ABSTRACT. We study whether the impact of climate change transition risk, as measured by carbon dioxide emissions, natural resources rents and renewable energy consumption, are factored into sovereign bond yield spreads. Higher transition risk results in higher default risk and hence, higher costs of debt in international capital markets. We hypothesise that that countries with higher carbon emissions and natural resources rents will incur a risk premium on sovereign borrowing costs. Moreover, countries with higher renewable energy consumption relative to total consumption will be rewarded with a discount on sovereign borrowing cost. Using a sample of data from 23 developed and 21 emerging markets from 2000-2018, we show that governments who perform poorly in managing their climate transition, may encounter increased sovereign borrowing costs, liquidity constraints, reduced capacity to effectively manage climate transition and the inability to finance economic recovery from severe climate shocks or natural disasters.

ABSTRACT. This paper studies the impact in Twitter sentiment to 'Climate Change' and 'Global Warming' for crude oil unexpected movements. Based on daily public attention in Twitter, we use the wavelet coherence methodology to analyse the correlation and the causality at dierent time-scales. Furthermore, in order to eliminate all the deterministic movements in the crude oil market, we apply a pricing model in-sample and worked with the residuals, using Moreno et al. (2019) as a benchmark model. Our ndings show correlation in high frequency at specic dates when there was an abnormal increase in the public debate, especially when it was related to the Paris Climate Deal signed on April 2016.

ABSTRACT. The paper considers different market settings for the participation to the balancing services market of small scale variable renewable energy sources connected at the distribution level to the grid. By mixing an economical and a technical approach, it evaluates the efficiency of participation to the market under two opposite settings: a commercial scheme and a technical one. In the former, the supply of the small scale variable distributed renewable energy sources are grouped on a purely commercial basis; in the latter, the DSO is responsible of the imbalances that may possibly arise in the distribution grid. By considering a reference distribution network and designing scenarios for the forecast uncertainty about supply and demand of power profiles, the impact of different market frameworks is assessed. The upward and downward balancing services provided by variable distributed energy resources and controllable units connected to the high voltage grid are both considered. Moreover, the power supply curtailments, that endogenously arise due to the violation of technical constraints of the distribution grid and the random nature of energy supply by renewables, are addressed, for each specific market model. It is shown that the social costs of balancing energy provision can be higher or lower according to the market framework and the specific scenario, depending on the relative share of the different types of distributed renewable energy sources as well as on the amount of reserved energy for balancing services and their cost.

ABSTRACT. Sustainable finance incorporates Environmental, Social, and Governance (ESG) principles into business decisions and investment strategies. Incorporating these kinds of considerations in finance has become a key element in recent years. ESG issues can have a material impact on firms performance. ESG ratings may represent a crucial element in the company’s fund raising process. The aim of the present paper is to identify how structural information about the company influence the ESG score. Using a machine learning approach we detect how balance sheet items affect the ESG score of the companies listed in the Euro Stoxx 600 index. We find that the balance sheet items represent a powerful tool to explain the ESG score, the Random Forest algorithm provides the best prediction performance compared to the standard Linear regression approach.

ABSTRACT. This article analyses the impact of Exchange-Traded Funds (ETFs) on commodity markets. We first develop an equilibrium model where three markets are linked to each other: the spot market, where the physical trading of the commodity takes place, and the associated futures and ETF markets. Arbitrage operations link spot, futures and ETF prices together. Thanks to the model, we can study the impact of the ETF, on the long run, on the variance of commodity spot and futures prices in different situations: i) when futures prices are in contango (stocks are abundant and arbitrage is easy) and when they are in backwardation (stocks are rare and arbitrage is difficult); ii) when the ETF relies on operations on the commodity itself (physical replication) and when it relies on futures transactions (synthetic replication). The model allows to define a Vector Error Correcting representation and to obtain a price discovery metrics in the case of three markets. On the basis of this measure, we perform empirical tests on 130 ETFs on precious metals, from 2004 to 2019. We show that, in our sample, the price discovery process is shared between the spot and the ETF markets, and that the latter is most of the time dominant in terms of information flows. Finally, we propose a method that allows for the identification of the days when ETFs are active on the futures and on the spot markets of the commodity. Controlling for other possible shocks we show, on a large sample of commodity ETFs, that when the ETFs are active, the price volatility of the commodity increases, with the exception of platinum. These results have implications for commodity production, storage and transformation decision making as well as for the regulation of commodity markets.

