Showing papers on "Spot contract published in 2017"

Purchase Bidding Strategy for a Retailer With Flexible Demands in Day-Ahead Electricity Market

Abstract: The paper aims to determine the day-ahead market bidding strategies for retailers with flexible demands to maximize the short-term profit. It proposes a short-term planning framework to forecast the load under dynamic tariffs and construct biding curves. Stochastic programming is applied to manage the uncertainties of spot price, regulating price, consumption behaviors, and responsiveness to dynamic tariffs. A case study based on data from Sweden is carried out. It demonstrates that a real-time selling price can affect the aggregate load of a residential consumer group and lead to load shift toward low-price periods. The optimal bidding curves for specific trading periods are illustrated. Through comparing the bidding strategies under different risk factors, the case study shows that a risk-averse retailer tends to adopt the strategies with larger imbalances. The benefit lies in the reduction of low-profit risk. However, the aversion to risk can only be kept in a certain level. A larger imbalance may lead to a quick reduction of profit in all scenarios.

Capacity Management in Agricultural Commodity Processing and Application in the Palm Industry

Abstract: This paper studies the capacity investment decisions of an agri-processor that uses a commodity input to produce a commodity output and a byproduct. Using a multiperiod model, we study the one-time processing and (output) storage capacity investment decisions, and the periodic processing and inventory decisions in the presence of input and output spot price uncertainties and uncertain production yield. We identify three capacity investment strategies, investing in storage-dominating, processingdominating or mixed portfolio, and provide conditions under which each strategy is optimal. Using a calibration based on the palm industry, we provide rules of thumb for capacity management: The processor should decrease its processing capacity with an increase in price correlation; and with an increase (a decrease) in input or output price volatility when this volatility is low (high). The storage capacity should be adjusted in a similar fashion only as a response to a change in output price volatility, otherwise it should not be altered. We nd that not accounting for the byproduct revenue or inventory holding possibility in capacity planning leads to sizeable prot loss. Ignoring production yield uncertainty has a signicant negative impact on protability if the capacity planning is made based on the maximum yield possible, as often done in practice; but it has an insignicant impact if the planning is made based on the average yield.

HotSpot: automated server hopping in cloud spot markets

Abstract: Cloud spot markets offer virtual machines (VMs) for a dynamic price that is much lower than the fixed price of on-demand VMs. In exchange, spot VMs expose applications to multiple forms of risk, including price risk, or the risk that a VM's price will increase relative to others. Since spot prices vary continuously across hundreds of different types of VMs, flexible applications can mitigate price risk by moving to the VM that currently offers the lowest cost. To enable this flexibility, we present HotSpot, a resource container that "hops" VMs---by dynamically selecting and self-migrating to new VMs---as spot prices change. HotSpot containers define a migration policy that lowers cost by determining when to hop VMs based on the transaction costs (from vacating a VM early and briefly double paying for it) and benefits (the expected cost savings). As a side effect of migrating to minimize cost, HotSpot is also able to reduce the number of revocations without degrading performance. HotSpot is simple and transparent: since it operates at the systems-level on each host VM, users need only run an HotSpot-enabled VM image to use it. We implement a HotSpot prototype on EC2, and evaluate it using job traces from a production Google cluster. We then compare HotSpot to using on-demand VMs and spot VMs (with and without fault-tolerance) in EC2, and show that it is able to lower cost and reduce the number of revocations without degrading performance.

Stable modeling of different European power markets

Abstract: In this paper, we address the issue of modeling spot prices of different European power markets. With the German, Nordic and Polish power markets, we consider three markets at a very different stage of liberalization. After summarizing the stylized facts about spot electricity prices, we provide a comparison of the considered markets in terms of price behavior. We find that there are striking differences: while for the Nordic and German power exchange prices show heavy tails, spikes, high volatility and heteroscedasticity, returns of spot prices in the Polish market can be modeled adequately by the Gaussian distribution. We introduce the stable Paretian distribution to capture heavy tails, high kurtosis and asymmetries in electricity spot prices. We further provide ARMA/GARCH time series models with Gaussian and stable innovations for modeling the behavior of the different markets.

Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

Abstract: Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

System and method executed on a blockchain network

Abstract: A blockchain network includes first, second, third computers and a server computer at first, second, third and fourth blockchain nodes, respectively. An initial state is processed with a service provider computer by entering a spot rate of a price of a first currency relative to a price of second currency on the blockchain. A trade entry is processed with the first market participant computer by entering contract terms for a contract. The first and second market participant computers process first and second trade affirmations by entering an affirmation of the contract terms. A mark to market is processed with the service provider computer by entering a mark to market rate. All the blockchain nodes validate a signature and contract value received from the service provider computer. A settlement is processed and balances of first and second market participants are updated on the blockchain nodes.

Option Market Characteristics and Price Monotonicity Violations

Abstract: This study reexamines whether option price monotonicity properties hold in a liquid market with little market friction and considers the validity of the monotonicity properties in light of option market characteristics. We confirm that option prices do not monotonically correlate with underlying spot prices and that call and put prices often increase or decrease together, indicating that the monotonicity properties are not consistent with the observed option price dynamics. The violations of monotonicity properties are associated with not only market microstructure effects, trade sizes, and option leverage but also other market characteristics such as trade direction, individual investor demand, foreign investor trading, and market volatility. Violation occurrences tend to be clustered, and their relationship with option market characteristics has not been affected by the recent market reform. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark

Supplier Competition with Option Contracts for Discrete Blocks of Capacity

Abstract: When a firm faces an uncertain demand, it is common to procure supply using some type of option in addition to spot purchases. A typical version of this problem involves capacity being purchased in advance, with a separate payment made that applies only to the part of the capacity that is needed. We consider a discrete version of this problem in which competing suppliers choose a reservation price and an execution price for blocks of capacity, and the buyer, facing known distributions of demand and spot price, needs to decide which blocks to reserve. We show how to solve the buyer’s (combinatorial) problem efficiently and also show that suppliers can do no better than offer blocks at execution prices that match their costs, making profits only from the reservation part of their bids. Finally we show that in an equilibrium the buyer selects the welfare maximizing set of blocks. The online appendix is available at https://doi.org/10.1287/opre.2017.1593.

Service Refusals in Supply Chains: Drivers and Deterrents of Freight Rejection

Abstract: Contracts in the for-hire trucking industry are unusual in that, although they establish prices for different services, there is typically no legally binding obligation or penalty for either party to offer or accept a load. When a load is rejected by all contract carriers, shippers must turn to the spot market, which can significantly increase supply chain costs. Because these transactions occur between private parties, data on load acceptances/rejections and contract/spot prices have not been available to academic researchers, leaving the freight rejection problem largely unexplored. We are able to examine this problem using a detailed transactional data set of a large national shipper. We estimate that spot prices for truckload services average about 62% higher than contract rates. We find key operational and economic factors to be drivers of freight rejection and the shipper-carrier relationship to be a deterrent to freight rejection. We also find that primary and secondary carriers respond differently...

Volatility spillovers and causality of carbon emissions, oil and coal spot and futures for the EU and USA

Abstract: Recent research shows that the efforts to limit climate change should focus on reducing the emissions of carbon dioxide over other greenhouse gases or air pollutants. Many countries are paying substantial attention to carbon emissions to improve air quality and public health. The largest source of carbon emissions from human activities in some countries in Europe and elsewhere is from burning fossil fuels for electricity, heat, and transportation. The prices of fuel and carbon emissions can influence each other. Owing to the importance of carbon emissions and their connection to fossil fuels, and the possibility of [1] Granger (1980) causality in spot and futures prices, returns, and volatility of carbon emissions, crude oil and coal have recently become very important research topics. For the USA, daily spot and futures prices are available for crude oil and coal, but there are no daily futures prices for carbon emissions. For the European Union (EU), there are no daily spot prices for coal or carbon emissions, but there are daily futures prices for crude oil, coal and carbon emissions. For this reason, daily prices will be used to analyse Granger causality and volatility spillovers in spot and futures prices of carbon emissions, crude oil, and coal. As the estimators are based on quasi-maximum likelihood estimators (QMLE) under the incorrect assumption of a normal distribution, we modify the likelihood ratio (LR) test to a quasi-likelihood ratio test (QLR) to test the multivariate conditional volatility Diagonal BEKK model, which estimates and tests volatility spillovers, and has valid regularity conditions and asymptotic properties, against the alternative Full BEKK model, which also estimates volatility spillovers, but has valid regularity conditions and asymptotic properties only under the null hypothesis of zero off-diagonal elements. Dynamic hedging strategies by using optimal hedge ratios are suggested to analyse market fluctuations in the spot and futures returns and volatility of carbon emissions, crude oil, and coal prices.

