in value of a portfolio of financial assets with a given probability over a given time horizon. VaR has become a standard measure of market risk and a common practice is to compute VaR by assuming that changes in value of the portfolio are conditionally normally distributed. However, assets returns usually come from heavy-tailed distributions, so computing VaR under the assumption of conditional normality can be an important source of error. We illustrate in our application to electric power, that VaR estimates based on extreme value theory models — in particular the generalized Pareto distribution — are more accurate than those produced by alternative models such as normality or historical simulation.

This chapter is concerned with the statistical behavior of energy commodity prices. A particularly salient feature of many commodity markets is the unexpectedly rapid changes — or jumps — that result from the arrival of new information. Such a process would contradict the view that energy commodity prices follow a geometric Brownian motion (GBM) process (i.e. log returns are normally distributed). That is, assuming a GBM process for the data-generating mechanism would be insufficient to capture the true dynamics of energy commodity markets. The discontinuous arrival of information necessitates a stochastic process that incorporates this feature, and as such, Jump processes have become an important tool in the analysis of energy markets. While such models allow for multiple jumps in a period, the jump intensity is assumed to be constant over time — a questionable assumption given the dynamics of such energy markets. The autoregressive conditional jump intensity (ARJI) model of Chan and Maheu [2002],which allows for a time-varying jump intensity, is applied to important energy commodity markets. The results indicate the importance of incorporating time-varying jump intensities in energy markets.

We study the forward-looking information concerning nation-wide electricity demand and generation that is available to all participants in the UK market and measure its predictive power with respect to forecasting the occurrence of price spikes for horizons ranging from 2 days to 2 weeks. Considering a 14-year period (19 January 2003–31 December 2016), we find that — irrespective of the spike identification algorithm and except for the first 3 years of data — the probability of observing a spike is approximately an exponentially increasing function of the demand-to-capacity ratio. This is in contrast to an earlier study on the UK power market which reported that ca. 85% of spikes occurred when the demand-to-capacity ratio was in the range [0.908, 0.960].

In this Chapter, we concentrate on stochastic modeling and pricing of energy markets’ contracts for stochastic volatilities with delay and jumps. Our model of stochastic volatility exhibits jumps and also past-dependence: the behavior of a stock price right after a given time t not only depends on the situation at t, but also on the whole past (history) of the process S(t) up to time t. The spot price process S(t) is modeled by the Ornstein–Uhlenbeck (OU) process driven by independent increments process. The basic products in these markets are spot, futures and forward contracts and options written on these. We study forwards and swaps. A numerical example is presented for stochastic volatility with delay using the Henry Hub daily natural gas data (1997–2011).

This chapter will provide important insights into the linkages between the oil and financial markets and explore a new cross-hedging strategy to manage risk in the pipeline and energy sector market. Specifically, it will focus on examining the mean and volatility spillover effects between the US oil market, US stock market and the US Energy Pipeline Sector Index. Of particular interest is the impact of the recent liquidity crisis in the financial markets on volatility spillovers. Results demonstrate that both the US oil and stock markets have statistically significant volatility spillover effects on the US Energy Pipeline Sector. In addition, the volatility transmission from the US oil market and stock market to the US energy pipeline market increased after the 2007–2008 financial crisis. Furthermore, decreasing liquidity in the US financial market is associated with a statistically significant increase in volatility transmission between the markets. The analysis of the hedging strategy effectiveness shows that both in-sample and out-of-sample performance of the new cross-hedging strategy introduced in this chapter enhance the oil-stock hedging strategies proposed in previous studies [Basher and Sadorsky, 2016; Salisu and Oloko, 2015].

Natural gas plays an important role in Europe. During the last 10 years, the European gas market has undergone significant changes. While the share of oil-indexed gas in markets has declined, gas-on-gas competition has grown significantly. This chapter investigates gas prices at the trading hub NetConnect Germany (NCG) under various oil price scenarios in the period 2017 to 2023. This is done by performing sensitivity analyses using the worldwide gas market model WEGA. The results reveal that the NCG prices are very sensitive to the changes of oil prices throughout this period. However, NCG prices are smoothed and delayed due to the price formula of oil-indexed contracts as well as the decline of the share of oil-indexed gas.