ABSTRACT. In this paper we focus on the link between micro-level behavioral patterns existing among traders and macro-observed outcomes. In the context of grains futures market we implement agent-based model of interaction and information transmission originally proposed by Kirman (1993). We construct the market sentiment indicator based on the comovement of grains futures prices and, by assuming two-type heterogeneity among traders according to their sentiment, i.e. optimists and pessimists, analyze its diffusion process. The proposed model of the endogenous market dynamics accounts for behavioral regularities and expectations of traders, including the herding tendency, their long-run beliefs as well as the market fraction. Our findings suggest that the proportion of optimistic and pessimistic traders indicates the principal phases of grains futures market. Moreover, the herding behavior has been proved to be a useful tool for predicting the incoming market turbulences due to its inverse relation with the grains futures prices. An important implication of this is the possibility to apply the propensity to herd as an early-warning indicator of grains prices spikes and inform the investors about them.

ABSTRACT. Dynamic non-linear models with multiple  occasionally binding constraints are of increasing interest. For example, non-negativity and capacity constraints on storage, and price floors, have particular relevance for water, oil, or gas storage. In this paper,  first we provide sufficient conditions for convergence and for non-convergence of iteration algorithms that have been used to solve inventory demands for such dynamic models.  Second, we discuss implications of changes in uncertainty due for example to  climate change in the case of water, or increased uncertainty about future supply in oil and gas markets.  Our results indicate that increased uncertainty in supply (or demand)  could lead to increased value of the first unit in storage but to a decreased value of the last unit in storage when storage capacity is nearly full. The latter effect is in sharp contrast to the literature which, based on Jensen's inequality, predicts increased marginal value of storage in the face of increased risk. 

ABSTRACT. Using a novel dataset of oil storage futures, we document that, contrary to common beliefs, the 1-month storage cost fluctuates over time and represents 0.5% of the spot price of oil on average. We propose a decomposition of the basis into the storage cost channel (scc) and the adjusted convenience yield channel (acyc). The scc dominates the mean of the basis and accounts for half of its variations. This result is stronger during contango than backwardation periods. Furthermore, we show that the scc is the main conduit through which the predictive power of the basis for oil spot returns arises.

ABSTRACT. Natural gas is an important energy source for heating and electricity generation. We measure supply security of European countries by considering pipeline disruptions using the expected shortfall of the effect of a network incident.

This measure is calculated for both the current network, a long-term closure of the Ukrainian corridor, the construction of the Nord Stream 2 and the combination of these. We consider both winter and summer scenarios with a different availability of gas from storage facilities. We find that Nord Stream alleviates the gas shortage problem if the Ukrainian corridor is closed, but otherwise the construction benefits Spain, Germany and the UK, while increases the risk for Central-Eastern Europe.

ABSTRACT. Recently, the development in combining point forecasts of electricity prices obtained with different length of calibration windows have provided an extremely efficient and simple tool for improving predictive accuracy. However, the proposed methods are strongly depended on expert knowledge and may not be directly transferred from one to another model or market. Hence, we consider a novel extension and propose to use Principal Component Analysis (PCA) to automate procedure of averaging over a rich pool of predictions. We apply PCA to a panel of over 650 point forecasts obtained for different calibration windows. The robustness of the approach is evaluated with three different forecasting tasks, i.e., forecasting day-ahead prices, forecasting intraday ID3 prices one day in advance and finally very short term forecasting of ID3 prices (i.e., six hours before delivery). The empirical results are compared using the Mean Absolute Error measure and Giacomini and White test for conditional predictive ability (CPA). The results indicate that PCA averaging not only yields significantly more accurate forecasts than individual predictions but also outperform other forecast averaging schemes.

ABSTRACT. This paper develops an econometric price model with fundamental impacts for intraday electricity markets of 15-minute contracts. A unique data set of intradaily updated forecasts of renewable power generation is analyzed. We use a threshold regression model to examine how 15-minute intraday trading depends on the slope of the merit order curve. Our estimation results reveal strong evidence of mean reversion in the price formation mechanism of 15-minute contracts. Additionally, prices of neighboring contracts exhibit strong explanatory power and a positive impact on prices of a given contract. We observe an asymmetric effect of renewable forecast changes on intraday prices depending on the merit-order-curve slope. In general, renewable forecasts have a higher explanatory power at noon than in the morning and evening, but price information is the main driver of 15-minute intraday trading.

