ABSTRACT. This work provides an understanding of the dynamics of trading in the intraday power market in Germany and Austria. We highlight differences between the intraday power market and intrady stock markets, and discuss how this affects intraday algorithm trading strategies. We use a data set comprising all orders placed in Q2/2015 and Q2/2016 with Germany as delivery area (i.e. high-frequency data) to analyze order book characteristics. In particular we analyze: volume distributions on the first level of the order book and beyond, average execution costs, average bid-ask spreads, in addition to order flow characteristics such as average limit and market order arrival rates. Preliminary findings are: (i) For Q2/2015, the distributions of first-level volumes show some concentration at small values, followed by rather stable frequencies up to values between 20 to 25~MW and a decrease thereafter. Furthermore, the impact of market participants discretizing their volumes to multiples of 5~MW as well as making use of iceberg orders with a minimum peak quantity of 25~MW may be observed. For Q2/2016, the frequencies show an increase first, followed by a more or less steady decrease. %A dependency of the distribution of first-level volumes on the remaining time to gate closure is suspected. (ii) The average volumes of contracts supplied on the levels beyond the first level in the book decrease in the distance of the prices associated with these levels from the prices on the first level. (iii) The average differences between the prices on two consecutive levels increase with increasing distances of the prices associated with these levels from the prices on the first level. (iv) Average bid-ask spreads decrease with decreasing time-to-maturity of the contracts until about 15 minutes before gate closure and then increase again. Average bid-ask spreads for Q2/2016 are significantly smaller than for Q2/2015. (v) Average market order arrival rates of market orders are very low after market opening and increase (exponentially) in the hours nearer to gate closure. While the traded volume in Q2/2016 is similar to the traded volume in Q2/2015, the average market order arrival rates have increased substantially.

ABSTRACT. The paper documents the evolution of block trading in the crude oil options market, following the reduction of the minimum permissible block size threshold in October 2012. Block trading, that was sparse prior to the change, currently accounts for over 30% of the trading volume in WTI crude oil options, a large position of which involves option trading strategies. We compare the execution costs of large/block orders across trading venues before and after the October 2012 regulatory change, in order to gain a better understanding of the factors behind the recent increase in block trading. We find that the upstairs market attracts orders with lower information content. However, compared to large trades in the downstairs market, block trades face higher total execution costs which potentially serve as compensation for the high search and negotiation costs surrounding the execution of option trading strategies.

ABSTRACT. The weekly release of the U.S. inventory level by the DOE-EIA is known as the market mover in the U.S. oil futures market and to be a significant piece of information for all world oil markets in which the WTI is a price benchmark. We uncover suspicious trading patterns in the WTI futures markets in days when the inventory level is released that are higher than economists’ forecasts: there are significantly more orders initiated by buyers in the two hours preceding the official release of the inventory level. We also show a clear drop in the average price of -0.25% ahead of the news release. This finding is consistent with informed trading. We also provide evidence of an asymmetric response of the oil price to the news, and highlight an over-reaction that is partly compensated in the hours following the announcement.

ABSTRACT. In energy markets forward contracts can be of two types: in our terminology, forwards and swaps. Who sells a swap contract commits to deliver over a certain period, for instance, power, while by forward we mean the classical financial agreement settled on a maturity date. Our purpose is to design a Heath-Jarrow-Morton framework for an additive, mean-reverting, multidimensional market consisting of forward contracts of any maturity date or delivery period. The main assumption is that forward prices can be represented as affine functions of a universal source of randomness. In a Brownian setting, we are able to completely characterize the models which do not allow for arbitrage opportunities. We study the possibility of introducing more general Lévy components either driving the dynamics of prices or in the context of a stochastic volatility model.

ABSTRACT. In contrast to geometric models, additive models in energy markets, in particular in markets where forward contracts are delivered during a period like electricity and natural gas, allows easily the computation of forward prices in closed form. Moreover they naturally allow the presence of negative prices, which start to appear more and more frequently in electric markets.

In this paper we present an additive multicommodity model, based on the observed prices of forward contracts based on the mean on a period, which are the most liquid instruments in natural gas and electricity markets. The model is capable to exibit mean-reversion and a term structure for volatility of forward prices, which are empirically observed in energy markets. We also present a way to estimate the model parameters, based on quadratic variation/covariation.

ABSTRACT. This paper analyzes the relation between commodity spot, forward prices, and convenience yield under incomplete market. We model a maximization prot model of a representative rm that use input commodities in order to produce output commodities while storing spot commodities and trading forward to hedge its risk. Here we assume the firm have constraints for holding spot commodity. Since the model is stemmed on incomplete market, we assume that the rm maximize its prot under a present value vector. Solving the maximization problem, we can relate commodity spot, forward price, and the shadow price which is interpreted as the convenience yield.

