modeling and forecasting electricity prices with input output hidden markov models in competitive electricity markets in addition to the uncertainty of exogenous variables such as energy demand water inflows and availability of generation units and fuel costs participants are faced with the uncertainty of their competitors behavior the analysis of electricity price time series reflects a switching nature related to discrete changes in competitors strategies which can be represented by a set of dynamic models sequenced together by a markov chain an input output hidden markov model iohmm is proposed for analyzing and forecasting electricity spot prices the model provides both good predictions in terms of accuracy as well as dynamic information about the market in this way different market states are identified and characterized by their more relevant explanatory variables moreover a conditional probability transition matrix governs the probabilities of remaining in the same state or changing to another whenever a new market session is opened the model has been successfully applied to real clearing prices in the spanish electricity market

quality and competition in recent years the practitioner literature in operations management has seen a dramatic surge in articles on quality management it reflects the increased emphasis on quality by u s firms which has been attributed largely to increased competition faced by them the question of how quality is influenced by competitive intensity however has not received much attention either in the practitioner or the academic research literatures the notion of competitive intensity itself has not been defined precisely in this paper we develop formal models of oligopolistic competition to investigate whether equilibrium levels of quality increase as competition intensifies we consider three different competitive settings i asymmetric duopolistic competition where the dominant firm s intrinsic demand potential decreases ii a symmetric duopoly where the firms are precluded from cooperating in setting quality levels and iii symmetric oligopolistic competition where the number of firms increases we find that the relation between equilibrium quality and competitive intensity depends on what is understood by increased competition and in addition the relation is contingent on the values of parameters describing the cost and demand structure for the industry

mixed models for short run forecasting of electricity prices application for the spanish market short run forecasting of electricity prices has become necessary for power generation unit schedule since it is the basis of every profit maximization strategy in this article a new and very easy method to compute accurate forecasts for electricity prices using mixed models is proposed the main idea is to develop an efficient tool for one step ahead forecasting in the future combining several prediction methods for which forecasting performance has been checked and compared for a span of several years also as a novelty the 24 hourly time series has been modelled separately instead of the complete time series of the prices this allows one to take advantage of the homogeneity of these 24 time series the purpose of this paper is to select the model that leads to smaller prediction errors and to obtain the appropriate length of time to use for forecasting these results have been obtained by means of a computational experiment a mixed model which combines the advantages of the two new models discussed is proposed some numerical results for the spanish market are shown but this new methodology can be applied to other electricity markets as well

an adaptive wavelet neural network based energy price forecasting in electricity markets in a competitive electricity market an accurate forecasting of energy prices is an important activity for all the market participants either for developing bidding strategies or for making investment decisions an adaptive wavelet neural network awnn is proposed in this paper for short term price forecasting stpf in the electricity markets a commonly used mexican hat wavelet has been chosen as the activation function for hidden layer neurons of feed forward neural network ffnn to demonstrate the effectiveness of the proposed approach day ahead prediction of market clearing price mcp of spain market which is a duopoly market with a dominant player and locational marginal price lmp forecasting in pjm electricity market are considered the forecasted results clearly show that awnn has good prediction properties compared to other forecasting techniques such as wavelet arima multi layer perceptron mlp and radial basis function rbf neural networks as well as recently proposed fuzzy neural network fnn

vertical economies in electric power evidence on integration and its alternatives abstract the restructuring of the electric power industry raises questions about the possible sacrifice of economies previously secured by integrating generation with downstream transmission and distribution this study utilizes a data set of u s electric utilities that differ widely in their degree of vertical integration in order to model estimate and measure such economies cost savings from vertical integration are found to be quite substantial for all but the smallest utilities and are largest for those that are nearly fully integrated the study goes on to investigate alternatives to sources of and determinants of vertical integration among other findings it appears that some holding companies achieve economies comparable to those from vertical integration

mitigating contagion risk by investing in the safety of rivals abstract firms often benefit when an unfavorable event befalls a rival usually through a shift in demand but sometimes negative especially catastrophic events adversely affect an entire industry we refer to such phenomena as contagion and note that each firm faces not only its own direct risks but also the contagion risks imposed by rivals who for example avoid strong safety measures because investment cost exceeds expected loss the conclusion of this paper is that in the extreme case low risk firms may benefit from investing in safety improvements for their higher risk rivals for example a firm that over complies with safety requirements may benefit from investing in safety improvements in a rival that complies with regulations at a minimal level this research explores conditions under which such a contagion risk mitigation strategy is profitable our findings indicate that for a low risk firm there is a threshold above which such an investment would be profitable in a market where price sensitivity to a rival s safety is close to zero a low risk firm can decrease this threshold by extending the investment horizon the investment is less likely to pay off when firms compete on quantity as opposed to price we also show that below a threshold market price a third firm that is neutral neither needs investment nor invests may be put at a cost disadvantage when this contagion risk mitigation strategy is implemented