ABSTRACT. We consider a market maker who trades in the German intraday electricity market. She faces the problem of pricing her buy and sell limit orders to maximize the expected cash from making markets while she penalizes holding inventories. Her market model incorporates two important features: stochastic bid-ask spread, and time dependence and clustering of event arrivals with impact on the mid price and bid-ask spread. We present an approximate solution for her markup on the best ask price and her markdown on the best bid price, which depend negatively on the inventory penalties and the bid-ask spread. The larger her marks are, the smaller is the impact of excitement on them. Backtests show that the outperformance of a naive strategy is substantial, whereas a strategy with only clustering missing is not outperformed substantially.

ABSTRACT. Australia’s National Electricity Market (NEM) is experiencing one of the world’s fastest and marked transitions toward variable renewable energy generation. This transformation poses challenges to system security and reliability and has triggered increased variability and uncertainty in electricity prices. By employing an exponential generalized autoregressive conditional heteroskedasticity (eGARCH) model, we gauge the effects of wind power generation on the dynamics of electricity prices in the NEM. We find that a 1 GWh increase in wind generation decreases daily prices up to 1.3 AUD/MWh and typically increases price volatility up to 2%. Beyond consumption and gas prices, hydro generation also contributes to an increase in electricity prices and their volatility. The cross-border interconnectors play a significant role in determining price levels and volatility dynamics. This underscores the important role of strategic provisions and investment in the connectivity within the NEM to ensure the reliable and effective delivery of renewable energy generation. Regulatory interventions, such as the carbon pricing mechanism and nationwide lockdown restrictions due to COVID-19 pandemic, also had a measurable impact on electricity price dynamics.

ABSTRACT. We consider a price-maker company which generates electricity and sells it in the spot market. The company can increase its level of installed power by irreversible installations of solar panels. In absence of the company's economic activities, the spot electricity price evolves as an Ornstein-Uhlenbeck process, and therefore it has a mean-reverting behavior. The current level of the company's installed power has a permanent impact on the electricity price and affects its mean-reversion level. The company aims at maximizing the total expected profits from selling electricity in the market, net of the total expected proportional costs of installation. This problem is modeled as a \emph{two-dimensional degenerate singular stochastic control problem} in which the installation strategy is identified as the company's control variable. We follow a guess-and-verify approach to solve the problem. We find that the optimal installation strategy is triggered by a curve which separates the waiting region, where it is not optimal to install additional panels, and the installation region, where it is. Such a curve depends on the current level of the company's installed power, and is the unique strictly increasing function which solves a first-order ordinary differential equation (ODE). Finally, our study is complemented by a numerical analysis of the dependency of the optimal installation strategy on the model's parameters.

ABSTRACT. SREC markets are a market-based system designed to incentivize solar energy generation. A regulatory body imposes a lower bound on the amount of energy each regulated firm must generate via solar means, providing them with a certificate for each MWh generated. Regulated firms seek to navigate the market to minimize the cost imposed on them, by modulating their SREC generation and trading activities. As such, the SREC market can be viewed through the lens of a large stochastic game with heterogeneous agents, where agents interact through the market price of the certificates. We study this stochastic game by solving the mean-field game (MFG) limit with sub-populations of heterogeneous agents. Our market participants optimize costs accounting for trading frictions, cost of generation, SREC penalty, and generation uncertainty. Using techniques from variational analysis, we characterize firms' optimal controls as the solution of a new class of McKean-Vlasov FBSDE and determine the equilibrium SREC price. We numerically solve the MV-FBSDEs and conclude by demonstrating how firms behave in equilibrium using simulated examples.

ABSTRACT. We find the optimal investment timing and capacity of flexible combined heat and power (CHP) units. We show that flexibility guarantees earlier investment but has an ambiguous effect in terms of optimal capacity with respect to investments in standard CHP units. A numerical exercise using data from the pulp and paper industry concludes the paper.

ABSTRACT. Deep Neural Networks are currently gaining popularity, with many seeing them as the state-of-the-art modeling and forecasting technique. Their effectiveness in the context of the day-ahead electricity price forecasting was also shown by some researchers. Here, the overview of possible approaches and the use of forecast improvement frameworks, such as Variance Stabilizing Transformations, are presented along with potential issues and results of limited testing on diverse datasets originating from the United States and Europe. Main research question regards the Artificial Neural Network depth – the study tests, whether the more computationally intensive deep structures are improving the model predictive accuracy when compared to simpler, shallow networks.