ABSTRACT. We estimate the model parameters of a structural electricity model based on historical fuels' and power futures data as well as bid and offer data from EPEX Spot. We proof the estimators to be asymptotically normally distributed. The estimation is done in three steps: (i) estimation of fuels' dynamic, (ii) estimation of the electricity bid stack and (iii) estimation of the (implied) demand dynamic. The bid stack parameters are estimated with the help of a normal mixture density network. We use the Wasserstein-distance to come back to bid stack parameters. We reveal that even for only two marginal fuels gas and coal, we get a realistic fit of the bid stack for the German electricity market. Furthermore, the (implied) demand dynamic mirrors the well-known Samuelson effect. The effect is transferred to the demand dynamic by calibrating the demand volatility against power futures data. Based on the asymptotically normal estimators our aim is to quantify parameter risk of a structural electricity model. We measure parameter risk in terms of an average value-at-risk for a derivative's present value distribution. The distribution is implied by uncertainty in the estimators. We apply the algorithm to the pricing of a coal virtual power plant. We find that parameter risk is higher for high power prices due to the steepness of the bid stack in that part of the bid stack.

ABSTRACT. In this work we consider the valuation of swing options with the possibility of incorporating spikes in the underlying electricity price. This kind of contracts are modelled as path dependent options with multiple exercise rights. From the mathematical point of view the valuation is posed in terms of a sequence of free boundary problems where two exercise rights are separated by a time period. Due to the presence of jumps, the complementarity problems are associated with a partial-integro differential operators.

In order to solve the pricing problem, we propose appropriate numerical methods based on a Crank-Nicolson semi-Lagrangian method for the time discretization of the differential part jointly with the explicit treatment of the integral term by using the Adams-Bashforth scheme and combined with biquadratic Lagrange finite elements for space discretization. Additionally, we use an augmented Lagrangian active set method to cope with the early exercise feature when it appears. Moreover, we employ appropriate artificial boundary conditions to treat the bounded domain after truncation. Finally, we present some numerical results in order to illustrate the proper behaviour of the numerical schemes.

ABSTRACT. By means of Malliavin Calculus we construct an optimal linear strike convention for exchange options under stochastic volatility models.This convention allows us to minimize the di¤erence between the model and implied correlations between the two underlying assets in the spread. Moreover, we show that this optimal convention does not depend on the specific stochastic volatility model. Numerical examples are given.

ABSTRACT. We investigate the reliability and the consistency of liquidity provision by fast liquidity providers (“FLPs”) in periods of market stress. We draw on a comprehensive, non-public, account-level intraday dataset of trading activity in crude oil futures (the world’s largest commodity market), where liquidity provision has always been entirely voluntary. That market was transformed in 2006 by the onset of electronic trading, with liquidity provision since dominated by machines trading at ultra-high speeds. We ask if these FLPs significantly reduce their participation or liquidity provision amid liquidity shocks or in information-rich periods (characterized by persistently high volatility or elevated information asymmetry). Using market stress episodes from January 2006 to June 2009, we compare FLPs’ trading with the contemporaneous behaviors of the (now “Dead”) Locals in the trading pits and of the (“Slow”) e-Locals in the electronic market. Compared to slower liquidity providers, we find that FLPs withdraw more (and provide less liquidity to customers) during high-volatility and other information-rich periods but are less sensitive to liquidity shocks. In contrast, FLP-to-customer spreads are not substantially affected by high volatility per se but go up significantly in response to high informational asymmetries.

ABSTRACT. In the data, an increase in oil price volatility dampens current and future output, investment, employment, and consumption, controlling for market volatility and other business cycle variables. High oil uncertainty negatively affects equity prices, with a much more pronounced impact in durable industries. We develop a two-sector production model to explain the novel evidence in the data. In the model, oil is an essential input for production and can be stored. At times of high oil volatility, oil suppliers increase oil inventories and curb oil supply to the market. As a result, investment, production, and consumption go down, and oil inventories go up. These mechanisms are directly supported in the data.

ABSTRACT. The "financialization" of commodity markets is widely considered to have (a) resulted in increases in correlations across commodities, and (b) distorted commodity prices. Adapting a canonical model of commodity hedging and speculation, I show that various forms of financialization, including index trading improve welfare by improving the allocation of risk. Moreover, financialization (including index trading) can cause correlations to rise or fall. Furthermore, since commodity trading by financial firms is endogenous, and responds to changes in both the demand and supply for risk bearing services, reduced form predictive regressions are of limited utility in evaluating the causal impact of index trading and financialization in general on commodity prices and risk premia.