Performance Analysis of Four Decomposition-Ensemble Models for One-Day-Ahead Agricultural Commodity Futures Price Forecasting

Abstract: Agricultural commodity futures prices play a significant role in the change tendency of these spot prices and the supply–demand relationship of global agricultural product markets. Due to the nonlinear and nonstationary nature of this kind of time series data, it is inevitable for price forecasting research to take this nature into consideration. Therefore, we aim to enrich the existing research literature and offer a new way of thinking about forecasting agricultural commodity futures prices, so that four hybrid models are proposed based on the back propagation neural network (BPNN) optimized by the particle swarm optimization (PSO) algorithm and four decomposition methods: empirical mode decomposition (EMD), wavelet packet transform (WPT), intrinsic time-scale decomposition (ITD) and variational mode decomposition (VMD). In order to verify the applicability and validity of these hybrid models, we select three futures prices of wheat, corn and soybean to conduct the experiment. The experimental results show that (1) all the hybrid models combined with decomposition technique have a better performance than the single PSO–BPNN model; (2) VMD contributes the most in improving the forecasting ability of the PSO–BPNN model, while WPT ranks second; (3) ITD performs better than EMD in both cases of corn and soybean; and (4) the proposed models perform well in the forecasting of agricultural commodity futures prices.

Multivariate modeling and analysis of regional ocean freight rates

Abstract: In this paper, we propose a new multivariate model for the dynamics of regional ocean freight rates We show that a cointegrated system of regional spot freight rates can be decomposed into a common non-stationary market factor and stationary regional deviations The resulting integrated CAR process is new to the literature By interpreting the common market factor as the global arithmetic average of the regional rates, both the market factor and the regional deviations are observable which simplifies the calibration of the model Moreover, forward contracts on the market factor can be traded in the Forward Freight Agreement (FFA) market We calibrate the model to historical spot rate processes and illustrate the term structures of volatility and correlation between the regional prices and the market factor Our model is an important contribution towards improved modelling and hedging of regional price risk when derivative market liquidity is concentrated in a single global benchmark

On Estimating Minimum Bids for Amazon EC2 Spot Instances

Abstract: Consumers can realize significant cost savings by procuring resources from computational spot markets such as Amazon Elastic Compute Cloud (EC2) Spot Instances. They can take advantage of the price differentials across time slots, regions, and instance types to minimize the total cost of running their applications on the cloud. However, Spot markets are inherently volatile and dynamic, as a consequence of which Spot prices change continuously. As such, prospective bidders can benefit from intelligent insights into the Spot market dynamics that can help them make more informed bidding decisions. To enable this, we propose a descriptive statistics approach for the analysis of Amazon EC2 Spot markets to detect typical pricing patterns including the presence of seasonal components, extremes and trends. We use three statistical measures -- the Gini coefficient, the Theil index, and the exponential weighted moving average. We also devise a model for estimating minimum bids such that the Spot instances will run for specified durations with a probability greater than a set value based on different look back periods. Experimental results show that our estimation yields on average a bidding strategy that can reliably secure an instance at least 80% of the time at minimum target guarantee between 50% and 95%.

