ABSTRACT. We empirically reinvestigate the issue of the excess co-movement of commodity prices initially raised in Pindyck and Rotemberg (1990). Excess co-movement appears when commodity prices remain correlated even after adjusting for the impact of fundamentals. We use recent developments in large approximate factor models to consider a richer information set and adequately model these fundamentals. We consider a set of eight unrelated commodities along with 184 real and nominal macroeconomic variables, from developed and emerging economies, from which nine factors are extracted over the 1993-2013 period. Our estimates provide evidence of time-varying excess co-movement which is only occasionally significant. We further show that speculative intensity is a driver of the estimated excess co-movement, as speculative trading is both correlated across the commodity futures markets and correlated with the futures prices. Our results can be taken as direct evidence of the significant impact of financialization on commodity-price cross-moments.

ABSTRACT. In this paper we borrow recent advances from the fixed income literature, to study the dynamics of futures prices. From a time-series perspective, ours is a study of commodity risk premiums across contract maturities, while form a cross sectional point of view, our work can be understood as a study of convenience yields.

Joslin, Singleton and coauthors have developed a new approach to deal with Dynamic Term Structure Models (DTSM). This consisted basically in the rotation of the latent factor model to another one based on observable variables (a portfolio of bonds of different maturities). These authors have studies DTSM models i) with only observables, ii) with observables and macroeconomic factors that are spanned from the term structure of interest rates, and ii) with observables and unspanned macroeconomic factors. Unspanned factors means that these are not bond pricing factors, however, they appear in the pricing kernel, therefore, that can drive the risk premia of the observables.

We adapt and implement all these innovations to the study of commodity futures prices. In our empirical work, we collect several variables related to commodity markets and the macroeconomy. In particular, we focus on crude oil futures and as risk premia determinants we use the stock market return, economic growth, inflation, exchange rate changes, default spread, term spread, and commodity-specific variable, such as, hedging pressure and inventory.

In our preliminary results we find only weak evidence of spanning, since at most the first three PC of crude oil log futures prices explain 31.6\% of variation in inventories. In consequence, three factor models including a non futures risk factor with spanned macro risk do a poor job fitting the observed PC compared to a model with three futures risk factors. In light of these results, the unspanned macro risk hypothesis was tested. We estimate four factor models with the first three PC as pricing factors. We obtained only weak evidence supporting a relationship between crude oil futures risk premia and stock market returns.

ABSTRACT. This paper proposes a more granular momentum strategy termed ‘microscopic momentum’, which decomposes the 12-month conventional momentum into single-month components. The novel decomposition reveals a persistent seasonality effect in the cross-section of commodity futures returns. An investment strategy that exploits the seasonal pattern generates statistically significant economic profits after controlling for standard, commodity-specific risk and behavioral factors. We show that the cross-sectional seasonality documented is not driven by transaction costs, particular commodity sectors or month-of-year seasonality effects, but appears to be related to commodities hedging pressure. Furthermore, our findings suggest that conventional momentum profits in commodity futures can be grounded within a risk-based framework of term structure and hedging pressure factors after controlling for the cross-sectional seasonality.

ABSTRACT. Photovoltaic (PV) technology has importantly increased in the last few years generating an important policy debate on the various incentive measures (feed-in-tari¤s or FiT, R&D subsidies, tradable green certi…- cates, renewable portfolio standard, net metering) coupled with the Eu- ropean 2030 target (27% share of renewable energy consumption) and in our case study, with the 2030 objectives of the French "Transition Energetique" (40% of energy consumption must be renewable).Indeed, feed-in tari¤s for PV in France have had their ups and downs following several changes of heart of the regulatory authority. Indeed, at the end of 2010, the moratorium froze the photovoltaic market in France by sus- pending subsidies and connections to grid network of ongoing projects except for small installations with power below 3 kWp, i.e. household installations. Some papers have tried to understand the extent of PV deployment considering its main drivers. The seminal works of Shae¤er et al. 2004, Nemet, 2006 Pillai 2015 and Rubin 2015 consider as the main driver the learning e¤ect of installed capacity. The limits regarding this liter- ature is that results are very sensitive to data and the fact that there may be factors other than learning that are also playing a role. Then, other papers have focused on deployment where the di¤usion of inno- vation is often described by a logistic function or "S-curves" (see Bass model-Gerowski, 2000, Guidolin and Mortarino, 2010). Such models, despite being insightful, do not take into account the incentive policies such as subsidies and feed-in tari¤s. Finally, papers like Dusonchet and Telaretti, 2010 or Zhang and Hamori, 2011 consider the pro…tability of PV investment across Europe, Japan, Germany, etc. in presence of in- centives schemes but they do not consider the long-run targets that are behind the choice of those incentives. In this paper we contribute to the previous literature by answering the following research questions: Do we really need Feed In Tari¤s (FIT) for French residential con- sumers/producers? The reply to the previous question depends on the answer we give to the following question: is the trend in the installation price of panels exogenous or is there a learning-by-doing e¤ect in France?