An overview of risk measurement techniques for typical energy utilities is given. Most common calculated risk measures are explained among the often simple calculation methods used in practice. For a more sophisticated risk analysis, the various model classes proposed in the literature are reviewed. A three-factor model is explained in mathematical detail and its application to the practical modeling of energy prices is shown. This includes spot and futures prices in different time resolutions as well as the calibration of such models.

In power delivery systems, the use of dispersed generation and security control to improve network utilization requires the optimal use of system control devices. The installation of loop controller allows the distribution system to operate in a loop configuration, achieving effective management of voltage and power flow. In the investment planning process, it is important to identify the optimal location and installed capacity of the equipment such that all operational constraints are satisfied. This chapter presents a method for identifying the optimal location and capacity with the minimum installation cost. Our novel approach uses an economic model that considers the fixed costs. A slope scaling procedure is presented, and its efficiency is demonstrated using numerical experiments.

Corruption has traditionally been wildly rampant in the energy sector and its effects have proved to be extremely dramatic. For decades energy companies have perpetrated corrupt practices with impunity to obtain from the local governments the green light to construct intrusive energy infrastructures, which have the potential of causing serious damages to a country’s environment and social fabric. This illicit way of carrying out business activities has been de facto tolerated by governments for a long time. It affected particular developing nations, causing the so-called “resource curse”. Then, almost unpredictably, over the course of last two decades, significant and increasingly determined efforts have been devoted to fighting against such a criminal phenomenon. It is well established that the role that transparency plays within such a grim scenario is fundamental. This chapter will examine the intimate relationship between the lack of corporate transparency and corruption, and analyze the most important legal instruments that have been developed to encourage a transparent way of conducting business activities in the energy sector at the international, regional and domestic level. Then, will also focus on the recent Trump’s administration decision to nullify the US transparency rules for the extractive industries analyzing the scope of its potentially dramatic consequences.

This chapter mainly deals with the internalization process of accidents and pollution risk of nuclear energy through the setting of the nuclear third-party liability. The international treaties governing this liability regime favor a capped strict liability exclusively “channeled” through the operator. This responsibility increasingly concerns private companies. Indeed, since the end of the last century, the electronuclear energy has undergone full or partial privatization process. The underlying question bears on the compliance of the liberalization tendency with safety and care. Through a literature review, this chapter’s main concern is about the research on a specific industrial organization that would optimize the safety level of electronuclear parks.

This empirical work uses market capitalization of oil companies and proved reserves to investigate the role of in-ground oil stocks in risk diversification. It builds on the theoretical model of Gaudet and Khadr [1991], who use an intertemporal capital asset pricing approach to derive the stochastic version of the Hotelling rule which forms the basis for the estimations done in this chapter. The proxy used for the scarcity rent of oil is the difference between the growth rate of market capitalization of oil firms and that of oil proved reserves. In estimating the Gaudet and Khadr’s stochastic Hotelling rule, we rely on an econometric approach that combines both the Nowman [1997] method for estimating diffusion processes and the Delta method. The empirical results suggest that holding oil reserves as assets can constitute a form of insurance against adverse long-run market fluctuations.

The allocation of fixed resources among multiple dimensions is a usual challenge faced by natural resource managers. In this chapter, we propose a capital allocation framework for assigning financial resources among multiple environmental dependent risks. This approach relies on the use of the multivariate gamma distribution as a formal model for the estimation of different capital allocation principles, given its properties of dependence and positive skewness. The presented methodology is applied to the CO₂, CH₄ and N₂O emissions of the EU-28 in 2013 in order to determine the incomes we should use for financing the reduction of the risks related to each gas. Our results indicate that CO₂ is the riskiest pollutant, and then more capital is allocated to it. Finally, using simulation techniques, we study the sensitivity of our results.

The duality of cost minimization is utilized to examine the effects of climate change on US source differentiated sectoral energy demands or cost shares from 1970 to 2015 using a transcendental logarithmic cost function. The sectors include commercial, industrial, residential and transportation, while the sources include coal, electricity, natural gas, petroleum and wood & waste. The first-order conditions of cost function provide source differentiated sectoral compensated demand for energy. Second, the efficiency of source differentiated sectoral energy demand is estimated using a random panel stochastic frontier analysis model. The results suggest a differential impact of mean and variance of upside and downside precipitation and temperature risk on source differentiated energy demand. The efficiency of source differentiated sectoral energy demand ranges from 43% to 98%.

