Showing papers on "Spot contract published in 2012"

The Role of Speculation in Oil Markets: What Have We Learned so Far?

Abstract: A popular view is that the surge in the price of oil during 2003-08 cannot be explained by economic fundamentals, but was caused by the increased financialization of oil futures markets, which in turn allowed speculation to become a major determinant of the spot price of oil. This interpretation has been driving policy efforts to regulate oil futures markets. This survey reviews the evidence supporting this view. We identify six strands in the literature corresponding to different empirical methodologies and discuss to what extent each approach sheds light on the role of speculation. We find that the existing evidence is not supportive of an important role of speculation in driving the spot price of oil after 2003. Instead, there is strong evidence that the co-movement between spot and futures prices reflects common economic fundamentals rather than the financialization of oil futures markets.

Monetary Cost-Aware Checkpointing and Migration on Amazon Cloud Spot Instances

Abstract: Recently introduced spot instances in the Amazon Elastic Compute Cloud (EC2) offer low resource costs in exchange for reduced reliability; these instances can be revoked abruptly due to price and demand fluctuations. Mechanisms and tools that deal with the cost-reliability tradeoffs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. We study how mechanisms, namely, checkpointing and migration, can be used to minimize the cost and volatility of resource provisioning. Based on the real price history of EC2 spot instances, we compare several adaptive checkpointing schemes in terms of monetary costs and improvement of job completion times. We evaluate schemes that apply predictive methods for spot prices. Furthermore, we also study how work migration can improve task completion in the midst of failures while maintaining low monetary costs. Trace-based simulations show that our schemes can reduce significantly both monetary costs and task completion times of computation on spot instance.

Optimal bidding in spot instance market

Abstract: Amazon introduced Spot Instance Market to utilize the idle resources of Amazon Elastic Compute Cloud (EC2) more efficiently. The price of a spot instance changes dynamically according to the current supply and demand for cloud resources. Users can bid for a spot instance and the job request will be granted if the current spot price falls below the bid, whereas the job will be terminated if the spot price exceeds the bid. In this paper, we investigate the problem of designing a bidding strategy from a cloud service broker's perspective, where the cloud service broker accepts job requests from cloud users, and leverages the opportunistic yet less expensive spot instances for computation in order to maximize its own profit. In this context, we propose a profit aware dynamic bidding (PADB) algorithm, which observes the current spot price and selects the bid adaptively to maximize the time average profit of the cloud service broker. We show that our bidding strategy achieves a near-optimal solution, i.e., (1−∈) of the optimal solution to the profit maximization problem, where ∈ can be arbitrarily small. The proposed dynamic bidding algorithm is self-adaptive and requires no a priori statistical knowledge on the distribution of random job sizes from cloud users.

Operation of concentrating solar power plants with storage in spot electricity markets

Abstract: Concentrating solar power (CSP) is a promising technology, which will most likely develop in some parts of the world in the near future. It is already being exploited in certain countries, such as the USA and Spain, where subsidy policies are granted to support its development. One advantage of this technology is the possibility of storing the received thermal energy, and using it later. This storage allows CSP plants to smooth the electricity production and to schedule it independently of the instantaneous solar resource. In this way, these plants may be considered as dispatchable, and therefore they can be more easily and efficiently integrated into the power grid. In an electricity market, this means fitting the production as much as possible to the spot price, which sets the system needs of energy. In this way, shifting part of its production to hours with more demand and higher prices maximises the revenues of the plant. This study proposes an optimisation method that aims to maximise CSP plant revenues by taking into account daily electricity prices. The model is independent of the technology used. The performed simulation cases with this model show that current Spanish subsidy policies reduce the incentive for the full use of CSP plant dispatchability.

Optimal Bids for Spot VMs in a Cloud for Deadline Constrained Jobs

Abstract: Spot virtual-machine (VM) instances, such as Amazon EC2 Spot VMs, are a class of VMs that are purchased through a market mechanism of price-bids submitted by cloud users. Spot VMs can be obtained at substantially lower cost than other VM classes such as Reserved and On-demand instances, but they do not have guaranteed availability since it depends on the submitted price bids and the fluctuating spot VM price. Many applications with large computing requirements but no real-time availability constraints, such as scientific computing, financial modelling and large data analysis, can be carried out at a significantly lower cost using spot VMs. For such jobs, an important question that arises is what should the submitted price bids be so that the computation is completed within a fixed time interval while the cost is minimized. Towards this goal, we model a job as a fixed computation request with a deadline constraint and formulate the problem of designing a dynamic bidding policy that minimizes the average cost of job completion. We obtain analytical and closed-form results for the optimal strategy under a Markov spot price evolution, and then evaluate the performance of the algorithms on the actual spot price history of Amazon EC2 Spot VMs.