ABSTRACT. We examine the decision problem of a power producer contemplating an upgrade of its current generation capacity based on a fossil fuel technology (gas plant). The operator can choose the best of four mutually exclusive alternatives, continue operating the current technology, replace it by a more efficient fossil fuel technology (gas plant), replace it by a renewable technology (wind plant), and divest (liquidate). There are four corresponding decision regions with three boundaries. Optimal boundaries of regions are characterized through a tri-variate system of coupled Fredholm equations and valuation formulas derived. Investing in a more efficient gas plant is optimal if the gas price falls below an upper threshold and the electricity price exceeds an associated boundary. For some model parameters, the gas price must also exceed a lower threshold. Investing in a wind plant is optimal if the gas price exceeds the upper threshold and the electricity price an associated boundary. The possibility of investing in wind displaces new gas investment and postpones liquidation of the existing gas plant. We study the value of the firm and the Green Energy premium, and assess the impact of model parameters.

ABSTRACT. We propose a stochastic spanning approach to assess whether a traditional portfolio of stocks and bonds spans augmented portfolios including commodities, foreign exchange, and real estate. We show that in all seven combinations, the augmented portfolio is not spanned by the traditional one. Our results are confirmed by further parametric and non-parametric tests in an out-of-sample setting. Therefore, traditional investors can generally benefit in terms of higher Sharpe ratios from augmenting their portfolio with alternative asset classes. Further analysis demonstrates that diversification benefits can be explained by the current state of the U.S. economy and stock markets.

ABSTRACT. In this paper, we analyze the volatility patterns of crude oil and natural gas markets in the United States, and how they have changed due to economic policy uncertainty in the pre- and post-shale era. Using Markov-Switching GARCH models, we find evidence of heterogeneous volatility regimes for both commodities (i.e., high vs. low volatility). While the volatility persistence for crude oil is similar during the two sub-periods, significant changes have occurred to the natural gas market. Natural gas during the post-2010 period is characterized by short-lived tranquil market conditions for the first regime, and periods with more persistent volatility and agitated movements in the second regime. Using quantile regressions, we find that economic policy uncertainty increases the probability of agitated market conditions of both markets, although this effect has weakened during the post-shale period.

ABSTRACT. We propose a model that predicts, and we provide empirical evidence, that forward-looking uncertainty regarding the world price of agricultural commodities is boosted by export bans in top producer countries. In order to tests our model's predictions, we use options-on-futures implied return volatilities (IVols) to capture market uncertainty. We construct a novel, comprehensive dataset of major restrictions on wheat, corn, and soybean exports that were announced, adopted, or repealed between 2001 and 2020. Using this daily, country-level information, we document that IVols are significantly higher on the day and the week when a ban is first imposed and also during the whole period when the ban is in effect. After the ban is removed, IVols drop. The effects of export bans are statistically and economically significant. The results hold even when we control for global macro-economic uncertainty and risk aversion (jointly captured by the equity VIX), weather shocks, and the state of grain inventories prior to the ban. Finally, we document that exports bans have own-commodity and cross-commodity impacts on IVols. Our results demonstrate the importance of taking political risk into account when analyzing forward-looking volatility in agricultural markets.

ABSTRACT. Prominent companies have committed to procuring a percentage of their power demand from renewable sources by a future date. Long-term financial contracts with renewable generators, known as corporate power purchase agreements (CPPAs), are popular to meet such a renewable power purchase target (RPPT). By analyzing a simplified three-stage model, we show that the generation capacity contracted via a CPPA is more nuanced to structure optimally compared to traditional long-term power contracts due to the interplay between price and supply uncertainties as well as the RPPT. We subsequently propose a Markov decision process (MDP) to formalize rolling-power purchase policies used in practice, that is, the construction of dynamic CPPA portfolios to meet an RPPT. The optimal MDP policy is intractable to compute but possesses the following key properties: (i) its decisions account for stochastic prices and supply, (ii) it captures the timing flexibility to enter CPPAs, and (iii) it can sign CPPAs with different tenures. We develop forecast-based reoptimization heuristics and a novel information-relaxation based reoptimization approach that sacrifice and approximate, respectively, the first property of the MDP policy and capture the remaining properties. We perform an extensive computational study on realistic procurement instances to uncover managerial insights related to procurement costs, the control of risks arising from supply uncertainty, the relevance of CPPAs as markets evolve, and the near-optimality of rolling power purchases from our information-relaxation based procurement heuristic.