This study provides descriptive evidence on the adjustment in selected determinants of the credit margin carried out by project participants in the renewable energy sector facing the Global Financial Crisis after 2007.Adataset comprising financing parameters of 950 credit tranches granted in the renewable energy sector in the years 2000–2011 was retrieved from the database Dealogic ProjectWare. We provide descriptive evidence on the impact of the Global Financial Crisis after 2007 on the credit margin. The selection of determinants is based on the existent literature as well as on the realization of 13 case studies of project-financed transactions in the renewable energy sector. Our results show the substantial upward effect on the margins, shorter credit maturities and the need of considerably larger collaterals in order to achieve a Financial Close for projects initiated after the year 2007. A thorough examination of the methods of financing, the structure of project finance, the risk assessment methods and collaterals complement our analysis of the determinants of the credit margin. Overall, an adjustment in certain financing parameters as well as the application of risk-mitigating instruments such as, for example, project bonds, miniperm loans or cofinancing structures allow project finance to remain a proven and stable instrument for energy finance throughout the financial crisis.

This chapter studies how a well-defined environmental concerns factor influences the equity performance of US energy firms and whether or not this influence differs for renewable and non-renewable energy firms. We construct the environmental sentiment factor and seven sub-group environment-related risk factors using the Dynamic Factor Model econometric method. Then, we estimate stock abnormal returns from well-established asset pricing models to measure firm equity performance. We use an unbalanced panel of 448 US energy firms at the monthly frequency from January 2004 to October 2016. First, we find that all US energy companies show negative abnormal returns in the sampled period, that is the actual returns were lower than expected. Renewable energy firms had higher company value, since renewable energy stocks had lower abnormal returns. We also find that the abnormal returns of renewable energy stocks mainly come from environment regulation risks, while the abnormal returns of non-renewable energy stocks are more sensitive to the measure of public environmental concerns and extreme weather conditions. Consequently, we find that holding the level of environmental related risks constant, non-renewable energy stocks command relative lower abnormal returns.

In energy markets, changes in the spot price due to the influence of weather and seasonal demand conditions are partially predictable. In this work, we examine the German GASPOOL and NetConnect Germany natural gas markets using the Ederington and Salas [2008] framework that considers the predictive power of the base (futures price minus spot price) in the estimation of minimum variance hedge ratios. A considerable improvement in risk reduction and hedging effectiveness can be obtained by considering the partial predictability of changes in spot prices. We find that long hedges perform better than short hedges and there is no benefit to be gained by using more complex hedging estimations (BEKK) over the simpler OLS model. Seasonality is also found in hedging ratios.

Energy has unique features in terms of storage, transportation and consumption, while the energy price in deregulated markets exhibits its own uncertainty, volatility, spikes and seasonality characteristics. Hedging with energy options (energy derivatives) provides one way that players in the market deal with the price uncertainty and associated risk. Common financial option pricing tools, such as modified Black–Scholes and binomial and trinomial lattices, can be used to value the options. However, in atypical markets, including those where something unforeseen happens in energy production or supply, or there are changes in economic conditions or external factors, such pricing tools may not apply. This chapter shows how energy options can be valued in such atypical markets. The approach adopts a probabilistic present worth analysis based on isolating the cash flows, with the approach’s strengths being that it is intuitive to understand, straightforward to implement and requires low mathematical sophistication. Different types of energy options are explored — plain call and put options, spark spread options and swing options, and options associated with callable and putable forwards. As background to the approach, the peculiarities of the Australian context on derivatives, trading and energy prices are given, including comment on the differences between the country’s various States on practices, sources of energy including renewables and varied climate effects.

Derivatives have been used widely in the world over the last 30 years as an important risk management instrument. Although theoretical researchers suggest that derivatives usage can enhance value of a firm by alleviating costs arising from several market imperfections, the existing evidence is not quite consistent among empirical studies up to date. The purpose of this chapter, therefore, aims to examine determinants of derivatives use, a relationship between derivatives use, firm value and exposures for a sample of 881 non-financial firms in eight East Asian countries in the 2003–2013 period. The analysis is based on a novel and manually collected data.

