Spot contract

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

Manipulation of Cash‐Settled Futures Contracts

Abstract: Replacement of delivery settlement of futures contracts with cash settlement is frequently proposed to reduce the frequency of market manipulation. This article shows that it is always possible to design a delivery-settled futures contract that is less susceptible to cornering by a large long than any given cash-settled contract. Such a contract is more susceptible to manipulation by large shorts, however. Therefore, cash settlement does not uniformly dominate delivery settlement as a means of reducing the frequency of market power manipulations in derivatives markets. The efficient choice of settlement mechanism depends on whether supply and demand conditions favor short or long manipulations. Copyright 2001 by University of Chicago Press.

Revenue Management of a Make-to-Stock Queue

Abstract: Motivated by recent electronic marketplaces, we consider a single-product make-to-stock manufacturing system that uses two alternative selling channels: long-term contracts and a spot market of electronic orders. At time 0, the risk-averse manufacturer selects the long-term contract price, at which point buyers choose one of the two channels. The resulting long-term contract demand is a deterministic fluid, while the spot-market demand is modeled as a stochastic renewal process. An exponential reflected random walk model is used to model the spot-market price, which is correlated with the spot-market demand process. The manufacturer accepts or rejects each electronic order, and long-term contracts and accepted electronic orders are backordered if necessary. The manufacturer's control problem is to select the optimal long-term contract price as well as the optimal production (i.e., busy/idle) and electronic-order admission policies to maximize revenue minus inventory holding and backorder costs. Under heavy-traffic conditions, the problem is approximated by a diffusion-control problem, and analytical approximations are used to derive a policy that is simple, and reasonably accurate and robust.

VaR constrained hedging of fixed price load-following obligations in competitive electricity markets

Abstract: Load serving entities providing electricity to regulated customers have an obligation to serve load that is subject to systematic and random fluctuations at fixed prices. In some jurisdictions like New Jersey, such obligations are auctioned off annually to third parties that commit to serve a fixed percentage of the fluctuating load at a fixed energy price. In either case the entity holding the load following obligation is exposed to the load variation and to a volatile wholesale spot market price which is correlated with the load level. Such double exposure to price and volume results in a net revenue exposure that is quadratic in price and cannot be adequately hedged with simple forward contracts whose payoff is linear in price. A fixed quantity forward contract cover, is likely to be short when the spot price is high and long when the spot price is low. In this paper we develop a self-financed hedging portfolio consisting of a risk free bond, a forward contract and a spectrum of call and put options with different strike prices. A popular portfolio design criterion is the maximization of expected hedged profits subject to a value at risk (VaR) constraint. Unfortunately, that criteria is difficult to implement directly due to the complicated form of the VaR constraint. We show, however, that under plausible distributional assumptions, the optimal VaR constrained portfolio is on the efficient mean-variance frontier. Hence, we propose an approximation method that restricts the search for the optimal VaR constrained portfolio to that efficient frontier. The proposed approach is particularly attractive when the mean-variance efficient frontier can be represented analytically, as is the case, when the load and logarithm of price follow a bivariate normal distribution. We illustrate the results with a numerical example.