ABSTRACT. We study merchant energy production modeled as a compound switching and timing option. The resulting Markov decision process is intractable. State-of-the-art approximate dynamic programming methods applied to realistic instances of this model yield policies with large optimality gaps that are attributed to a weak upper (dual) bound on the optimal policy value. We extend pathwise optimization from stopping models to merchant energy production to investigate this issue. We apply principal component analysis and block coordinate descent in novel ways to respectively precondition and solve the ensuing ill conditioned and large scale linear program, which even a cutting-edge commercial solver is unable to handle directly. Compared to standard methods, our approach leads to substantially tighter dual bounds and smaller optimality gaps at the expense of considerably larger computational effort. Specifically, we provide numerical evidence for the near optimality of the operating policies based on least squares Monte Carlo and compute slightly better ones using our approach on a set of existing benchmark ethanol production instances. These findings suggest that both these policies are effective for the class of models we investigate. Our research has potential relevance for other commodity merchant operations settings.

ABSTRACT. We consider the problem of a storage owner who trades in a multi-settlement electricity market comprising an auction-based day-ahead market and a continuous intraday market. We show in a stylized model that a coordinated policy that reserves capacity for the intraday market is optimal and that the gap to a sequential policy increases with intraday price volatility and market liquidity. To assess the value of coordination in a realistic setting, we develop a multi-stage stochastic program for day-ahead bidding and hourly intraday trading along with a corresponding stochastic price model. We show how tight upper bounds can be obtained based on calculating optimal bi-linear penalties for a novel information relaxation scheme. To calculate lower bounds, we propose a scenario tree generation method that lends itself to deriving an implementable policy based on re-optimization. We use these methods to quantify the value of coordination by comparing our policy with a sequential policy that does not coordinate day-ahead and intraday bids. In a case study, we find that coordinated bidding is most valuable for flexible storage assets with high price impact, like pumped-hydro storage. For small assets with low price impact, like battery storage, participation in the day-ahead auction is less important and intraday trading appears to be sufficient. For less flexible assets, like large hydro reservoirs without pumps, intraday trading is hardly profitable as most profit is made in the day-ahead market. A comparison of lower and upper bounds demonstrates that our policy is near-optimal for all considered assets.

ABSTRACT. Plant-based meat is an innovative type of food that has been received with enthusiasm by consumers and investors. It is also a threat for the demand of traditional meat, and for those crops used primarily as animal feed. In 2018, 59% of global corn and 83% of global soybean output were used as inputs for meat production. In this paper we consider plausible global plant-based meat adoption scenarios for 2030 and couple them with production, efficiency and price elasticity measures from the literature. We generate simulated corn and soybean negative demand shocks and their associated price change distributions. Our average aggregate estimates for corn and soybeans, under low and high plant-based meat adoption scenarios, are -18% and -31% permanent real price decrease for grain in a 10 year horizon, relative to a baseline scenario without plant-based meat. Corn and soybean producers are likely to suffer from technological displacement.

ABSTRACT. We use non-public data regarding all trader-level futures positions, reported to the U.S. grain and oilseed derivatives market regulator (the CFTC), in order to describe the nature of market participants, the maturity structure of their holdings, and the aggregate position patterns for nine different categories of traders that we separate based on their main lines of business. We provide novel evidence about the overall extent of calendar spreading and about the contribution of commercial traders to total spreading activity.

We provide the first empirical evidence about the financialization of those market after 2001. We document the evolution of index trading—in terms of numbers of firms involved, absolute and relative magnitude of their position, and concentration of the activity.

We show that, in the aggregate, the positions of commercial dealers and hedge funds (including commodity pool operators, commodity trading advisors, managed money traders, and associated persons) are highly negatively correlated. This correlation is strikingly strong for short positions.

Finally, we provide evidence on the drivers of positions further along the futures maturity curve, as well as the true magnitude of calendar spreading. None of these patterns can be inferred from public data, as the CFTC’s Commitments of Traders Reports (COT) do not break out spreads for “traditional” commercial traders in general and commercial dealers and merchants in particular.