the role of co opetition in low carbon manufacturing abstract low carbon manufacturing has become a strategic objective for many developed and developing economies this study examines the role of co opetition in achieving this objective we investigate the pricing and emissions reduction policies for two rival manufacturers with different emission reduction efficiencies under the cap and trade policy we assume that the product demand is price and emission sensitive based on non cooperative and cooperative games the optimal solutions for the two manufacturers are derived in the purely competitive and co opetitive market environments respectively through the discussion and numerical analysis we uncovered that in both pure competition and co opetition models the two manufacturers optimal prices depend on the unit price of carbon emission trading in addition higher emission reduction efficiency leads to lower optimal unit carbon emissions and higher profit in both the pure competition and co petition models interestingly compared to pure competition co opetition will lead to more profit and less total carbon emissions however the improvement in economic and environmental performance is based on higher product prices and unit carbon emissions

improving channel coordination through franchising in this paper we explore the role of franchising arrangements in improving coordination between channel members in particular we focus on two elements of the franchising contract namely the royalty structure and the monitoring technology we begin with a simple analysis where a manufacturer distributes its product through a retailer and the retail demand is affected by the retail price and the service provided by the retailer in this context we show that neither royalty payments nor monitoring are needed for full coordination we then extend the model to allow for free riding by franchisees and show that although monitoring helps affect the behavior of the franchisees royalty payments are still unnecessary to coordinate the activities of the franchisor and the franchisees finally we show that in environments where factors affecting retail sales are controlled by both the franchisor and the franchisee royalty payments provide the necessary incentives for the franchisor while monitoring systems are used to ensure that franchisees also behave in the best interest of the channel our general results are found to be consistent with those in brickley and dark 1987 and norton 1988 who conclude that franchising is affected by principal agent problems our theory finds special support in the work of lafontaine 1988 where it is concluded that the incidence of franchising is related to cost of monitoring and the importance of franchisor investments

timing and eco nomic efficiency of climate friendly investments in supply chains abstract emission trading schemes such as the european union emissions trading system euets attempt to reconcile economic efficiency with ecological efficiency by creating financial incentives for companies to invest in climate friendly innovations using real options methodology we demonstrate that under uncertainty economic and ecological efficiency continue to be mutually exclusive this problem is even worse if a climate friendly project depends on investing in of a whole supply chain we model a sequential bargaining game in a supply chain where the parties negotiate over implementation of a carbon dioxide co2 saving investment project we show that the outcome of their bargaining is not economically efficient and even less ecologically efficient furthermore we show that a supply chain becomes less economically efficient and less ecologically efficient with every additional chain link finally we make recommendations for how managers or politicians can improve the situation and thereby increase economic as well as ecological efficiency and thus also the eco efficiency of supply chains

value of information in capacitated supply chains we incorporate information flow between a supplier and a retailer in a two echelon model that captures the capacitated setting of a typical supply chain we consider three situations 1 a traditional model where there is no information to the supplier prior to a demand to him except for past data 2 the supplier knows the s s policy used by the retailer as well as the end item demand distribution and 3 the supplier has full information about the state of the retailer order up to policies continue to be optimal for models with information flow for the finite horizon the infinite horizon discounted and the infinite horizon average cost cases study of these three models enables us to understand the relationships between capacity inventory and information at the supplier level as well as how they are affected by the retailer s s s values and end item demand distribution we estimate the savings at the supplier due to information flow and study when information is most beneficial

measuring economies of vertical integration in network industries an application to the water sector this paper provides a framework that aims at distinguishing the technological economies of vertical integration from the vertical economies resulting from an inefficient input allocation due to upstream market imperfections to illustrate our analysis we use consistent panel data econometric methods to estimate cost functions on a sample of north american water utilities contrary to what has been found for other network industries electricity and gas for instance we show that the global and technological economies of vertical integration are not significant except for the smallest utilities

preservation technology investment for deteriorating inventory abstract the objective of this study is to develop a deteriorating inventory policy when the retailer invests on the preservation technology to reduce the rate of product deterioration a solution procedure is presented to determine an optimal replenishment cycle shortage period order quantity and preservation technology cost such that the total profit per unit time is maximized a numerical example and sensitivity analysis are presented to illustrate the model