ABSTRACT. It is well known that gas price follows a mean reverting dynamics with jumps. It is less known that jumps can happen when the demand of gas is high and storage levels are low, which usually occurs during the winter period when the consumption for heating purposes, especially in the residential sector, necessarily increases. It is then reasonable to assume that gas price is influenced by the atmospheric temperature. [Mu, 2007] was the first to study the dependence between the Henry Hub futures price and the temperature measured in the United States. [Stoll & Wiebauer, 2010] performed a somehow similar analysis on the price quoted in the Title Transfer Facility trading hub and the temperature measured in Germany, and found that temperature has an impact on the value of a gas storage contract/facility. In this work we propose to model the gas price as a mean reverting jump-diffusion process with temperature dependent stochastic jump intensity, and use it to evaluate gas storage contracts/facilities in the US market. As first proposed by [Boogert & De Jong, 2008], we compute the no arbitrage value of the contract by a real options approach. The ensuing discrete time stochastic optimal control problem is solved by dynamic programming. We compute the continuation value of the dynamic programming algorithm by Fast Fourier Transform (FFT), generalizing the approach proposed by [Kiely et al., 2015]. A no arbitrage approach requires the specification of a no arbitrage pricing measure and a calibration procedure that make the model consistent with the observed market prices of liquid derivatives contracts at the valuation date. However, the low liquidity of the options market can make the classical calibration procedure of implied volatilities unreliable in practice. For this reason we first specify gas and temperature dynamics under the real world measure, and estimate them using the relative time series. Then we derive the no arbitrage dynamics for the gas price by a suitable change of measure, which introduces in the dynamics new parameters that can account for market prices of risk implicits in futures and option prices. Finally, we present some numerical results about the calibration of the model and the valuation of a gas storage contract.

ABSTRACT. We present a joint calibration-estimation risk measurement methodology, extending recent literature, which incorporates both market calibration and historical estimation risk within a meaningful distributional assessment of parameter risk. Extending the emerging literature on model risk issues in energy markets, we apply our technique to the problem of natural gas storage valuation, using a flexible multifactor Mean Reverting Variance Gamma model specification that is both forward curve consistent and calibrated to market traded options. Realistic models of the natural gas forward curve cannot be calibrated to benchmark instruments alone due to the lack of a liquid time-spread options market and thus the correlation structure is typically estimated from historical data. We additionally devise an accessible model selection technique based on our distributional assessment of parameter risk. For a basic one-year 20in/20out storage contract, we show that the parameter risk of our two-factor Mean Reverting Variance Gamma model is higher relative to single-factor Mean Reverting Variance Gamma and Mean Reverting Jump-Diffusion benchmarks, with very different distributional characteristics. Formally pricing the parameter risk, shows the model based bid-ask spread to be over five times that of the benchmarks. The greater flexibility of the two-factor Mean Reverting Variance Gamma model in capturing more extrinsic value therefore comes at the cost of greater uncertainty. Our novel model selection technique shows, however, this increased uncertainty to be bearable, concluding that the two-factor Mean Reverting Variance Gamma model is an acceptable choice over its one-factor counterpart.

ABSTRACT. The use of energy storage in electric grids is increasing and, with it, the importance of operational optimisation from the twin viewpoints of cost and system stability. In this paper we assess the real option value of balancing reserve provided by an energy-limited storage unit. The contractual arrangement is a series of American-style call options in an energy imbalance market (EIM), physically covered and delivered by the store, and purchased by the power system operator. We take the EIM price as a general regular one-dimensional diffusion and impose natural economic conditions on the option parameters. In this framework we derive an optimal operational strategy of the storage operator in two cases: (a) when the operator maximises the total expected discounted future cash flows, (b) when he maximises the average profit per day. To this end we solve, iteratively, a pair of optimal stopping problems: when to purchase energy to load the store (to provide physical cover for the option) and when to sell the option to the system operator. Assuming that the store is used only for provision of balancing services, i.e., options are offered back-to-back, we give necessary and sufficient conditions for the finiteness and positivity of the value function as well as its fixed point characterisation. The latter allows us to design a straightforward procedure for the numerical evaluation of the optimal operational strategy (stopping regions, i.e., sets of prices when power should be purchased) and the value function. Interestingly, our developments sidestep differential characterisation of the value function favouring instead a purely probabilistic approach. Theoretical results are illustrated with an operational and economic analysis using data from the German Amprion EIM.

ABSTRACT. The aim of this paper is to examine the role of gold as a safe haven for the stock market. We compare its ability as a safe haven to crude oil and SPDR sectors. We model the tails and their dependencies using two newly introduced methods based on both options and return data. We nd that gold acts as a hedge and a safe haven but only during the European debt crisis. Crude oil serves as a safe haven only during the nancial crisis.