ABSTRACT. Currently most Renewable Energy Certificate (REC) markets (or green certificates) are defined based on targets which create an artificial step demand function resembling a cliff. This target policy produces volatile prices which can make investing in renewables a risky proposition. In this paper, we propose an alternative policy called Adjustable Dynamic Assignment of Penalties and Targets (ADAPT) which uses a sloped compliance penalty and a self-regulating requirement schedule, both designed to stabilize REC prices, helping to alleviate a common weakness of environmental markets. To capture market behavior, we model the market as a stochastic dynamic programming problem to understand how the market might balance the decision to use a REC now versus holding it for future periods (in the face of uncertain new supply). Then, we present and prove some of the properties of this market, and finally we show how this mechanism reduces the volatility of REC prices which can stabilize the market and encourage long-term investment in renewables.

ABSTRACT. With increasing wind power penetration more and more volatile and weather dependent energy is fed into the German electricity system. To manage the risk of windless days and transfer the risk of unstable revenue stream from wind turbine owners to third parties wind power derivatives were introduced. These insurance-like contracts allow to hedge the risk of unstable wind power production at turbine location on exchanges like Nasdaq, European Energy Exchange. We present a new method to model weather derivatives with very skewed data incorporating extreme events in modelling seasonal volatility and compare with common approaches of Benth et al. (2009) and Weron (2011) in transformed Gaussian and pure non-Gaussian CARMA(p, q) models. Our results indicate that our transformed Gaussian CARMA(p, q) model is preferred over the non-Gaussian alternative with Lévy increments. The calibration of the empirical market price of risk shows typical behaviour for futures and forwards in electricity markets.

ABSTRACT. Using six prominent metal commodities, we provide evidence on the out-of-sample forecasting of stock returns for the market indices of the G7 countries, for which there is little prior evidence in this context. We find precious metals (Gold and Silver) can improve forecast accuracy relative to the benchmark and performs well compared to forecast combinations. From an economic gains perspective, forecasting returns provides certainty equivalent gains in a market-timing strategy for the G7 countries. These certainty equivalent gains are large enough to make active portfolio management attractive, even for individual investors. Gains remain after considering reasonable transaction costs.

ABSTRACT. A growing literature, e.g., Gorton & Rouwenhorst (2006), Gargano & Timmermann (2014) and Barunk & Krehlk (2016), analyzes several variables that are known to have predictive ability for stocks and documents predictive power also for commodity excess returns and volatility. The growing number of predictive variables raises several questions: Which variables known to predict stock excess returns can also predict commodity excess returns? Do the variables that predict commodity excess returns also forecast commodity volatility? These are some of the questions we want to study.

ABSTRACT. The literature has established that the risk premia of commodity futures can be captured using roll-yield, past performance, hedging pressure or volatility signals, inter alia. Rather than confronting all the available signals to choose an optimal one, we propose to integrate them by allocating wealth to the commodities that most signals recommend to take positions on. Our results indicate that, despite its simplicity, the proposed approach is very effective at capturing the risk premia of commodity futures, and performs better than alternative integration approaches based on principal components or utility maximization. These findings are robust to the use of both front-end and spreading returns, are corroborated by cross-sectional tests, sustain relatively small transaction costs and are not driven by data mining.

ABSTRACT. We study a dynamic Cournot model of competition between firms for a single homogeneous good. The market is divided into several geographical regions, each having its own demand and market price. The competition spans over several time periods. Both buyers and sellers can produce inventory in a period and use it in subsequent periods. Our analysis highlights that heterogeneity in the firms' shipping costs plays a major role for price formation and price disparities across regions. We shows that there exists a unique subgame perfect equilibrium allocation plan for inventory and product distribution, in which each firm sells a positive quantity in each period. We illustrate the power of our framework on the market of fertilizers.

ABSTRACT. There are several approaches in the literature for the derivation of price forward curves (PFCs) which distinguish among each other by the procedure employed for the derivation of seasonality shapes, smoothing technique and by the design of the optimization procedure. However, a comparative study to highlight the strengths and weaknesses of different methods is missing. For the construction of PFCs we typically incorporate the information about market expectation from the observed futures prices and the deterministic seasonal effects of electricity prices. In most existing approaches, the seasonality shape is historically derived and it is an exogenous input to the optimization procedure. As seasonal effects on electricity prices differ between markets, our model allows a more general and flexible definition of the seasonality shape. In this study, we propose an alternative calibration procedure for the seasonality shape, where the level of futures as well as historical spot prices are simultaneously taken into account in a joint optimization approach. We discuss comparatively the features of existing methods for PFCs and highlight the advantages of our optimization procedure.

ABSTRACT. We consider a stylized model for a power system with distributed local energy generation and micro-storage. The aim of each agent is to minimize his own cost of electricity consumption by controlling the size and the management of its own storage device. We consider non-cooperative game setting that leads to the analysis of a non-zero sum stochastic game with $N$ players and to the search of Nash-equilibria. In a linear quadratic setting, we are able to give explicit solutions for the storage management and to link the implied extended Mean-Field Game to Mean-Field type control problem.

