Showing papers on "Spot contract published in 2015"

Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage

Abstract: We examine two models of commodity futures prices. The theory of storage explains the difference between contemporaneous futures and spot prices (the basis) in terms of interest changes, warehousing costs, and convenience yields. We find evidence of variation in the basis in response to both interest rates and seasonals in convenience yields. The second model splits a futures price into an expected premium and a forecast of the maturity spot price. We find evidence of forecast power for 10 of 21 commodities and time-varying expected premiums for five commodities.

A Model of Financialization of Commodities

Abstract: A sharp increase in the popularity of commodity investing in the past decade has triggered an unprecedented inflow of institutional funds into commodity futures markets, referred to as the financialization of commodities. In this paper, we explore the effects of financialization in a model that features institutional investors alongside traditional futures markets participants. The institutional investors care about their performance relative to a commodity index. We find that in the presence of institutional investors prices and volatilities of all commodity futures go up, but more so for the index futures than for nonindex ones. The correlations amongst commodity futures as well as in equity-commodity correlations also increase, with higher increases for index commodities. Within a framework additionally incorporating storage, we show how financial markets transmit shocks not only to futures prices but also to commodity spot prices and inventories. Commodity spot prices and inventories go up with financialization. In the presence of institutional investors shocks to any index commodity spill over to all storable commodity prices.

Informational Frictions and Commodity Markets

Abstract: This paper develops a model with a tractable log-linear equilibrium to analyze the effects of informational frictions in commodity markets. By aggregating dispersed information about the strength of the global economy among goods producers whose production has complementarity, commodity prices serve as price signals to guide producers' production decisions and commodity demand. Our model highlights important feedback effects of informational noise originating from supply shocks and futures market trading on commodity demand and spot prices. Our analysis illustrates the weakness common in empirical studies on commodity markets of assuming that different types of shocks are publicly observable to market participants

How to Bid the Cloud

Abstract: Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Amazon sets the spot price dynamically and accepts user bids above this price. Jobs with lower bids (including those already running) are interrupted and must wait for a lower spot price before resuming. Spot pricing thus raises two basic questions: how might the provider set the price, and what prices should users bid? Computing users' bidding strategies is particularly challenging: higher bid prices reduce the probability of, and thus extra time to recover from, interruptions, but may increase users' cost. We address these questions in three steps: (1) modeling the cloud provider's setting of the spot price and matching the model to historically offered prices, (2) deriving optimal bidding strategies for different job requirements and interruption overheads, and (3) adapting these strategies to MapReduce jobs with master and slave nodes having different interruption overheads. We run our strategies on EC2 for a variety of job sizes and instance types, showing that spot pricing reduces user cost by 90% with a modest increase in completion time compared to on-demand pricing.

Moral hazard in health insurance: do dynamic incentives matter?

Abstract: Using data from employer-provided health insurance and Medicare Part D, we investigate whether healthcare utilization responds to the dynamic incentives created by the nonlinear nature of health insurance contracts. We exploit the fact that, because annual coverage usually resets every January, individuals who join a plan later in the year face the same initial ("spot") price of healthcare but a higher expected end-of-year ("future") price. We find a statistically significant response of initial utilization to the future price, rejecting the null that individuals respond only to the spot price. We discuss implications for analysis of moral hazard in health insurance.

Sharing a Ride on the Commodities Roller Coaster: Common Factors in Business Cycles of Emerging Economies

Abstract: Fluctuations in commodity prices are an important driver of business cycles in small emerging market economies (EMEs). We document how these fluctuations correlate strongly with the business cycle in EMEs. We then embed a commodity sector into a multi-country EMEs’ business cycle model where exogenous fluctuations in commodity prices follow a common dynamic factor structure and coexist with other driving forces. The estimated model assigns to commodity shocks 42 percent of the variance in income, of which a considerable part is linked to the common factor. A further amplification mechanism is a ”spillover” effect from commodity prices to risk premia.

The real option value of mining operations using mean-reverting commodity prices

Abstract: By determining the optimal price threshold of mining activation, this research aims at estimating a mine’s in situ value by incorporating its real option value (ROV). The traditional discounted cash flow (DCF) method, the standard tool for economic feasibility studies in mineral industry, can be problematic since it fails to address uncertainties and operational flexibilities (Trigeorgis Adv Futures Options Res 4:S1537164, 1990; Schwartz J Financ 3:923–973, 1997; Slade J Environ Econ Manag 41:193–233, 2001; Abdel Sabour and Dimitrakopoulos J Min Sci 47(2):191–201, 2011). DCF normally results in under-evaluation when significant price variability is present in commodity prices such as gold, silver, copper, and recently rare earths. A mining project is more valuable in expected value terms if it is activated following an appropriately chosen price threshold. In this work, the commodity price is modeled using a mean-reverting process, which is more relevant to commodity economics than the generally used Geometric Brownian motion process (Pindyck and Rubinfeld 1991). It is shown that the value of flexibility is significant and peaks when mining cost equals spot price; the exercising price threshold increases as average cost rises and probabilities of exercising the option are estimated. ROV method provides a tractable and realistic scheme to evaluate a mine’s in situ value and a strategy to manage mining activities.