We find that firms in countries with lower corruption have more incentive to use financial derivatives and use derivatives with greater intensity than those firms located in highly corrupt countries. Better governance induces firms to use derivatives to hedge exposure and mitigate costs. Firms in countries with weak governance use derivatives for speculating and/or selective hedging or self-management purposes. Overall, our findings provide strong evidence of the role of countries’ governance quality in driving firms’ derivatives-related behaviors. This macro-based effect on derivatives use is independent from firm-specific factors, which are frequently invoked by hedging theories.

Regarding relationship between firm value and derivatives use, using Tobin’s Q as a proxy of firm value, we find that low corruption level of home country (host country) induces the use of financial derivatives and rewards domestic firms and domestic MNCs (foreign affiliates) with higher value. Hedging behavior of domestic MNCs outperforms domestic firms and foreign affiliates in terms of firm value. Derivative usage is value-enhancing activity for domestic firms and domestic MNCs, but it does not add value for foreign affiliates.

Finally, we measure exposure to home (host) country risks, and provide novel evidence that financial hedging of domestic firms and domestic MNCs reduces exposure to home country risks by 10.91% and 14.42% per 1%increase in national derivative holdings, respectively, while affiliates of foreign MNCs fail to mitigate exposure to host country risks. Domestic MNCs with derivatives activities reduce exposures of a larger magnitude than do other firms.

Energy-based assets are showing increased susceptibility to volatility arising out of geo-political, economic, climate and technological events. Given the economic importance of energy products, their market participants need to be able to access efficient strategies to effectively manage their exposures and reduce price risk. This chapter will outline the key futures-based hedging approaches that have been developed for managing energy price risk and evaluate their effectiveness. A key element of this analysis will be the breadth of assets considered. These include Crude and Refined Oil products, Natural Gas and wholesale Electricity markets. We find significant differences in the hedging effectiveness of the different energy markets. A key finding is that, Natural Gas and particularly Electricity futures are relatively ineffective as a risk management tool when compared with other energy assets.

In July 2012, the Australian government introduced a Carbon Pricing Mechanism (CPM) to regulate national Greenhouse Gas (GHG) emissions. However, due to political controversies, there were concerns over the fate of this policy throughout its implementation. To determine the effect of carbon pricing policy on major electric utility companies, this chapter extracts an implied carbon price from the Australian electricity derivative market and models its volatility interaction with the utility index in Australian Stock Exchange (ASX).We find that Autoregressive Conditional Heteroscedasticity (ARCH) model can adequately simulate and forecast the dynamics of implied carbon price. As the cost of carbon emissions was fully transparent and passed onto the market, carbon pricing pushed up electricity prices, which subsequently enhanced expected stock returns of electric utility companies. While shocks from implied carbon price caused immediate response in the stock market, transfer of volatility from the latter to electricity derivative market was lagged. The persistent shift of volatility level further demonstrates the effect of carbon pricing and related policy uncertainty on the Australian electric utility sector. Findings from this chapter provide insight into market sentiment on carbon pricing policy and can contribute to financial risk management.

Cointegrating relationship and Granger causal analysis are used widely inempirical macroeconomics, especially in energy economics. This chapter aims to provide a survey of the development of the two widely used econometric methodologies with a practical guidance on issues that should be considered in implementation and software packages that are available for usage. An example which studies the existence of cointegrating and Granger causal relationship between energy consumption and economic growth is given to illustrate how such analysis can be carried out.

We examine the effect of European Central Bank’s (ECB’s) unconventional monetary policy measures on energy prices. Our results indicate that the non-standard ECB monetary policies during the recent financial crises had a significant and negative impact on energy prices. For example, we find that these policies accounted for approximately 10% of the price variance for the Light Crude Oil Futures Contract. Impulse Response Functions suggest that an increase in the ECB’s propensity to unconventional policy decreases energy prices for all five energy price proxies during the first two months after the monetary shock. Since these policies have now become part of a central bank’s arsenal during financial turmoil and may be used in future crises, these results shed light on their potential impact energy prices.

The purpose of the chapter is to gain a better understanding of energy market movements and dynamics by examining two competing theories: deterministic chaos theory and stochastic paradigm. We advocate that feature of both worlds may coexist in the same phenomenon. In particular, we reassess the chaotic paradigm, by considering the advances that have been made in the design of estimation tools. We illustrate all the methodologies exploited in the energy finance literature and the related results.

The following section is included:

• Index