ABSTRACT. The ratio of gold to platinum prices (GP) reveals persistent variation in risk and proxies for an important economic state variable. GP predicts future stock returns in the time-series, explains stock return variation in the cross-section, and is significantly correlated with option-implied tail risk measures. Contrary to conventional wisdom, gold prices fall in recessions, albeit by less than platinum prices. A model featuring recursive preferences, time-varying tail risk, and preference shocks for gold and platinum can account for asset pricing dynamics of equity, gold, and platinum, rationalize the return predictability, and explain why gold prices fall in bad times.

ABSTRACT. As the largest market for commodity exchange‐traded products (ETP), gold is reputed a safe haven for investors and a hedge against dollar depreciation. While these properties suggest being important drivers for investors’ demand for gold ETPs, results show that the changes in gold ETP holdings can be explained by past gold returns and stock index returns from several developed countries (USA, UK, Germany, France and Japan). However, stock market volatility, extreme stock market developments and currency indexes of these countries do not seem to be very important drivers for investors’ demand. Additionally, the safe haven property of gold and the relationship with currency depreciation vary widely among the different countries.

ABSTRACT. Security Design, Nontradable Risk, and Market Segmentation

ABSTRACT. Many corporations in the developed world face price and quantity uncertainty in commodities for which there are traded assets -- futures and options contracts -- which permit these corporations to hedge the risk to which they are exposed. Finance research has demonstrated frictions in capital markets are equivalent to risk-averse decision-making: Accordingly, decision-makers may make optimal decisions based on a trade-off between risk and return.

The optimal hedging program hinges on the amount of exposure the corporation wishes to hedge: As risk aversion increases the company's optimal hedge proceeds from no-hedging, to acquiring options, then to replacing options with futures contracts. This paper provides an empirical test of whether corporations' hedge ratios are consistent with such optimal management of risk exposure using gold futures and/or options.

ABSTRACT. This paper investigates corporate hedging under regret aversion. Regret-averse firms try to avoid deviations of their hedging policy from the ex post best policy, an intuitive consideration if one has to justify one's decisions afterward. The study presents a model of a firm that faces uncertain prices and seeks to hedge both profit risk and regret risk with derivatives. It characterizes optimal hedge positions and shows that regret aversion leads to stronger incentives to hedge downside price risk than standard expected utility theory. In the profit region of the price distribution, however, regret aversion reduces the hedging of price risk to avoid large regret in the case of increasing prices. The results show that regret aversion has a strong effect on the choice of the hedging instrument and provides a preference-based explanation for the use of options in corporate risk management.

ABSTRACT. In day-ahead electricity price forecasting the daily and weekly seasonalities are always taken into account, but the long-term seasonal component was believed to add unnecessary complexity and in most studies ignored. The recent introduction of the Seasonal Component AutoRegressive (SCAR) modeling framework has changed this viewpoint. However, the latter is based on linear models estimated using Ordinary Least Squares. Here we go one step further and show that considering non-linear neural network-type models with the same inputs as the corresponding SCAR model can lead to a yet better performance. Like in the SCAR framework, also for Seasonal Component Artificial Neural Network (SCANN) models not every decomposition yields an improvement over a model calibrated to non-deseasonalized prices. However, for adequately conducted seasonal decompositions the improvements are more significant than for linear models, especially when ensemble averaging is performed.

ABSTRACT. In this paper a class of generative probabilistic models based on multivariate gaussian mixtures is proposed to study electricity Day Ahead Market hourly prices. These models, well known in the machine learning community, are shown to be models already used in the econometric community, being essentially discrete-time stochastic regime switching autoregressions. Being generative and not discriminative, these nonlinear autoregressions allow for self-organizing data in clusters, their parameters having a simple and clear explanation in terms of market phenomenology, like spikes and nigh/day seasonality. The paper is divided in two Parts. In the first Part, Vector Gaussian Mixture Models (VGMMs) are used to estimate and model daily sequences of $24$ prices, preserving and encoding very well intraday dynamic structure like autocorrelation up to $24$ lags, but not being able to handle interday structure. In the second Part, these regime switching autoregressions are dynamically extended to incorporate and expand features typical of hidden Markov models, thus becoming Vector Gaussian Hidden Markov Models (VGHMMs). VGHMMs are shown to be able to model intraday phenomenology like autocorrelation beyond $24$ lags, and to possess enough structure to exploit hierarchical clustering of data and carrying it forward in their dynamics, their parameters still preserving a simple and clear interpretation in terms of market phenomenology. VGHMMs are thus shown to be able to handle at once cross-sectional, longitudinal and hierarchical complexity of DAM market price series.