Risk averse supply portfolio selection with supply, demand and spot market volatility

Abstract: Enterprise Risk Management (ERM) has become one of the most essential subjects in business management. This paper establishes how risk modeling can be applied to supply chain management, specifically to supply portfolio procurement decisions of a firm. In a single period setting, parts can be procured via traditional forward contracts, option contracts or spot purchases. Customer demand and spot prices are random and possibly correlated and firm׳s primary suppliers are subject to complete disruptions and yield uncertainties. This paper analyzes several scenarios where the spot market is not available, available for buying only, and available for both buying and selling. This article develops and solves mathematical models considering the risk neutral and risk averse (CVaR) objectives independently or simultaneously. For the special case of normally distributed random variables and a risk neutral objective, optimality properties were developed. A broad numerical study examines the sensitivity of procurement strategies to key problem parameters such as, risk attitude, demand and spot price volatilities, correlation between demand and spot prices and terms of option contracts.

Bidding for Highly Available Services with Low Price in Spot Instance Market

Abstract: Amazon EC2 has built the Spot Instance Marketplace and offers a new type of virtual machine instances called as spot instances. These instances are less expensive but considered failure-prone. Despite the underlying hardware status, if the bidding price is lower than the market price, such an instance will be terminated. Distributed systems can be built from the spot instances to reduce the cost while still tolerating instance failures. For example, embarrassingly parallel jobs can use the spot instances by re-executing failed tasks. The bidding framework for such jobs simply selects the spot price as the bid. However, highly available services like lock service or storage service cannot use the similar techniques for availability consideration. The spot instance failure model is different to that of normal instances (fixed failure probability in traditional distributed model). This makes the bidding strategy more complex to keep service availability for such systems. We formalize this problem and propose an availability and cost aware bidding framework. Experiment results show that our bidding framework can reduce the costs of a distributed lock service and a distributed storage service by 81.23% and 85.32% respectively while still keeping availability level the same as it is by using on-demand instances.

Dynamic Price Prediction for Amazon Spot Instances

Abstract: Variable pricing cloud resources are the most recent advancement in cloud computing business models. Cloud vendors like Amazon Web Services, a.k.a. Amazon AWS provide a new cloud instance type known as "Spot instance". The distinguishing feature of spot instance is its dynamic pricing. The price of spot instances varies dynamically with time based on demand and supply of cloud resources in the data centers across the globe. Customers place bids to obtain spot instances using an online auction platform. The auction platform determines the market clearance price, a.k.a. "Spot price" and the users whose bids are above the aforementioned price obtain the instances. Cloud vendors provide current and archived spot price data to assist their customers in bidding process. The major challenge for the customers in this new business model is to predict the spot price before placing their bids. In this paper, we have provided a novel algorithm for spot price prediction. We also have instantiated and demonstrated the proposed algorithm. The results show high accuracy of 9.4% Mean Absolute Percent Error (MAPE) for short term (one day ahead) and less 20% MAPE for long term (five days ahead) forecasting.

Fractional Price Matching Policies Arising from the Ocean Freight Service Industry

Abstract: We consider a situation in which shippers (customers) can purchase ocean freight services either directly from a carrier (service provider)in advance or from the spot market just before the departure of an ocean liner. The price is known in the former case, while the spot price is uncertain ex-ante in the latter case. Consequently, some shippers are reluctant to book directly from the carrier in advance unless the carrier is willing to “partially match” the realized spot price when it is lower than the regular price. This study is an initial attempt to examine if the carrier should bear some of the “price risk” by offering a “fractional” price matching contract that can be described as follows. The shipper pays the regular freight price in advance; however, the shipper will get a refund if the realized spot price is below the regular price, where the refund is a “fraction” of the difference between the regular price and the realized spot price. By modeling the dynamics between the carrier and the shippers as a sequential game, we show that the carrier can use the fractional price matching contract to generate a higher demand from the shippers compared to no price matching contract by increasing the “fraction” in equilibrium. However, as the carrier increases the “fraction,” the carrier should increase the regular price to compensate for bearing additional risk. By selecting the fractional price matching contract optimally, we show that the carrier can afford to offer this price matching mechanism without incurring revenue loss: the optimal fractional price matching contract is “revenue neutral.”

Electricity Futures Prices in an Emissions Constrained Economy: Evidence from European Power Markets

Abstract: We investigate the economic factors that drive electricity risk premia in the European emissions constrained economy. Our analysis is undertaken for monthly baseload electricity futures for delivery in the Nordic, French and British power markets. We find that electricity risk premia are significantly related to the volatility of electricity spot prices, demand and revenues, and the price volatility of the carbon dioxide (CO2) futures traded under the EU Emissions Trading Scheme (EU ETS). This finding has significant implications for the pricing of electricity futures since it highlights for the first time the role of carbon market uncertainties as a main determinant of the relationship between spot and futures electricity prices in Europe. Our results also suggest that for the electricity markets under scrutiny futures prices are determined rationally by risk-averse economic agents.