ABSTRACT. We investigate whether commercial market participants in agricultural futures markets (who are supposedly “hedging”) might in fact be speculating and, in the affirmative, whether they do so especially when the USDA's Risk Management Agency (RMA) price is exceeded.

We propose a theoretical model of how the RMA price offers an option to farmers and we show how farmers' incentive to take on futures positions optimally depends on whether the RMA price is exceeded. We then test the model's predictions empirically using a VAR with asymmetric effects and public (COT) data on commercial traders' aggregate positions in US corn and soybean futures markets. Finally, we outline the policy implications of our analysis in a world in which (a) position limits are being discussed for speculators but not commercial “hedgers” and (b) the crop insurance program in fact provides incentives for farmers to speculate.

ABSTRACT. This paper revisits the cost-of-carry model and proposes a decomposition of the futures basis that disentangles the seasonality risk premium from the scarcity risk premium. The contribution of this paper to the asset pricing literature is threefold. First, it brings novel insights on the fundamental relationship between the futures basis and inventory dynamics. Empirical evidence shows that the seasonality risk premium captures expectations about the inventory seasonalities while the scarcity risk premium reflects the excess supply and demand imbalances over the expected seasonal fluctuations. Second, this papers investigates the pricing of expectations within the futures basis. Results suggest that the seasonality risk premium is priced-in and that the scarcity risk premium carries all the predictive power embedded in the futures basis. The third contribution of this paper is to provide evidence that the scarcity risk premium, and ultimately the futures basis, is mostly a compensation for the unexpected increase in the risk of stock-out and that the associated return predictability finds its origin in the slow diffusion of information and underreaction to abnormal changes in inventories, above and beyond seasonal dynamics.

ABSTRACT. This paper studies the spread of Brent-WTI futures prices during 2010-2016 using a no-arbitrage term structure model with one common and two latent idiosyncratic risk factors. We document more negative risk premia for WTI than for Brent, and the differences are more pronounced at longer maturities. The expectation of future spot price dominates the risk premium in determining the term structure of Brent-WTI futures spread, especially at short maturities. The common risk premia in both markets are negative and similar, while their corresponding idiosyncratic risk premia have opposite signs. WTI risk prices and Brent common risk price are generally related to the macroeconomy and oil activity in the US; however, WTI risk prices have much smaller correlations with the crude oil production near the North Sea, implying a leading role for WTI in global oil markets. The variance decomposition indicates that the idiosyncratic factors account for a considerable part at longer forecast horizons in both markets.

ABSTRACT. The paper uses linear and nonlinear predictive models to study the linkage between a set of 128 macroeconomic and financial predictors and subsequent commodity futures returns. The linear models use shrinkage methods based on naive averaging and principal components. The nonlinear models use feedforward deep neural networks either as stand-alone (DNN) or in conjunction with LSTM, a recurrent long short-term memory network. Out of the four specifications considered, the LSTM-DNN architecture is the most successful at transforming the 128 predictive variables into profitable investment strategies. The risk premium then modelled is unrelated to, and exceeds, those earned on previously-published characteristic-sorted portfolios. Our analysis is robust to the presence of transaction costs and illiquidity.

ABSTRACT. This paper explores the consequences of considering renewable energy power producers besides conventional (fossil-fuel and nuclear) producers and retailers in the electricity markets. We find that the higher the kurtosis of spot prices, the lower the day-ahead premium. The sign of the impact of the variance and the skewness on the day-ahead premium is not necessarily negative and positive, respectively, but depends on the demand level. Larger renewable energy production reduces the day-ahead premium. But a higher variation of renewable energy production increases the day-ahead premium. As increases in the average production level of renewable energy also imply higher variation around this average, the ultimate effect of a higher share of renewable energy on the day-ahead premium depends on the relative size of both effects. We test the model's implications in the Spanish electricity day-ahead and spot markets during 2017, and the empirical results are largely consistent with the theoretical predictions.

ABSTRACT. Given the popularity of renewable portfolio standards and other policies that provide additional per-MWh revenue for non-polluting generators, we assess under what conditions such generation subsidies increase efficiency of wholesale markets and what effect they have on equilibrium generation mix and prices. We derive an analytical model of wholesale energy and capacity markets which shows that, as long as the subsidized resource is not the marginal resource in the peak period, generation subsidies do not affect the equilibrium price in capacity markets. We also demonstrate that while homogeneous subsidies cannot achieve first-best outcomes, even when combined with energy consumption taxes, there exists a range of subsidy rates that improve the economic efficiency of wholesale markets when externalities are present. Finally, using our model and simulations based on data from one of the U.S. wholesale electricity markets, we evaluate the capacity market reforms that are being undertaken in the U.S. in response to the subsidies.