Modelling energy spot prices by volatility modulated Lévy-driven Volterra processes

Abstract: This paper introduces the class of volatility modulated Levy-driven Volterra (VMLV) processes and their important subclass of Levy semistationary (LSS) processes as a new framework for modelling energy spot prices. The main modelling idea consists of four principles: First, deseasonalised spot prices can be modelled directly in stationarity. Second, stochastic volatility is regarded as a key factor for modelling energy spot prices. Third, the model allows for the possibility of jumps and extreme spikes and, lastly, it features great flexibility in terms of modelling the autocorrelation structure and the Samuelson effect. We provide a detailed analysis of the probabilistic properties of VMLV processes and show how they can capture many stylised facts of energy markets. Further, we derive forward prices based on our new spot price models and discuss option pricing. An empirical example based on electricity spot prices from the European Energy Exchange confirms the practical relevance of our new modelling framework.

Efficient estimation of Markov regime-switching models: An application to electricity spot prices

Abstract: In this paper we discuss the calibration of models built on mean-reverting processes combined with Markov regime-switching (MRS). We propose a method that greatly reduces the computational burden induced by the introduction of independent regimes and perform a simulation study to test its efficiency. Our method allows for a 100 to over 1000 times faster calibration than in case of a competing approach utilizing probabilities of the last 10 observations. It is also more general and admits any value of γ in the base regime dynamics. Since the motivation for this research comes from a recent stream of literature in energy economics, we apply the new method to sample series of electricity spot prices from the German EEX and Australian NSW markets. The proposed MRS models fit these datasets well and replicate the major stylized facts of electricity spot price dynamics.

Optimal Resource Rental Planning for Elastic Applications in Cloud Market

Abstract: This paper studies the optimization problem of minimizing resource rental cost for running elastic applications in cloud while meeting application service requirements. Such a problem arises when excessive generated data incurs significant monetary cost on transfer and inventory in cloud. The goal of planning is to make resource rental decisions in response to varying application progress in the most cost-effective way. To address this problem, we first develop a Deterministic Resource Rental Planning (DRRP) model, using a mixed integer linear program, to generate optimal rental decisions given fixed cost parameters. Next, we systematically analyze the predictability of the time-varying spot instance prices in Amazon EC2 and find that the best achievable prediction is insufficient to provide a close approximation to the actual prices. This fact motivates us to propose a Stochastic Resource Rental Planning (SRRP) model that explicitly considers the price uncertainty in rental decision making. Using empirical spot price data sets and realistic cost parameters, we conduct simulations over a wide range of experimental scenarios. Results show that DRRP achieves as much as 50% cost reduction compared to the no-planning scheme. Moreover, SRRP consistently outperforms its DRRP counterpart in terms of cost saving, which demonstrates that SRRP is highly adaptive to the unpredictable nature of spot price in cloud resource market.

Financial optimization in the Turkish electricity market: Markowitz's mean-variance approach

Abstract: Electricity constitutes the input into many products that produced by industry and used by people. Hence, it can be considered as a product or service that has vital importance in human life and economy. Since it has such special properties of instantaneous production and consumption obligation and unfeasible storage, electricity market is not like other markets. In a competitive electricity market, generation company faces price risks and delivery risks. So that risk management is an important part of a generation company and can deeply effect companies’ profitability. This paper focuses on electricity generation asset allocation between bilateral contracts, such as forward contracts, and daily spot market, considering constraints of generating units and spot price risks. The problem is to find the optimal portfolio based on known electricity generation total costs, bilateral contract prices, it employed Turkish historical balanced market hourly system marginal and day-ahead hourly market prices between of 2006 and 2011. There are limited studies about portfolio optimization in electricity markets in literature and this paper should be considered frontier study taking spot market's hourly prices separately as risky assets. Markowitz mean-variance optimization which is claimed to be the beginning of modern portfolio theory in financial sector is used to demonstrate this approach. Mean-variance optimization has been successfully applied to all cases that modeled for electricity market. Some suggestions for future work are also listed in this paper.

Does Futures Speculation Destabilize Spot Prices? New Evidence for Commodity Markets

Abstract: Motivated by repeated price spikes and crashes over the last decade, we investigate whether the rapidly growing market shares of futures speculators have destabilized commodity spot prices. We approximate conditional volatility and regress it on expected and unexpected speculative open interest. In this context, we split our sample into two equally long sub-periods, and document whether the speculative impact on conditional volatility has increased. However, with respect to six heavily traded agricultural and energy commodities, we find no evidence that this is the case. We thus conclude that the increasing financialization of raw material markets has not made them more volatile.