The value of information sharing for truckload shippers

Abstract: This study explores the potential value to shippers of sharing load offers with carriers and obtaining carriers’ responses in advance of the scheduled pickup date. Using a private transactional dataset from a large national shipper, we find that truckload spot prices increase considerably as the lead time before pickup decreases. As an extension of this empirical analysis, we develop a method to estimate near-real-time market prices, which does not currently exist in the truckload industry. A key insight is that market prices persist through time, meaning that current prices are good predictors of future prices.

Organization and functioning of liberalized electricity markets: An overview of the Dutch market

Abstract: In this paper, we examine the organization and the functioning of the Dutch electricity market. First we describe the organization of the Dutch electricity supply chain and the role of the main market participants including the transmission system operator, distribution system operators, program responsible parties and metering companies. We then describe the organization of financial trading and clearing mechanism of electricity through the organized futures exchange (The European Energy Derivatives Exchange), and the spot market (Amsterdam Power Exchange) which includes the day-ahead market and intra-day markets. We also detail the functioning of the imbalance market and reserve capacity management in the Netherlands. Through a set of numerical analysis, we provide an exploratory analysis of the APX day-ahead spot prices and the real-time imbalance prices using electricity price data from 2002 to 2013. We observe the price spikes both in the day-ahead and imbalance markets usually occur around 6–10 AM and 5–7 PM. We also observe that in the imbalance market system overages happen significantly more often than shortages pointing out that the market tends to buy more than what is demanded. This could be explained by the risk attitude of the market participants in the imbalance market.

Price Convergence and Information Efficiency in German Natural Gas Markets

Abstract: In 2007, Germany changed network access regulation in the natural gas sector and introduced a so-called entry-exit system. The spot market effects of the reregulation remain to be examined. We use cointegration analysis and a state space model with time-varying coefficients to study the development of natural gas spot prices in the two major trading hubs in Germany and the interlinked spot market in the Netherlands. To analyse information efficiency in more detail, the state space model is extended to an error correction model. Overall, our results suggest a reasonable degree of price convergence between the corresponding hubs. Market efficiency in terms of information processing has increased considerably among Germany and the Netherlands.

Modeling electricity spot prices: combining mean reversion, spikes, and stochastic volatility

Abstract: Starting with the liberalization of electricity trading, this market grew rapidly over the last decade. However, while spot and future markets are rather liquid nowadays, option trading is still limited. One of the potential reasons for this is that the spot price process of electricity is still puzzling researchers and practitioners. In this paper, we propose an approach to model spot prices that combines mean-reversion, spikes and stochastic volatility. Thereby we use dierent mean-reversion rates for "normal" and "extreme" (spike) periods. Another feature of the model is its ability to capture correlation structures of electricity price spikes. Furthermore, all model parameters can easily be estimated with help of historical data. Consequently, we argue that this model does not only extend academic literature on electricity spot price modeling, but is also suitable for practical purposes, e.g. as underlying price model for option pricing.

Understanding the Tracking Errors of Commodity Leveraged ETFs

Abstract: Commodity exchange-traded funds (ETFs) are a significant part of the rapidly growing ETF market They have become popular in recent years as they provide investors access to a great variety of commodities, ranging from precious metals to building materials, and from oil and gas to agricultural products In this article, we analyze the tracking performance of commodity leveraged ETFs and discuss the associated trading strategies It is known that leveraged ETF returns typically deviate from their tracking target over longer holding horizons due to the so-called volatility decay This motivates us to construct a benchmark process that accounts for the volatility decay, and use it to examine the tracking performance of commodity leveraged ETFs From empirical data, we find that many commodity leveraged ETFs underperform significantly against the benchmark, and we quantify such a discrepancy via the novel idea of realized effective fee Finally, we consider a number of trading strategies and examine their performance by backtesting with historical price data

Optimal procurement of long-term contracts in the presence of imperfect spot market

Abstract: B2B spot market has grown rapidly and become an effective trading channel for commodity products. Besides long-term contract procurement from conventional suppliers (forward and option), a buyer can procure or sell commodities at any time in B2B spot market to adjust her inventory level. However, spot prices are generally volatile and the market is imperfect in the sense that spot trading may be realized with uncertainty in a given period of time and often comes with extra transaction cost. This paper considers a commodity buyer who can order forward and option contracts in advance and trade in a B2B spot market when spot price and demand are observed stochastically. Based on a single-period newsvendor model, we discuss three optimal order strategies and derive respective expected profits when the buyer is risk-neutral. The sensitivity of purchase costs, market liquidity and transaction cost is investigated. We also compare the optimal expected profits for different strategies to illustrate the effects of the two long-term contracts in the presence of the B2B spot market. We then extend our model to a multi-period setting and derive the optimal strategy. Finally, we numerically compute the optimal order strategy for a risk-averse buyer and analyze the impact of spot market, risk aversion, as well as the correlation between customer demand and spot price.