ABSTRACT. This study presents a model for the joint dynamics of power price, temperature, and hydrological balance, with a view towards scenario analysis. Temperature is a major demand-side factor affecting power prices, while hydrobalance is a major supply-side factor in power markets dominated by hydrological generation, such as the Nordic market. Our time series modelling approach coupled with the skew-Student distribution allows for interrelations in both mean and volatility, and accommodates most of the discovered empirical features, such as periodic patterns and long memory. We find that in the Nordic market, the relationship between temperature and power price is driven by the demand for heating, while the cooling effect during summer months does not exist. Hydrobalance, on the other hand, negatively affects power prices throughout the year. We demonstrate how the proposed model can be used to generate a variety of joint temperature/hydrobalance scenarios and analyse the implications for power price.

ABSTRACT. This paper studies the pricing of a swing option under stochastic volatility. We propose an algorithm based on the finite element method to price put options under a Black-Scholes-Merton paradigm with stochastic volatility. We show the approach is accurate and efficient.

ABSTRACT. This paper studies the impact of financialization on commodity futures return distributions. Over the period associated with financialization, we find shifts in the nature of volatility across the entire cross-section of commodities and shifts in the nature of the entire returns distribution across the agricultural and metals sectors. The effeects of financialization appear to be the most profound on the metal sector and the altered nature of the metals return distributions appears to have transmitted itself to the mining sector, leading to simultaneous increases in capital expenditures which may have contributed to the recent value destruction in this sector.

ABSTRACT. This paper investigates the extend and impact of financialisation on the aluminium market. I test the hypothesis that the inflow in commodity linked financial products led to banks being more integrated in financial markets and eventually abusing their market power to manipulate prices. This hypothesis is tested by identifying a corresponding storage demand shock using a structural model of the US economy with aluminium used in production. This dynamic stochastic general equilibrium model includes storage and frictions representing warehouse queues. The model is estimated on data for the US from 1987 to 2008 and the frictions representing warehouse queues are found to be significant. Furthermore, monetary policy has a secondary transmission channel in a model with storage. The results suggest that financialisation played a role in explaining aluminium market dynamics leading up to 2008 and had a negative impact on the economy.

ABSTRACT. The financialization of crude oil markets over the last decade has changed the behavior of oil prices in fundamental ways. In this paper, we uncover the gradual transformation of crude oil from a physical to a financial asset. Although economic demand and supply factors continue to play an important role, recent indicators associated with financialization have emerged since 2008. We show that financial variables have become the main driving factors explaining the variation in crude oil returns and volatility today. Our findings have important implications for portfolio analysis and for the effectiveness of hedging in crude oil markets.

ABSTRACT. We examine whether and to what extent successful equities investment strategies are transferrable to the commodities futures market. We investigate a total of 7 investment strategies that involve optimization and mean-variance timing techniques. To account for the unique characteristics of the commodity futures market, we propose a novel method of classification based on momentum or term structure properties in the formation of long-short portfolios in conjunction with the quantitative strategies from the equities literature. Our approach generates significant excess returns and risk-adjusted performances as measured by the Sharpe and Sortino ratios and the maximum drawdown. We find no significant correlation between the strategies excess returns and common risk factors. There is no evidence that excess returns are a compensation for liquidity risk. The strategies are robust to transaction costs and choice of model parameters and exhibit stable performance across various market conditions including times of financial crisis.

ABSTRACT. We introduce the curve momentum strategy that operates within commodity futures curves. The curve momentum strategy, diversified across all markets, generates significantly positive average returns and a (annualized) Sharpe ratio of 1.28. This performance cannot be explained by exposure to the conventional carry and momentum strategies that operate primarily across (rather than within) commodity futures curves. The performance of the curve momentum strategy is difficult to reconcile with existing behavioral, limits-to-arbitrage and rational asset pricing theories that have been largely developed to explain the conventional momentum effect.

ABSTRACT. We propose a stochastic volatility CAPM model for commodity futures in continuous time. We implement the model using high-frequency intraday data from the crude oil and S&P500 futures markets for the October 2001--December 2012 period. We estimate this model with a simple GMM estimator that matches sample moments of the realized volatility to the corresponding population moments of the integrated volatility. We find evidence of a significant inpact of the S&P500 futures on crude oil futures.

ABSTRACT. This study investigates the volatility dynamics in the market for fresh-farmed Atlantic salmon. Previous studies suggest that there has been a shift, in the beginning of the 2000s, in terms of the drivers for wholesale salmon prices. Research shows that as the industry has matured, salmon prices have gone from being productivity-driven to being input factor driven, i.e. increasingly being determined by prices of agricultural products which are used in fish feed. At around the same time, salmon price volatility has more than doubled, possibly linked to an increase in food prices. In this study, we investigate whether the increased dependence of salmon wholesale prices on agricultural food prices is also evident as volatility spill-overs from agricultural prices to salmon prices, and whether we can find any structural shifts in the volatility spill-over. The results will be of interest to salmon producers in their hedging decisions for both input factor prices and wholesale salmon prices.

