Spot contract

About: Spot contract is a research topic. Over the lifetime, 3437 publications have been published within this topic receiving 91599 citations.

A fuzzy-neural approach to electricity load and spot-price forecasting in a deregulated electricity market

Abstract: Accurate short term load forecasting is crucial to the efficient and economic operation of modem electrical power systems. With the recent effort by many governments in the development of open and deregulated power markets, research in forecasting methods is getting renewed attention. Although long term and short term electric load forecasting has been of interest to the practicing engineers and researchers for many years, spot-price prediction is a relatively new research area. This paper examines the use of a neural-fuzzy inference method for the prediction of 24 hourly load and spot price for the next day. Publicly available data of the electricity market of the state of New South Wales, Australia is used in a case study.

Strategic default in the international coffee market

Abstract: This article studies strategic default on forward sale contracts in the international coffee market. To test for strategic default, we construct contract-specific measures of unanticipated changes in market conditions by comparing spot prices at maturity with the relevant futures prices at the contracting date. Unanticipated rises in market prices increase defaults on fixed-price contracts but not on price-indexed ones. We isolate strategic default by focusing on unanticipated rises at the time of delivery after production decisions are sunk and suppliers have been paid. Estimates suggest that roughly half of the observed defaults are strategic. We model how strategic default introduces a trade-off between insurance and counterparty risk: relative to indexed contracts, fixed-price contracts insure against price swings but create incentives to default when market conditions change. A model calibration suggests that the possibility of strategic default causes 15.8% average losses in output, significant dispersion in the marginal product of capital, and sizable negative externalities on supplying farmers.

Amazon EC2 Spot Price Prediction Using Regression Random Forests

Abstract: Spot instances were introduced by Amazon EC2 in December 2009 to sell its spare capacity through auction based market mechanism. Despite its extremely low prices, cloud spot market has low utilization. Spot pricing being dynamic, spot instances are prone to out-of bid failure. Bidding complexity is another reason why users today still fear using spot instances. This work aims to present Regression Random Forests (RRFs) model to predict one-week-ahead and one-day-ahead spot prices. The prediction would assist cloud users to plan in advance when to acquire spot instances, estimate execution costs, and also assist them in bid decision making to minimize execution costs and out-of-bid failure probability. Simulations with 12 months real Amazon EC2 spot history traces to forecast future spot prices show the effectiveness of the proposed technique. Comparison of RRFs based spot price forecasts with existing non-parametric machine learning models reveal that RRFs based forecast accuracy outperforms other models. We measure predictive accuracy using MAPE, MCPE, OOB Error and speed. Evaluation results show that $MAPE M A P E = 10 % for 66 to 92 percent and $MCPE M C P E = 15 % for 35 to 81 percent of one-day-ahead predictions with prediction time less than one second. $MAPE M A P E = 15 % for 71 to 96 percent of one-week-ahead predictions.

Price discovery function of forward contracts in the real estate market: an empirical test

Abstract: Studies on the pricing of financial forward contracts are abundant, and massively outnumber those on the pricing of real forward contracts due to the scarcity of data in the real forward contracts market. In addition, most real forward contracts markets are thinly transacted and heterogeneous in nature. The property pre-sales market is a major real forward contract market in Hong Kong that has been actively transacted. The large volume of data in the Hong Kong property pre-sales market allows us to construct and test a forward contracts pricing model for properties. Despite the relative higher information cost in the real forward contracts compared to financial future contracts, we found that uncompleted properties in the pre-sales market are efficiently priced and accurately reflect the spot price level and the discount due to rental income forgone during the preoccupation period. We also found that the expected spot price derived from our forward pricing model tracks the ex post spot price closely.