application of public domain market information to forecast ontario s wholesale electricity prices this paper evaluates the usefulness of publicly available electricity market information in forecasting the hourly ontario energy price hoep in order to do so relevant data from ontario and its neighboring electricity markets namely new york new england and pjm electricity markets are investigated and a final set of explanatory variable candidates that are available before real time are selected multivariate transfer function and dynamic regression models are employed to relate hoep behavior to the selected explanatory variable candidates univariate arima models are also developed for the hoep the hoep models are developed on the basis of two forecasting horizons i e 3 h and 24 h and forecasting performance of the multivariate models is compared with that of the univariate models the outcomes show that the market information publicly available before real time can be used to improve hoep forecast accuracy to some extent however unusually high or low prices remain unpredictable and hence the available data cannot lead to significantly more accurate forecasts nevertheless the generated forecasts in this paper are significantly more accurate than currently available hoep forecasts to analyze the relatively low accuracy of the hoep forecasts comparisons are made with respect to arima models developed for locational marginal prices lmps of ontario s three neighboring markets and price volatility analyses are presented

optimal exercise of jointly held real options a nash bargaining approach with value diversion this paper provides a two stage decision framework in which two or more parties exercise a jointly held real option we show that a single party s timing decision is always socially efficient if it precedes bargaining on the terms of sharing however if the sharing rule is agreed before the exercise timing decision is made then socially optimal timing is attained only if there is a cash payment element in the division of surplus if the party that chooses the exercise timing can divert value from the project then the first best outcome may not be possible at all and the second best outcome may be implemented using a contract that is generally not optimal in the former cases our framework contributes to the understanding of a range of empirical regularities in corporate and entrepreneurial finance

a garch forecasting model to predict day ahead electricity prices price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets both for spot markets and long term contracts price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize profits this paper provides an approach to predict next day electricity prices based on the generalized autoregressive conditional heteroskedastic garch methodology that is already being used to analyze time series data in general a detailed explanation of garch models is presented and empirical results from the mainland spain and california deregulated electricity markets are discussed

impact of product pricing and timing of investment decisions on supply chain co opetition abstract in supply chain co opetition firms simultaneously compete and co operate in order to maximize their profits we consider the nature of co opetition between two firms the product supplier invests in the technology to improve quality and the purchasing firm buyer invests in selling effort to develop the market for the product before uncertainty in demand is resolved we consider three different decision making structures and discuss the optimal configuration from each firm s perspective in case 1 the supplier invests in product quality and sets the wholesale price for the product the buyer then exerts selling effort to develop the market and following demand potential realization sets the resale price in case 2 the supplier invests in product quality followed by the buyer s investment in selling effort then after demand potential is observed the supplier sets the wholesale price and the buyer sets the resale price finally in case 3 both firms simultaneously invest in product quality and selling effort respectively subsequently observing the demand potential the supplier sets the wholesale price and the buyer sets the resale price we compare all configuration options from both the perspective of the supplier and the buyer and show that the level of investment by the firms depends on the nature of competition between them and the level of uncertainty in demand our analysis reveals that although configuration 1 results in the highest profits for the integrated channel there is no clear dominating preference on system configuration from the perspective of both parties the incentives of the co opetition partners and the investment levels are mainly governed by the cost structure and the level of uncertainty in demand we examine and discuss the relation between system parameters and the incentives in desiging the supply contract structure

reducing electricity price forecasting error using seasonality and higher order crossing information commodity prices are volatile and their volatility changes over time electricity prices are even more volatile in general because customers cannot easily switch to other energy source when the electricity prices fluctuate electricity prices also have strong seasonal behavior because the demand of electricity depends on the season to address these characteristics a time series forecasting model with seasonality and higher order crossing information for electricity prices is proposed the model measures the similarity between two time series by using their higher order crossing information the model can predict not only day ahead prices short term forecasting but also a series of prices for one week ahead or even longer simultaneously medium term price curve forecasting the model is tested on both high and low volatile data sets to evaluate its robustness and generality it is also shown that the model can produce more accurate prediction than some well known models

arima models to predict next day electricity prices price forecasting is becoming increasingly relevant to producers and consumers in the new competitive electric power markets both for spot markets and long term contracts price forecasts are necessary to develop bidding strategies or negotiation skills in order to maximize benefit this paper provides a method to predict next day electricity prices based on the arima methodology arima techniques are used to analyze time series and in the past have been mainly used for load forecasting due to their accuracy and mathematical soundness a detailed explanation of the aforementioned arima models and results from mainland spain and californian markets are presented