ABSTRACT. Accurate models for forecasting of the salmon price can support operational considerations such as inventory management and harvest timing, as well as financial aspects such as investment decisions, risk management, and trading. In this paper, a general-to-specific regression approach is applied in a twofold way: At first, to determine the most important factors that affect the Atlantic salmon spot price, represented by the Fish Pool Index (FPI), and, secondly, to provide short-term spot price forecasts. The forward selection procedure is employed to determine the best in-sample subsets of 37 different explanatory variables, as well as their lagged versions, across time increments of 1, 2, 3, and 4 weeks. Based on these determined subsets, the corresponding regression models are applied out-of-sample to provide one-step-ahead forecasts. The best set of forecasting models, consisting of five variables, provide favourable predictions. In particular, the out-of-sample forecast error is half that of the random walk.

ABSTRACT. We present a multistage stochastic programming model for determining the optimal hedging strategy for salmon producers. The objective is to maximize expected profits selling salmon either in the spot market or in forward contracts. The decisions are subject to constraints on conditional value-at-risk.

ABSTRACT. We study consequences of default supply auctions in electricity markets on premiums over spot prices obtained by winning bidders, and on speculation and hedging activities in power derivatives markets. We use data from sixty-four default supply auctions, those of (CESUR) in the Spanish Electricity Market from 2007 to 2013, and those of Basic Generation Service auctions (PJM-BGS) in New Jersey's PJM market from 2006 to 2015. Winning bidders obtained an average yearly premium of 10.15% (CESUR) and 112% (PJM-BGS) in excess of electricity spot prices. Premiums and number of bidders are negatively related. In CESUR, hedging-driven trading in power derivatives markets is predominant around auction dates, but in PJM-BGS, speculation-driven trading prevails. Thus, we detect two major challenges to default supply auctions, high ex-post premium and unintended speculative trading. As alternatives to default supply auctions, we evaluate the performance of several methods. The key factor in this regard is consumer’s aversion to price volatility. If consumers are not averse to price volatility, best methods are based on daily wholesale prices. However, as aversion to price volatility increases, methods based on prices of liquid electricity swap contracts become preferable to consumers.

ABSTRACT. We consider the sequential decision problem faced by the manager of an electric vehicle (EV) charging station, who aims to satisfy the charging demand of the customer while minimizing cost. Since the total time needed to charge the EV up to capacity is typically less than the amount of time that the customer is away, there are opportunities to exploit electricity spot price variations within some time window. However, it is also true that the return time of the customer is uncertain, so there exists the risk of an insufficient charge. We formulate the problem as a finite horizon Markov decision process (MDP) and consider a risk–averse objective function by optimizing under a dynamic risk measure constructed using a convex combination of expected value and conditional value at risk (CVaR). For the first time in the literature, we provide an analysis of the effect that risk parameters, e.g., the risk–level α used in CVaR, have on the structure of the optimal policy. We show that becoming more risk–averse in the dynamic risk measure sense corresponds to the intuitively appealing notion of becoming more risk–averse in the order thresholds of the optimal policy. This result allows us to develop computational techniques for approximating a spectrum of risk–averse policies generated by varying the parameters of the risk measure. Finally, numerical results for a case study using spot price data from California ISO (CAISO) are shown, where the Pareto optimality of our policies when measured against practical metrics of risk and reward is examined.

ABSTRACT. This paper analyses the interaction between centralised carbon emissive technologies and distributed intermittent non-emissive technologies. In our model, there is a representative consumer who can satisfy her electricity demand by investing in distributed generation (solar panels) and by buying power to a centralised firm at a price he set up. Distributed generation is intermittent and induces an externality cost to the consumer. The firm provides non-random electricity generation subject to carbon price and to transmission costs. The objective of the consumer is to satisfy her demand while minimising investment costs, payment to the firm and intermittency cost. The objective of the firm is to satisfy consumer's residual demand while minimising investment costs, demand deviation costs and maximising payment from the consumer. Investment decisions are formulated as McKean-Vlasov control problems with stochastic coefficients. We provide explicit, model-free solutions to the optimal decision problems faced by each player, the solution of the Pareto optimum and the Stackelberg equilibrium where the firm is the leader. We find that, from the social planner point of view, carbon price or transmission costs are necessary to justify a positive share of distributed capacity in the long-term, whatever the respective investment costs of both technologies are. The Stackelberg equilibrium is far from the Pareto equilibrium, leading to a much larger share of distributed energy and to a much higher price for centralised energy. Joint work with Matteo Basei, Imen Ben Tahar and Huyên Pham.

ABSTRACT. We evaluate the out-of-sample performance of commodities in portfolios composed of stocks, bonds, and T-bills. The evidence on their ability to generate economic value is mixed, with previous studies ignoring the potential role of asset return predictability, and the states of the business cycle. Using monthly data over the period 1976-2015, we document sizeable utility gains for a mean-variance investor who exploits predictability in asset returns. For example, an investor with moderate level of risk aversion, who imposes sensible restrictions on portfolio weights, can generate net-of-transactions-costs utility gains of over 130 basis points per annum. In addition, we find strong influence of the business cycle, as dated by the National Bureau of Economic Research, on portfolio performance. During the recessionary phase of the business cycle, commodities are shown to generate substantial utility gains of over 1338 basis points per annum. In expansionary periods of the business cycle, however, commodities do not add economic value generating utility losses of over 50 basis points. These findings suggest that the ability of commodities to improve portfolio performance is countercyclical. Our findings are robust to varying levels of risk aversion, portfolio weight constraints, transactions costs and alternative performance metrics.