ABSTRACT. Conducting an extensive empirical study on short-term electricity price forecasting (EPF), involving state-of-the-art parsimonious expert models as benchmarks, datasets from 12 power markets and 32 multi-parameter regression models estimated via the lasso, we show that using the latter shrinkage approach can bring statistically significant accuracy gains compared to commonly-used EPF models. We also address the long-standing question on the optimal model structure for EPF. We provide evidence that despite a minor edge in predictive performance overall, the multivariate modeling approach does not uniformly outperform univariate models across all datasets, seasons of the year or hours of the day, and at times is outperformed by the latter. This may be an indication that combining advanced structures or the corresponding forecasts from both modeling classes can bring a further improvement in forecasting accuracy. Finally, we also analyze variable selection for the best performing multivariate and univariate high-dimensional lasso-type models, thus provide guidelines to structuring better performing forecasting model designs.

ABSTRACT. We show that listed shipping firms hold more cash than similar firms in other asset-heavy industries. The higher cash holdings in the shipping industry are not attributable to firm- or country-level characteristics, but rather to the higher marginal value of cash. Shipping firms value an additional dollar of cash higher than matched manufacturing firms, independent of their financial constraints status, but depending on their cultural background and their cyclicality of growth opportunities. In particular, less cyclical shipping firms have a higher marginal value of cash, and this valuation effect is most pronounced in bad times of the business cycle when external capital supply is scarce.

ABSTRACT. This article examines the formation of Forward Freight Agreement (FFA) rates in the dry bulk shipping industry. We illustrate that the bulk of volatility in the FFA basis can be attributed to expectations about future physical market conditions rather than expectations about future risk premia, as is commonly suggested in the literature. In addition, there appears to be predictability of risk premia using three price-based signals; namely, lagged spot growth, lagged risk premia, and the FFA basis. We further contribute to the literature by developing a behavioural asset pricing framework that explains the predictability of future risk premia by the price-based signals. The proposed heterogeneous-agent model incorporates three types of agents; ship-owners, charterers, and speculators. The distinct feature of the proposed framework is that, apart from having ‒ as is standard in the literature ‒ different objective functions, agents might also differ in the way they form expectations about future market conditions. Specifically, we depart from the rational expectations benchmark incorporating a behavioural bias known as “the law of small numbers” ‒ or, equivalently, “reversion to the mean” or “gambler’s fallacy” ‒ on the part of speculators. To the best of our knowledge, the FFA market had never been examined from the perspective of a structural, behavioural economic model before. In addition, we contribute to the generic commodity finance literature by incorporating explicitly the behavioural dimension in the formation of derivative contracts rates.

ABSTRACT. We empirically investigate whether speculative activity in Chinese futures markets for agricultural commodities destabilizes futures returns. To capture speculative activity a speculation and a hedging ratio is used. Applying GARCH models we first analyse the influence of both ratios on the conditional volatility of three heavily traded Chinese futures contracts, namely soybeans, soybean meal, and sugar. Additionally, VAR models in conjunction with Granger causality tests, impulse-response analyses and variance decompositions are used to get insight into the lead-lag relationship between speculative activity and returns volatility.

ABSTRACT. We examine the effect of financial investments on the futures market of seventeen agriculture commodities during 2006-16. Introducing a financialization index we show that financial investors significantly affect the variation in the co-movement of these commodities. We find even stronger evidence that a higher relative share of commodity index traders increases the correlation between individual commodity prices. Our analysis indicates that in order to avoid financial interests affecting agriculture markets the relative share of commodity index traders’ long open interest should not be significantly higher than 28%. Changes in the intensity of financial speculation thus have a non-negligible influence on agriculture commodity markets.

ABSTRACT. In this paper the futures prices of corn, wheat and soybeans traded on the Chicago Mercantile Exchange are examined to understand the second moment or variance and covariance of returns of these prices. The second moment is a fundamental part of the pricing of derivatives contracts and is crucial for risk allocation, i.e. hedging and investment. The term structure of futures prices is the key source of price information for storable commodities. Using half-hourly price data over a period from 1982 to 2016, the volatilities of the near-dated futures price and far-dated futures prices as well as their correlations are computed for each of the three agricultural commodities. Our initial results indicate that the level of the volatilities of the prices and the convenience yield are very low compared to what one would expect using a continuous-time pricing model or a simple sample moment. A preliminary estimate shows that these volatilities revert around a few percentage points: one would expect a reversion around fifteen or twenty percentage points or greater. Interestingly, Haugom, Wesgtgaard, Solibakke and Lien (2011) show a similar discrepancy for the Nordpool electricity market. Furthermore, all the volatilities increase during the period preceding the 2007/8 financial crisis but rapidly come back to their pre-crisis level: a change of pattern starting from the 2003/2004 period. Hence we find little evidence of the so-called ‘financialisation’ of agriculture commodities from the dynamics of the volatilities. Our empirical evidence contradicts the theoretical findings of Baker (2016) as far as the energy market is concerned. Third, both volatilities and correlations look to undergo infrequent but very large jumps: the variations can easily be multiplied by a factor of three to six. Interestingly a cursory review of the underlying times series of the three futures prices does not exhibit such spikes. To the best of our knowledge, these spikes in variances and correlations are not documented in prior literature for agricultural commodities. These findings have important consequences in terms of risk modelling and price risk management and will be of interest to commodity market traders, growers and regulators. The next version of this manuscript will extract the jump component of variation and covariation to independently study this jump component and the volatilities/correlations of these commodities.