ABSTRACT. Renewable generation has increased exceptionally its weight in electricity markets, and its relevance is due to increase with the introduction of climate policies in Europe. The merit-order effect ranks first on the direct impacts of renewables on electricity markets. However, in order to analyze its impact it is necessary to control for the different forces driving electricity prices. As a result, the analysis through an structural model of demand and supply is interesting to capture price drivers and measure correctly the merit-order effect. The objective of this paper is to introduce this framework on the Spanish day-ahead market, using weekly data for the period 2013-2019. The empirical analysis was carried applying ARDL models to the equations, with the addition of GARCH models to control for the innate autoregressive behavior of volality in electricity markets. In line with previous literature, demand is elastic to economic growth and shows a significant level of substitution between electricity and natural gas. For supply, capacity factors, inputs prices and external balance were shown to be significant to explain equilibrium quantities. Besides, the estimation on the merit-order effect is aligned with previous literature. Estimations report that a 10\% increase in the average quantity generated by special regime technologies (wind, solar and cogeneration) is associated with a reduction of 5\% in electricity prices (around 2.35 €/MWh of the average price for the analyzed period).

ABSTRACT. We investigate the impacts of financial investors in commodity markets using intraday trade- and-quote data for commodity futures. We find strong evidence of order flows and price impacts in agricultural futures markets associated with changes in the positions of index traders reported by the CFTC. These order flows and price impacts are consistent with the magnitudes of the index flows, and are concentrated in the minutes just prior to daily futures settlement, when the price impact of order flow is generally lowest. While we confirm the positive returns around the issuance of commodity-linked notes documented by Henderson, Pearson, and Wang (2015), we find that these notes are an order of magnitude too small for the price impacts of hedging trades to explain these returns. We provide evidence that the positive returns are more consistent with CLN issuance responding to commodity prices rather than vice-versa.

ABSTRACT. [For the Special Session on "Sustainable Energy Finance".]

This paper derives the optimal investment policy of an electricity producer during a pandemic. We consider three problems: 1) investing in a gas-fired plant, 2) investing in a wind plant, and 3) investing in the best of a gas plant and a wind plant. Optimal investment boundaries are characterized and valuation formulas derived. For single technology projects, a pandemic postpones wind investment, but can accelerate gas investment when the relative price of gas decreases. For choices between the two technologies, a substitution effect can reinforce the single technology effects, accelerating gas investment under certain conditions. The paper examines the impact of pandemic parameters, economic parameters and policy parameters on the investment boundaries, the values of projects and the premium for Green energy.

ABSTRACT. In this paper we propose a mechanism for the allocation of pipeline capacities, assuming that the participants bidding for capacities do have subjective evaluation of various network routes. The proposed mechanism is based on the concept of bidding for route-quantity pairs. Each participant defines a limited number of routes and places multiple bids, corresponding to various quantities, on each of these routes. The proposed mechanism assigns a convex combination of the submitted bids to each participant, thus its called convex combinatorial auction. The capacity payments in the proposed model are determined according to the Vickrey-Clarke-Groves principle. We compare the efficiency of the proposed algorithm with a simplified model of the method currently used for pipeline capacity allocation in the EU (simultaneous ascending clock auction of pipeline capacities) via simulation, according to various measures, such as resulting utility of players, utilization of network capacities, total income of the auctioneer and fairness.

ABSTRACT. Microgrids are local electricity grids that can operate in islanded mode. They are an important ingredient in the transition to the decentralized smart grid and typically combine renewables-based generation with storage facilities and back-up generator or public grid connection. Efficient management of microgrids is an emergent topic that brings to the fore the intrinsic stochasticity in balancing power supply from distributed energy resources and local load. I will provide an overview of the modeling frameworks for microgrid management within the paradigm of stochastic control. Among sub-problems, I will describe optimal battery storage management, probabilistic constraints on probability of loss-of-load, and mean-field-control setups. In aggregate, microgrid operations offer a challenging new testbed that is motivating development of models and numeric methodologies, especially linking to machine learning techniques.