ABSTRACT. The financialization of the commodity markets would have two effects. The first one is a liquidity effect, more speculators bring more liquidity. The second one is an informational effect, speculators come with their information which is more or less noisy. A burning issue is the consequences of these two effects on the physical market because of the feedback of the derivatives market. We decided to extend Ekeland et al. (2014) with information asymmetry. The core of both models is the same. This paper redefines what is an efficient market with a hedging pressure. Two theorems which generalize the fully-revealing rational expectations equilibrium with a linear hedging pressure are demonstrated. The empirical implications are strong. The spot price is predictable only by the futures price in a one-factor model. Moreover, noisy signals make the risk premium stochastic. They are consequences from the stochastic informational effect that we distinguish from the deterministic physical effect.

ABSTRACT. We solve a dynamic general equilibrium model with generalized disappointment aversion preferences and continuous state endowment dynamics. We apply the framework to the term structure of interest rates and show that the model generates an upward sloping term structure of nominal interest rates, a downward sloping term structure of real interest rates, and that it accounts for the failure of the expectations hypothesis. The key ingredients are disappointment aversion preferences, preference for early resolution of uncertainty, and an endowment economy with three state variables: time-varying macroeconomic uncertainty, time-varying expected inflation and inflation uncertainty.

ABSTRACT. In this study, we construct static and dynamic probit models to forecast the monthly excess return signs of U.S. oil and gas industry index by mining a big macroeconomic variable dataset which is made by McCracken and Ng (2015). Three different information criteria and a Forward Sequential Selection algorithm are used to select the most ”important” macroeconomic variables for prediction. Our results show that Durable Materials Industrial Production and Real Estate Loans at All Commercial Banks are two important predictors. Specifically, the growth of Durable Materials Industrial Production and the increasing growth speed of Real Estate Loans at All Commercial Banks can increase the probability of positive sign of excess stock returns for US oil and gas industry. In-sample and out-of-sample results show that Static-AIC model with 14 macroeconomic variables has better forecasting performance than other models chosen by BIC and HQC criteria. The active trading strategies based on the Static-AIC model can improve the Sharpe ratio of buy-and-hold strategy and the two cases(with and without transaction costs) are compared. The forecasting ability of the Static-AIC model is robust to different industry classification systems.

ABSTRACT. We examine the effect of collateral on firms' bank lines of credit. Although the average firm experiences a negative liquidity shock during the recent financial crisis, the concurrent increase in gold prices provides a positive shock to the collateral value of gold firms. Using this natural experiment, we find that gold firms experience better access to credit lines during the crisis period relative to non-gold firms. This effect is unique to firms who are not hedging price risk and are, therefore, most exposed to the positive price shock to gold. We also document the above collateral channel using two-stage least squares and report similar results with net debt issuances.

ABSTRACT. In 2014 17% of exports in the World was energy, the largest part, oil. The structural role of oil goes far beyond its share in trade and can only be understood by looking at interactions globally. The role of oil compared to the other three energy commodities -coal, gas and electricity, in shaping the world trade web (WTW) is addressed by means of network theory. Three main measures are taken into account: the ratio of mutual exchanges (reciprocity); the role of distances in trade patterns (spatial filling); and the correlation of energy commodities with the WTW and with four trading categories: food, capital goods, intermediate goods and consumption goods. The analysis delivers five main results:1) the energy commodities network was structurally stable amid dramatic growth during the decade considered; 2) oil is the most correlated energy commodity to the world trade web; 3) oil is the most pervasive network, though coal is the less affected by distances; 4) oil has a remarkably high level of internal reciprocity, higher than most of any other commodity traded in the world; 5) the reciprocity of the trade network is negative correlated in time with the price of oil, regardless of global imbalance.

ABSTRACT. This paper investigates the costs of oil shocks for the economy's welfare. Using a VECM, we empirically show that domestic US oil production shocks only have a weak and temporary impact on macroeconomic variables, while the effect of global oil price shocks is persistent and economically and statistically significant. We rationalize these findings within a calibrated two-sector model in which oil is an input factor for industrial production and also part of the household's consumption bundle. Based on the model, we show that oil shocks are associated with large welfare costs for oil-importing economies. Our framework enables several experiments regarding the welfare implications of a reduced oil share in production and consumption, the strategic petroleum reserve, and technological innovations such as fracking.

ABSTRACT. News about macroeconomic fundamentals and geopolitical events affect crude oil markets differently. Using sentiment scores for a broad set of global news of different types, we find that news related to macro fundamentals have an impact on the oil price in the short run and significantly predict oil returns in the long run. Geopolitical news have a much stronger immediate impact but exhibit not predictability. Moreover, geopolitical news generate more uncertainty and greater trading volume, consistent with a disagreement explanation, while macroeconomic news reduce informational asymmetry and are associated with subsequent lower trading volume. Finally, we find that news sentiment contains more information about future expectations than about future realizations of economic data.