ABSTRACT. As in most commodities markets, deregulated electricity markets allow the participation of purely financial traders to enhance informational and productive efficiency. The presence of financial players is expected, among other things, to help eliminate predictable pricing gaps between forward and spot prices, which may arise in the presence of market power and are linked to productive inefficiency. However, we find that the impact of financial players on reducing pricing gaps has been limited, even using credibly exogenous variation in financial activity to address potential endogeneity. A forward premium persists. We show that financial traders effect on the premium was limited by two barriers. First, arbitrageurs do not have unlimited access to capital. Trading was reduced during the financial crisis, when capital availability was restricted. The second is regulation, as high transaction costs imposed by the regulator restricted arbitrage. Moreover, during this period we observe that some financial players appear to be betting in exactly the opposite direction of the pricing gap, sustaining large losses while doing so. We find evidence consistent with participants using forward market bids to affect congestion and thus increase the value of their Financial Transmission Rights (FTR), i.e. these financial players incur losses with one financial instrument to make larger profits with another, introducing artificial congestion to the system.

ABSTRACT. We formulate the merchant trading of energy in a network of storage and transport assets as a Markov decision process with uncertain energy prices, generalizing known models. Because of the intractability of our model, we develop heuristics and both lower and dual (upper) bounds on the optimal policy value estimated within Monte Carlo simulation. We achieve tractability using linear optimization, extending near optimal approximate dynamic programming techniques for the case of a single storage asset, versions of two of which are commercially available. We propose (i) a generalization of a deterministic reoptimization heuristic, (ii) an iterative version of the least squares Monte Carlo approach, and (iii) a perfect information dual bound. We apply our methods to a set of realistic natural gas instances. The combination of our reoptimization heuristic and dual bound emerges as a practical approach to nearly optimally solve our model. Our iterative least squares Monte Carlo heuristic is also near optimal. Compared to our other heuristic, it exhibits slightly larger optimality gaps and requires some tuning, but is faster to execute in some cases. Our methods could enhance single storage asset software and have potential relevance beyond our specific application.

ABSTRACT. We examine how physical constraints limit financial arbitrage by studying the effect of crude oil storage constraints on arbitrage activity. We document both temporary and long-term violations of the no-arbitrage conditions due to storage capacity constraints at the Cushing hub where U.S. crude oil futures are priced. When crude oil storage levels are well below capacity, temporary violations of the upper no-arbitrage bound occur but are usually eliminated within a few days. However, as the amount of oil in storage approaches capacity, price adjustment slows and violations of the upper no-arbitrage limit persist. We find evidence of temporary, but not long-term, violations of the lower no-arbitrage futures pricing bound, with the latter being consistent with our observation that there were no periods of stock-out conditions during our 2004-2015 sample period. We also find that arbitrage was limited by financial constraints over our sample period. However, the strong evidence we document in support of physical constraints impeding arbitrage remains robust when we control for the effect of financial constraints. Our results are also robust to the use of different measures of physical constraints. Our findings highlight the importance of accounting for physical arbitrage limits in pricing commodity futures and contribute to the Theory of Storage literature by highlighting the price effects when inventories approach storage capacity limits.

ABSTRACT. This paper features an analysis of the cointegration relationships among agricultural commodity, ethanol and Cushing crude oil spot and futures prices. The use of grains for the creation of bio-fuels has sparked fears that these demands are inflating food prices. We analyse approximately 10 years of daily spot and futures prices for corn, wheat, sugar ethanol and oil prices from Datastream for the period 19 July 2006 to 2 July 2015. The analysis, featuring EngleGranger pairwise cointegration and Markov-switching VECM and Impulse Response Analysis, cofirms that these markets have signicant linkages which vary according to whether they are in low or high volatility regimes.

