Limit price

About: Limit price is a(n) research topic. Over the lifetime, 4865 publication(s) have been published within this topic receiving 148546 citation(s).

Relaxing price competition through product differentiation

Abstract: Central to the problem of providing adequate foundations for the analysis of monopolistic competition, is the problem of describing market equilibria in which firms choose both the specification of their respective products, and their prices. The present paper is concerned with a-very particular-model of such a market equilibrium. In this equilibrium, exactly two potential entrants will choose to enter the industry; they will choose to produce differentiated products; and both will make positive profits.

Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

Abstract: Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.

The price variability-volume relationship on speculative markets

Abstract: This paper concerns the relationship between the variability of the daily price change and the daily volume of trading on the speculative markets. Our work extends the theory of speculative markets in two ways. First, we derive from economic theory the joint probability distribution of the price change and the trading volume over any interval of time within the trading day. And second, we determine how this joint distribution changes as more traders enter (or exit from) the market. The model's parameters are estimated by FIML using daily data from the 90-day T-bills futures market. The results of the estimation can reconcile a conflict between the price variability-volume relationship for this market and the relationship obtained by previous investigators for other speculative markets. THIS PAPER CONCERNS the relationship between the variability of the daily price change and the volume of trading on speculative markets. Previous empirical studies [2, 3, 6, 12, 14, 16] of both futures and equity markets always find a positive association between price variability (as measured by the squared price change Ap2) and the trading volume.2 There are two explanations for the relationship. Clark's [2] explanation, which is secondary to his effort to explain why the probability distribution of the daily price change is leptokurtic, emphasizes randomness in the number of within-day transactions. In Clark's model the daily price change is the sum of a random number of within-day price changes. The variance of the daily price change is thus a random variable with a mean proportional to the mean number of daily transactions. Clark argues that the trading volume is related positively to the number of within-day transactions, and so the trading volume is related positively to the variability of the price change. The second explanation is due to Epps and Epps [6]. Their model examines the mechanics of within-day trading. The change in the market price on each within-day transaction or market clearing is the average of the changes in all of the traders' reservation prices. Epps and Epps assume there is a positive relationship between the extent to which traders disagree when they revise their reservation prices and the absolute value of the change in the market price. That is, an increase in the extent to which traders disagree is associated with a larger absolute price change. The price variability-volume relationship arises, then, because the volume of trading is positively related to the extent to which traders disagree when they revise their reservation prices.

THE DEMAND FOR RISKY ASSETS: Some Extensions

Abstract: Publisher Summary This chapter discusses some extensions on the demand for risky assets. The research on capital asset pricing has until very recently been devoted almost exclusively to the interrelationships of the risk premiums among different risky assets rather than to the determinants of the market price of risk. Such research has also generally relied on theoretical preconceptions to determine the appropriate utility functions of individual investors upon which both the market price of risk and the pricing of individual risky assets depend. The chapter discusses the highlights of the theoretical and empirical analysis and their conclusions. Then, the same analytical framework is used to obtain new results on the effect of inflation on the market price of risk. It turns out that by using nominal values for returns, the market price of risk under inflation is increased by positive covariance between the rate of inflation and the market rate of return, and decreased by negative covariance. However, statistically as the actual covariance has been very small since the latter part of the 19th century, at least in the USA, the measured market price of risk is not affected appreciably by the adjustment for inflation.