ABSTRACT. This paper uses the new, mildly explosive/multiple bubbles technique proposed by Phillips, Shi and Yu (2015, International Economic Review 56(4), 1043-1133) and the bubble migration hypothesis applied in Phillips and Yu (2011) International Economic Review to analyse the time series behaviour of a number of key financial variables during the global financial crises. We focus on the Dry Baltic Index as the main gauge of the state of the economy and find that it exhibits explosive behaviour at the end of the 2007 coinciding with the collapse of the home price bubble. We additionally document subsequent mildly explosive episodes in credit risk CDS portfolios and commodity prices. Eventually, as the crisis unfolds bubbles are reflected in the real economy as exhibited by the mild explosivity documented in US jobless claims just before the Lehman´s episode. Our framework is embedded under the sequential model proposed by Caballero, Farhi and Gourinchas (2008) (CFG) which episodes how bubble creation and collapse migrate across markets, from the U.S. subprime market, through certain commodities markets to the U.S. bond market. In this paper we contribute to the literature by testing for mild explosivity and bubble migration using a wider set of key financial variables than previously analysed in the literature subsequetnly testing the CFG migration hypothesis.

ABSTRACT. Price limits on commodity futures appear to restrict price discovery but not stop speculation. Consistent with delayed price discovery, returns continue in the same direction after limit days and do not reverse after one week, whereas returns are small after large price moves that do not hit limits. High volatility immediately before limits can be attributed to low inventories and increased stockout risk rather than excess speculation. Market participants trade similarly around limit days and non-limit days; speculators do not reduce their positions in response to limits. The options-implied futures price on the limit day predicts the next-day futures price, which suggests price discovery partially moves from the futures market to the options market.

ABSTRACT. Becoming an expert in the commodity markets has traditionally required a novice to seek an apprenticeship at an established commodity firm. This chapter provides an alternative approach: a reader is provided a reasonably comprehensive tour of the always dynamic and frequently opaque commodity markets, including views on (1) commodity trading strategies, (2) common mistakes, and (3) catastrophic blowups. The specific commodity trading strategies covered are trend-following and calendar spreads. The common mistakes that the chapter includes are (1) targeting returns rather than risk metrics, (2) establishing inappropriate trade sizing, and (3) failing to fully appreciate the psychological discipline required for trading. In addition, the chapter provides case studies on the catastrophic derivatives blowups at Amaranth and at MF Global.

ABSTRACT. Commodity derivatives are exposed to a variety of risks that usually do not impact financial derivatives. For example, the forces of supply and demand of the underlying commodity, weather, are just a few of those that influence the underlying price processes. The standard approach usually adopted by the market participants when analyzing and pricing commodity derivatives is to incorporate a normal component of these risks. However, sometimes a non-normal or jump event exists and its incorporation in the risk management and pricing processes should be carefully structured. The main difficulty is determining whether an event is permanent or not, and as a result, what is the impact on the risk management processes.

Outlier detection allows us to quantify the dimension and type of significant price changes in a commodity derivative. Once this identification is done, further impact analysis can be performed. This is important for a firm's risk management activities when hedging a commodity portfolio correctly. Hedging normal risk is executed with strategies appropriate for that type of risk. Different strategies are needed to hedge the non-normal risk that is found around extreme events or outliers.

Ignoring the content of an outlier's impact can cause the following:

1. Overstatement of normal risk of the portfolio, 2. Additional cost to hedge the overstated risk, 3. Inappropriate and inadequate hedges, 4. Misstatement of risk associated with extreme events, and 5. Poor scenario and sensitivity analysis.

ABSTRACT. In April 2012, Delta Air Lines (Delta) announced it would purchase the mothballed Trainer oil refinery to hedge fuel price risk. Analysts and academics emphatically derided the move, stressing that Delta’s management was ill-equipped to oversee large scale refining operations. However, we show that debt- and equity-holders responded significantly positively to the acquisition announcement, and confirm that Trainer subsequently reduced Delta’s equity exposure to fuel price shocks. The refinery's operations also reduced Delta’s bond and loan spreads over time. We conclude that this unique experiment in vertical integration and commodity price hedging proved successful for the airline.

ABSTRACT. We present evidence that the capital stock of firms in oil and gas exploration as well as oil inventories are each positively related to the slope of the futures curve, and negatively predict returns on holding crude oil futures contracts. We build an equilibrium model of resource extraction, exploration, and storage with these features. Capital lowers extraction costs as firms drill in increasingly expensive fields, while inventory helps smooth fluctuations in demand and extraction. The more aggressive supply response of producers during periods of high capital stocks implies that oil prices can decline in response to positive demand shocks, leading to a conditionally negative oil risk premium at such times. The model sheds light on the role of declining well quality on the positive trend of real oil prices in the current millennium, and the peaking of consumption.