Market efficiency and lead-lag relationships between spot, futures and forward prices: the case of the Iberian Electricity Market (MIBEL)

Abstract: This paper analyzes the relationships between prices from three different markets within the Spanish zone of the Iberian Electricity Market (MIBEL), namely futures, spot and OTC forward markets. The analysis focuses on three aspects: (i) contrasting the weak efficient hypothesis, (ii) analyzing the simple efficiency of the futures market and the short-term causality between the proxy of the spot and futures prices, and (iii) examining the price discovery relationships between the involved series of prices. The empirical results confirm that MIBEL (both spot and futures) prices satisfy the weak efficient hypothesis. As well, the MIBEL futures market is efficient in the simple sense and there is unidirectional short-term causality from the futures price to the proxy of the spot price. Lastly, price discovery relationships are also found. In particular, there is unidirectional causality from the futures market to the forward market and to the spot market for 1-month- and 1-quarter-ahead maturities.

Competition and price asymmetries in the Greek oil sector: an empirical analysis on gasoline market

Abstract: This article attempts to investigate the issue of asymmetries in the transmission of shocks to input prices and exchange rate onto the wholesale and retail price of gasoline respectively. For this purpose, we utilise the error-correction methodology in the Greek gasoline market. The sample consists of monthly data covering the period of January 1988 to June 2006. We also try to analyse by using impulse response functions the effect of competition on the dynamic adjustment of gasoline price to which has been paid scant attention in the past. The results favour the common perception that retail gasoline prices respond asymmetrically to cost increases and decreases both in the long and the short-run. At the wholesale segment, there is a symmetric response of the spot prices of gasoline towards the adjustment to the short-run responses of the exchange rate. Lastly, after the deregulation, wholesale prices of gasoline tend to gradually restore equilibrium triggered by a price shock compared to the regulated period.

Leverage vs. Feedback: Which Effect Drives the Oil Market?

Abstract: This article brings new insights on the role played by (implied) volatility on the WTI crude oil spot price. An increase in the volatility subsequent to an increase in the oil price (i.e. inverse leverage effect) remains the dominant effect as it might reflect the fear of oil consumers to face rising oil prices. However, this effect is amplified by an increase in the oil price subsequent to an increase in the volatility (i.e. inverse feedback effect) with a two-day delayed effect. This lead-lag relation between the oil price and its volatility is determinant for any type of trading strategy based on futures and options on the OVX implied volatility index, and thus is of interest to traders, risk- and fund-managers.

Forecasting spot rates at main routes in the dry bulk market

Abstract: The dry bulk shipping market is a major component of the international shipping market and it is characterized by high risk and volatility, in view of the uncertainty caused by factors such as the global economy, the volume and pattern of seaborne trade, and government policies. In such markets, to model price behavior (of spot- or time charter rates) has always been a topic of great interest among researchers. This article makes an attempt to forecast spot rates at main routes for three types of dry bulk vessels and to find superior forecasting models that can provide better forecasts. In this article, 1-month change in the Baltic Index, representing the market sentiment, is firstly invented and incorporated into the forecasting models, and this indicator is found to be very helpful in improving prediction performance. Furthermore, some significant exogenous variables are also employed to improve forecasting performance. The results of the cointegration test reveal that there are no long-run relationships of spot prices between trading routes for all three ship sizes. Hence, except a vector error correction model, time series models, such as the ARIMA, ARIMAX, VAR and VARX, are employed in this article to make the prediction. All spot prices cover the period from January 1990 to December 2010, which is split into an estimation period and an out-of-sample forecasting period. In order to test whether the market since 2003 is significantly different from the market before, the in-sample estimation is made over two sample periods. Various models are estimated firstly over the whole period from January 1990 to June 2009, and then estimated again over the second period from January 2003 to June 2009 at all routes for three ship sizes. The period from July 2009 to December 2010 is then used to evaluate independent out-of-sample forecasts. The forecasting performance of various forecasting models is evaluated and the comparison of the forecasting capabilities between various models provides useful information in the selection of superior forecasting models, which can yield better forecasting results.

Can Agent-Based Models Forecast Spot Prices in Electricity Markets? Evidence from the New Zealand Electricity Market

Abstract: Modelling price formation in electricity markets is a notoriously difficult process, due to physical constraints on electricity generation and flow. This difficulty has inspired the recent development of bottom-up agent-based models of electricity markets. While these have proven quite successful in small models, few authors have attempted any validation of their model against real-world data in a more realistic model. In this paper, we take one of the most promising algorithms, the modified Roth and Erev algorithm, and apply it to a 19-node simplification of the New Zealand electricity market. Once key variables such as water storage are accounted for, we show that our model can mimic short-run (weekly) electricity prices at these 19 key nodes quite closely.