Financialization in Commodity Markets

Abstract: The financialization view is that increased trading in commodity futures markets is associated with increases in the growth rate and volatility of commodity spot prices. This view gained credence because in the 2000s trading volume increased sharply and many commodity prices rose and became more volatile. Using a large panel dataset we constructed, which includes commodities with and without futures markets, we find no empirical link between increased futures market trading and changes in price behavior. Our data sheds light on the economic role of futures markets. The conventional view is that futures markets provide one-way insurance by allowing outsiders, traders with no direct interest in a commodity, to insure insiders, traders with a direct interest. The data are not consistent with the conventional view and we argue that they point to an alternative mutual insurance view, in which all participants insure each other. We formalize this view in a model and show that it is consistent with key features of the data.

CO2 Emission Allowances Trading in Europe - Specifying a new Class of Assets

Abstract: In the context of controlling greenhouse gas emissions, the directive on an EU-wide trading scheme for carbon dioxide (CO2) emission allowances may be considered as one of the major steps towards reducing environmental burden. A major question for market participants will be about regarding the price behavior of this new environmental asset. Due to non-maturity of the market, political regulations, fundamentals and certain characteristics of CO2 allowances it can be assumed that parameters for the price process or even the process itself changes through time. In this paper we review the stylized facts of emission allowances and come up with suggestions to model their price behavior adequately. We conduct a preliminary empirical analysis of CO2 allowance spot prices supporting our theoretical findings.

Space-time modeling of electricity spot prices

Abstract: In this paper we derive a space-time model for electricity spot prices. A general spatial Durbin model that incorporates the temporal as well as spatial lags of spot prices is presented. Joint modeling of space-time effects is necessarily important when prices and loads are determined in a network of power exchange areas. We use data from the Nord Pool electricity power exchange area bidding markets. Different spatial weight matrices are considered to capture the structure of the spatial dependence process across different bidding markets and statistical tests show significant spatial dependence in the spot price dynamics. Estimation of the spatial Durbin model show that the spatial lag variable is as important as the temporal lag variable in describing the spot price dynamics. We use the partial derivatives impact approach to decompose the price impacts into direct and indirect effects and we show that price effects transmit to neighboring markets and decline with distance. In order to examine the evolution of the spatial correlation over time, a time varying parameters spot price spatial Durbin model is estimated using recursive estimation. It is found that the spatial correlation within the Nord Pool grid has been increasing over time which we interpret as evidence for an increasing degree of market integration.

Mean-risk analysis of wholesale price contracts with stochastic price-dependent demand

Abstract: Given that risk is a pertinent issue in designing supply chain contracts with stochastic demand, Chap. 3 is devoted to developing a mean-risk analysis for the commonly adopted wholesale price contract. The research incorporates contract value risk into the wholesale price contract model. Regarding the contract value risk, it actually relates to the uncertainty in the true value of the contract and arises from various uncertainty sources inherent in the supply chain, such as demand uncertainty, price uncertainty, etc. In addition, given that the supply chain agents with different risk preferences will have different risk attitudes towards the contract value risk, which in turn affects their contracting decisions, the research also considers the degree of supply chain agents risk-aversion towards the contract value risk. This chapter makes the first attempt to assess the efficiency of wholesale price contracts, incorporating contract value risk and risk preferences attached to it; thereby some interesting managerial and academic insights are generated for supply chain contracts.

Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks

Abstract: Heating oil is an extremely important heating fuel to consumers in northeastern United States. This paper studies the fluctuations law and dynamic behavior of heating oil spot and futures prices by setting up their complex network models based on the data of America in recent 30 years. Firstly, modes are defined by the method of coarse graining, the spot price fluctuation network of heating oil (HSPFN) and its futures price fluctuation network (HFPFN) in different periods are established to analyze the transformation characteristics between the modes. Secondly, several indicators are investigated: average path length, node strength and strength distribution, betweeness, etc. In addition, a function is established to measure and analyze the network similarity. The results show the cumulative time of new nodes appearing in either spot or futures price network is not random but exhibits a growth trend of straight line. Meanwhile, the power law distributions of spot and futures price fluctuations in different periods present regularity and complexity. Moreover, these prices are strongly correlated in stable fluctuation period but weak in the phase of sharp fluctuation. Finally, the time distribution characteristics of important modes in the networks and the evolution results of the topological properties mentioned above are obtained.