ABSTRACT. We develop a jump-di¤usion model for pricing and hedging with margined options on futures. An attractive feature of margined options is that there is no early exercise premiums under plausible scenarios. Model parameter estimates and out-of-sample pricing errors are calculated using data on Brent Crude contracts. Using the same pricing technology, we also hedge equity style options with margined options. Hedging coefficients are derived by matching an extended set of Greeks. We find that a target equity option can be e¤ectively hedged using a portfolio of two margined options and the underlying. As has been reported elsewhere, a delta hedge is of little use when the underlying is a jump-di¤usion.

ABSTRACT. This paper presents an empirical study on hedging long-dated crude oil futures options with forward price models incorporating stochastic interest rates and stochastic volatility. Several hedging schemes are considered including delta, gamma, vega and interest rate hedge. Factor hedging is applied to the proposed multi-dimensional models and the corresponding hedge ratios are estimated by using historical crude oil futures prices, crude oil option prices and Treasury yields. Hedge ratios from stochastic interest rate models consistently improve hedging performance over hedge ratios from deterministic interest rate models, an improvement that becomes more pronounced over periods with high interest rate volatility, such as during the GFC. An interest rate hedge consistently improves hedging beyond delta, gamma and vega hedging, especially when shorter maturity contracts are used to roll the hedge forward. Furthermore, when the market experiences high interest rate volatility and the hedge is subject to high basis risk, adding interest rate hedge to delta hedge provides an improvement, while adding gamma and/or vega to the delta hedge at worsens performance.

ABSTRACT. Futures markets are a potentially valuable source of information about price expectations. Exploiting this information has proved difficult in practice, because time-varying risk premia often render the futures price a poor measure of the market expectation of the price of the underlying asset. Although this expectation in principle may be recovered by adjusting the futures price by the estimated risk premium, a common problem is that there are as many measures of the market expectation as there are estimates of the risk premium. We propose a general solution to this problem that allows us to select the most accurate estimate of the expectation for any set of risk premium estimates. We illustrate this approach by solving the long-standing problem of how to estimate the market expectation of the price of crude oil. We provide a new measure of oil price expectations that is substantially more accurate than the alternatives and more economically plausible. Our analysis has implications for the estimation of economic models of energy-intensive durables, for oil price forecasting, and for the measurement of oil price shocks.

ABSTRACT. In this paper, we investigate whether commodity index trader (CIT) positions help to explain the increase in the correlations between agricultural commodities and equities starting around 2008. Some argue institutional investors who invest both in stock and commodity markets demolish the borders between these two seemingly unrelated markets and increase correlations, a recent phenomenon known as financialization. Yet, some others argue recently correlations have decreased back to historical levels and thus such increase between 2008 and 2012 was due to business cycle effect. Our results do not support one side but show that both factors are critically important to predict correlations between agricultural commodities and stocks. Furthermore, we depict CITs prefer to go back to their playgrounds under scarce liquidity and thus correlations decrease back to levels absent institutions. Hence, low liquidity is a significant obstacle inhibiting financialization to occur.

ABSTRACT. Information is a crucial component in the decision making process of trading agents. However, the presumption that individuals form rational expectations by accurately processing all available information and forming their forecasts accordingly has found mixed support in the empirical literature. In this paper we develop a conceptual model and empirically test the impact of agents’ attitudes (market informedness and risk aversion) on their price expectation (forecast accuracy) through their trading behaviour (use of forecasting and hedging). Using a unique data set consisting of 158 trading agents, we tested our hypotheses on real ex ante forecasts, evaluated ex post, in an electricity market context. Electricity markets are an ideal research setting as they require detailed information-processing and they clear through the actions of heterogeneous agents whose expectations have major effects on market efficiency. We have three main findings; firstly the results reveal the importance of the informational role of hedging (forward trading) on decreasing agents’ expectation biases. Secondly, although we find that more informed agents use more advanced forecasting methods, most surprisingly more information does not further enhance forecast accuracy. Thirdly, we show that agents’ risk aversions do not impact their trading behaviour, contrary to the conventional theoretical constructs. This paper is one of the first to take an information-based view to study the trading behaviour of agents and their price expectations; highlighting the importance of informational efficiency of forward trading and market informedness in the decision making process of trading agents. Implications for further research, market regulators and market participants are derived and discussed.

ABSTRACT. This paper investigates how market liquidity affects value, risk, and performance of energy equity portfolios. For this purpose we develop a model that compares a first setting free of liquidity frictions with a setting that is adjusted to illiquidity. The model is calibrated on a dataset of thirty energy equities traded on the NYSE. We deal with tick-by-tick data to estimate market liquidity parameters, which means that millions of trades are considered. The calibrated model is then used to conduct a variety of empirical experiments. As a first outcome, we find that the negative effect of illiquidity on value, risk, and performance behaves in a convex way with portfolio quantities. This suggests that impact of illiquidity may be minimized by holding optimal energy equity quantities. Most importantly, our second outcome establishes that the performance-based ranking of energy equity portfolios changes depending on whether liquidity is taken into consideration in performance evaluation or not. Precisely, a portfolio that is preferred to another when liquidity issues are ignored may actually be preferred when illiquidity is taken into account. This result holds under different performance measures as the Sharpe ratio or the risk-adjusted return on capital.