ABSTRACT. Commodity futures prices respond to changes in expected physical supply. Demand for futures positions by firms who trade in the physical commodity is accommodated by speculators. But how do hedgers and speculators in a commodity futures market adjust their positions in response to changes in expected physical supply? What implications do their position changes have for equilibrium prices? A simple model of net short hedging predicts that a positive supply shock leads to lower prices and more selling by hedgers. However, recent literature finds negative correlation between prices and hedger positions as measured by CFTC Commitment of Traders data. To understand the joint determination of prices and positions in response to changes in physical supply, we construct a novel measure of variation in physical corn supply - accumulated rain during the growing season - and explicitly estimate its effect on corn futures prices and trader positions. We find that hedgers (who are generally net short) increase short positions when physical supply contracts due to a lack of rainfall -- they do not hedge more when their physical positions are larger. This effect persists even after accounting for the price impact of the physical supply shock and it is particularly strong under backwardation and tight inventories.

ABSTRACT. There is evidence that the use of corn as a biofuels feedstock has increased the crude oil pass-through to the corn price. Changes in US biofuels policy can be seen as initially increasing and subsequently retarding the use of corn in ethanol production. Because the policy both mandates but also limits this use, different regimes can prevail depending on which constraints are binding. Structural break methods show that the pass-through was important over the four years 2003-07 but has subsequently been much more limited. Competitive storage theory continues to explain much of the price movement even over those four years.

ABSTRACT. Many electricity markets are experiencing profound changes due to the large-scale deployment of renewable energy. This paper analyzes the effect of wind and hydro power on wholesale electricity prices. We focus on the Spanish market due to the high penetration of wind and hydro, and also the availability of data on water stored in Spanish dams. We estimate the impact of wind generation and hydro availability on the electricity price level and volatility through an ARX-GARCHX model. The results show that these two different renewables have different impacts on electricity prices: intermittent wind generation increases the price volatility, while dispatchable hydro reduces it. The results are consistent with hydropower plants behaving strategically and, given their zero marginal cost, dampening the otherwise higher price increases due to wind intermittency. Therefore, hydropower complements other intermittent renewables, not only operationally, but also economically.

ABSTRACT. The price spikes observed in electricity spot markets may be understood to arise from fundamental drivers on both the supply and demand sides. Each driver can potentially create spikes with different frequencies, height distributions and rates of decay. This behaviour can be accounted for in models with multiple superposed components, however their calibration is challenging. Given a price history we design a Markov Chain Monte Carlo (MCMC) procedure to perform Bayesian inference for the parameters of such models, and to execute posterior predictive checking for the assessment of model adequacy. The procedure is used to determine the number of signed jump components required in the APXUK and EEX markets, in time periods both before and after the recent global financial crises. We find significant structural changes in both markets with a reduction of the intensity and size, or disappearance, of positive price spikes. Our results can inform analyses which depend on the detailed structure of electricity prices, for example operational studies of flexible assets or the monitoring of spike behaviour by regulators.

ABSTRACT. Most electricity spot price series exhibit price spikes. These extreme observations may significantly impact the obtained model estimates and hence reduce efficiency of the employed predictive algorithms. For markets with only positive prices the logarithmic transform is the single most commonly used technique to reduce spike severity and consequently stabilize the variance. However, for datasets with very close to zero (like the Spanish) or negative (like the German) prices the log-transform is not feasible. What reasonable choices do we have then?

To address this issue, we conduct a comprehensive forecasting study involving 12 datasets from diverse power markets and evaluate 16 variance stabilizing transformations. We find that the \emph{probability integral transform} (PIT) combined with the standard Gaussian distribution yields the best approach, significantly better than many of the considered alternatives.

ABSTRACT. The causes of the surges in food prices in 2007 and 2012 are still a controversial issue. Literature offers their plausible explanations, which include e.g. financialization, depreciation of US dollars, or the tighter connection between the food market and the energy market (biofuels). The aim of the study is to investigate volatility spillovers between food, energy, US dollar and equity markets. The analysis uses daily series of volatility of corn, soybean, wheat, rice, US dollar, crude oil, and SP500 futures covering the period from January 4, 2000 to December 30, 2016. We base our analysis on forecast-error variance decompositions in a generalized vector autoregressive framework, which are invariant to the ordering of variables, as proposed by Diebold and Yilmaz (2012). The data are studied in rolling subsamples, since the evolution of relationships is expected. Taking into account a large number of parameters in the models in single iterations, lasso estimation methods are used. The results of the study reveal that both total and directional volatility spillovers change in time. Most volatility transmissions are observed among the same category of instruments, i.e. the financial instrument group or the agricultural commodity group. Corn is the most important agricultural commodity, as it transmits vast volatility to other instruments in the food market.