ABSTRACT. We model the impact of supply and demand variables on the price of electricity futures in a no-arbitrage model, using daily data between 2003 and 2014. The model allows for unspanned economic risk, which is captured by the supply and demand variables but not identified by the futures price. The model provides a satisfactory fit as well as a consistent framework to study the interactions between the electricity futures and the demand and supply variables. We characterize the risk premium implied by the model and identify the risk premium components associated with demand and supply. The unspanned risk premium associated with supply is highly time-varying and constitutes the most important component of the total risk premium embedded in electricity futures. This risk premium becomes negative after 2010.

ABSTRACT. The aim of the study is to assess the impact of development of the renewable energy sector in the EU on the relationships between renewable and non-renewable energy consumption and economic growth in the period 1995-2014. The study is divided into two stages. During the first one, two groups of EU member countries are identified with reference to the level of development of their renewable energy sectors in the analysed period. Three variables - employment, turnover and investment in the renewable energy sector - are used to identify the countries with similar levels of development of this sector. During the second stage, the dynamic common correlated effect estimator proposed by Chudik and Pesaran (2015) is used in order to estimate the error correction models. The models allow for verification of the long-term and short-term relationships between non-renewable and renewable energy (or electricity) consumption and economic growth in both groups of countries and also as a robustness check for all EU countries. The results reveal that the level of development of the renewable energy sector influences linkages between energy consumption and economic growth. The findings on long-term output elasticities suggest that along with traditional inputs, such as capital and labour force, both renewable and non-renewable energy plays a significant role in the process of economic development in the countries with a relatively high level of development of the renewable energy sector. In the remaining countries only non-renewable energy is important in long-term equilibrium. The results obtained for a short-term perspective indicate bidirectional causal relations between renewable energy consumption and economic growth in the group of countries with a relatively high level of development of the renewable energy sector. In other countries only non-renewable energy (or electricity) consumption weakly influences their economic growth.

ABSTRACT. This paper theoretically and empirically revisits carbon pricing from the supply-side perspective for carbon assets to solve a recent low price issue, which may delay the development of emission reduction technologies in the sense of marginal abatement costs. We propose a carbon pricing model linked to crude oil prices, which has historically been employed in supply-side driven pricing of long-term contracts for energy trading in the early stage. Since the model is designed to hold carbon prices between certain lower and upper boundaries using S-shaped carbon price linkage to crude oil prices, it can be useful to overcome a recent low carbon price issue. In addition, it is shown that the model can alleviate the difficulties of carbon derivative pricing in selecting market price of risk. Empirical studies using EUA and Brent crude oil futures prices estimate the parameters of the Brent crude oil-linked EUA price model. EUA prices simulated from the model with the parameter estimates are compared with historical EUA prices. The results suggest that simulated EUA prices from the model be kept relatively higher than historical EUA prices. This is preferable for accelerating carbon emission reductions in that emission reduction technologies with high marginal abatement costs are affordable. It may imply that EUA must be priced using a crude oil-linked carbon price model in the early stage of EUA trading until EUA markets are fully matured from the supply-side perspective for carbon asset pricing, not employing a premature market-based or supply and demand-based carbon price model. To show usefulness of crude oil-linked carbon pricing, we also give a numerical example of European carbon option pricing based on the Brent crude oil-linked EUA price model by using Crank-Nicolson finite difference method. Finally we discuss the relation between crude oil-linked carbon pricing and emission reduction risk. These studies may suggest carbon policy makers take into account of crude oil-linked carbon pricing to tackle low price and low liquidity issues of carbon assets.

ABSTRACT. We present a simple real options model that illustrates how changes in uncertainty can result in changes in equilibrium investment costs rather than in changes in investment levels. To empirically test the model, we examine a panel of oil rig rental rates. Our empirical analysis confirms that after we control for several relevant economic variables, price uncertainty negatively affects rig rates.

ABSTRACT. The paper studies the effects of nonlinear financial intermediary asset pricing in the oil futures market, using Markov switching in heteroskedasticity structural vector autoregressions. The empirical model is identified through theory-implied restrictions and allows for changes across regimes in both impact effects and volatility. The results suggest that the demand curve of financial intermediaries steepens significantly during turbulent times. This amplifies the price effects of all traders' demand shocks by two thirds. Additionally, the volatility of intermediaries' own demand shocks doubles during these episodes, further increasing volatility. These findings are consistent with the hypothesis that binding capital constraints of intermediaries are related to volatility and imply nonlinear trading behavior. Finally, we show that interest rates are the best indicators of entering turbulent times.

ABSTRACT. In this paper, we introduce the concept of statistical arbitrage through the definition of a trading strategy that captures persistent anomalies in long-run relationships among assets. We devise a methodology to individuate and test mean-reverting statistical arbitrage, and to develop trading strategies. Then, we empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium, and mispricings arise when West Texas Intermediate crude oil price diverges from the statistical portfolio value. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is measured by three specific profit indicators on out-of-sample data. Finally, we use a Monte Carlo simulation approach to develop a model for forecasting the expected Value at Risk (VaR) of the adopted trading strategy over an established holding period.