ABSTRACT. We investigate the impacts of the carbon tax (effective July 2012 to July 2014) on wholesale electricity prices in the Australian National Electricity Market (NEM). Analyzing spot and futures contracts in four major regional markets, we first compute ex-ante forward risk premiums in the pre-tax period, then use them to derive market-implied carbon premiums and pass-through rates in the carbon tax and post-tax periods. We find that carbon premiums and pass-through rates became increasingly higher, once the Clean Energy Bill had been introduced and subsequently passed in 2011. We also find strong evidence for a quick reaction of the extracted carbon premiums to changes in opinion polls for the Australian federal election in 2013 and the decision to repeal the tax. During periods where market participants could be relatively certain that the tax would be effective, we find expected carbon pass-through rates between 65% and 140%, which seem to be inversely related to emission intensities.

ABSTRACT. Studies on the relationship between oil prices and the EURUSD rate is mostly focused on the correlation of returns or levels. However, the volatility of oil price and the volatility of the EURUSD rate is also of importance for e.g., portfolio risk management, margin requirements or derivatives pricing models. In this paper, the relationship between the volatility of crude oil and the volatility of the EURUSD rate is analysed. A model-free analysis shows the presence of a joint factor in the volatilities after mid-2007. A term structure model for futures and options on both oil and EURUSD is proposed and estimated to WTI Crude Oil and EURUSD futures and options traded at the Chicago Mercantile Exchange from 2000-2012. The addition of a joint volatility factor signicantly improves the fit to oil options and short term EURUSD options after mid-2007.

ABSTRACT. Using time series econometric models and a simple Markowitz portfolio optimization approach on a unique dataset of diamond mining stocks’ prices, this paper addresses practical investment questions: Is investing in the diamond equity market a more feasible and liquid alternative to investing in diamonds? Are diamond stocks good portfolio diversifiers, as suggested by finance professionals? Our results show that the market of diamond stocks does not represent a valid investment alternative to the diamond commodity market. Additionally, we find evidence to believe that diamond equities are not good portfolio diversifiers.

ABSTRACT. This paper aims at measuring the systemic risk in commodity markets, which is defined as the risk of most of commodities trapping in price slumps (in distress). Based on daily data of a wide range of individual commodity futures indices from January 3, 2000 to 31 December, 2015, and with the very novel modeling of GAS-Factor copulas, this paper finds that the most clustered and highest commodity systemic risk is evident during the recent global financial crisis, and at the very beginning of the financialization of commodities and after 2013, the commodity systemic risk is also very high. Furthermore, we find that the Federal funds rate and Quantitative Easing have a significantly negative and positive impact on the commodity systemic risk, respectively.

ABSTRACT. Prior research documents a signicant variance risk premium (VRP) for the S&P 500 index but only for few equities. Using high-frequency data, we show that these results are affected by measurement errors in the realized variance estimates. We decompose the index VRP into factors related to the VRP of equities and the correlation risk premium. The former mostly drives the variations in the index VRP while the latter mainly captures the level of the index VRP. The two factors predict excess stock returns in the time-series and cross-section, but at different horizons. Together, they improve the return predictability.

ABSTRACT. We propose a model of adaptive expectations to back out the time-varying (ex-ante) risk premia for different commodities and maturities. Empirically, we show that the dynamics of risk premia is predominantly driven by market activity and financial risks, as proxied by open interest, hedging pressure, time-series momentum and the MSCI Emerging Markets Index. More generally, we provide evidence on the heterogeneity in the importance of different economic factors, e.g. changes in inventories, both across commodities and time horizons. Finally, we show that the expectations generated by adaptive learning are consistent with the cross-sectional average of Bloomberg's professional analysts’ forecasts.

ABSTRACT. The exploitation of the forward-looking structure of option prices enables to derive point risk measures in a model-free way. The 2012-2015 daily option-implied VaR and CVaR are extracted from the WTI crude oil future prices and the options written on it. Without relying on any distributional assumption, the proposed methodology overcomes the elicitability issue linked to the CVaR, thus making possible a comparison based on the backtesting results. Moreover, the CVaR-VaR ratio allows to forecast jumps in the underlying price density, which would have been a priori unpredictable with standard inference models.

ABSTRACT. What effect does the threat of expropriation have on resource extraction? Much of the economic literature suggests that uncertainty reduces investment, but the Green Paradox theory suggests the opposite. In this paper, we test this theory in the context of terrorism, which poses a real threat of violent expropriation of property rights. Facing this uncertainty, we find that oil producers in the Middle East and North Africa increase their extraction rate, especially in response to more severe terrorist attacks, as measured by victim deaths. This finding has important negative consequences for the world in terms of climate change and demonstrates a previously untested mechanism through which exhaustible resource supply is flooding the market.