electricity market price forecasting based on weighted nearest neighbors techniques this paper presents a simple technique to forecast next day electricity market prices based on the weighted nearest neighbors methodology first it is explained how the relevant parameters defining the adopted model are obtained such parameters have to do with the window length of the time series and with the number of neighbors chosen for the prediction then results corresponding to the spanish electricity market during 2002 are presented and discussed finally the performance of the proposed method is compared with that of recently published techniques

demand signalling under unobservable effort in franchising linear and nonlinear price contracts we study the signalling strategy of a principal who is privately informed about its high demand potential to an uninformed risk neutral agent we analyze the model in the context of a contract between a franchisor and a franchisee we examine the distortions of a two part pricing scheme necessary to credibly inform the franchisee agent we also study whether the inability of the franchisor principal to observe the agent s effort moderates or exaggerates the distortions from the first best two part pricing scheme a surprising outcome is that even though the principal incurs greater signalling cost the magnitude of distortion in the two part scheme is smaller when service is unobservable than when it is not thus a signalling strategy employing the fixed and variable fees is harder to detect under moral hazard empirical studies failing to control for moral hazard may incorrectly conclude that signalling strategy does not occur we later consider a three part scheme to verify whether or not the schem

a novel approach to forecast electricity price for pjm using neural network and similar days method price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk and it also plays a key role in the economic optimization of the electric energy industry this paper explores a technique of artificial neural network ann model based on similar days sd method in order to forecast day ahead electricity price in the pjm market to demonstrate the superiority of the proposed model publicly available data acquired from the pjm interconnection were used for training and testing the ann the factors impacting the electricity price forecasting including time factors load factors and historical price factors are discussed comparison of forecasting performance of the proposed ann model with that of forecasts obtained from similar days method is presented daily and weekly mean absolute percentage error mape of reasonably small value and forecast mean square error fmse of less than 7 mwh were obtained for the pjm data which has correlation coefficient of determination of 0 6744 between load and electricity price simulation results show that the proposed ann model based on similar days method is capable of forecasting locational marginal price lmp in the pjm market efficiently and accurately

forecasting next day electricity prices by time series models in the framework of competitive electricity markets power producers and consumers need accurate price forecasting tools price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities respectively this paper provides two highly accurate yet efficient price forecasting tools based on time series analysis dynamic regression and transfer function models these techniques are explained and checked against each other results and discussions from real world case studies based on the electricity markets of mainland spain and california are presented

energy price forecasting in the ontario competitive power system market this paper introduces a method for forecasting energy prices using artificial intelligence methods such as neural networks and fuzzy logic and a combination of the two the new approach is compared with some of the exiting methods various factors affecting the market clearing price are investigated results for the ontario electricity market are presented

capacity sharing between competitors market competition may lead to mismatch between supply and demand that is overpricing may give rise to underselling and underpricing may yield stockout capacity sharing is a common practice to

electricity price short term forecasting using artificial neural networks this paper presents the system marginal price smp short term forecasting implementation using the artificial neural networks ann computing technique the described approach uses the three layered ann paradigm with backpropagation the retrospective smp real world data acquired from the deregulated victorian power system was used for training and testing the ann the results presented in this paper confirm considerable value of the ann based approach in forecasting the smp

strategic wholesale pricing in a supply chain with a potential entrant this paper develops an adverse selection model for a two stage supply chain with one supplier one retailer and a potential outside entrant supplier who makes a partially substitutable product the work is different from most research on entry deterrence that only considers a single stage model our main interest is to investigate how the incumbent supplier can strategically maximize her profit by a wholesale pricing policy when facing the potential entrant we focus on a model where the entrant supplier will sell her product through the same incumbent retailer we derive the optimal decisions for each player and study the comparative statics of the equilibrium to investigate how the supply chain structure may affect the deterrence strategy of the incumbent supplier we also consider three alternative models with different channel structures when both suppliers sell their products directly when the entrant has another independent retailer and when the entrant sells her product directly through the comparison we find that the existence of the common downstream retailer often enhances the deterring motivation of the incumbent supplier

day ahead electricity price forecasting using the wavelet transform and arima models this paper proposes a novel technique to forecast day ahead electricity prices based on the wavelet transform and arima models the historical and usually ill behaved price series is decomposed using the wavelet transform in a set of better behaved constitutive series then the future values of these constitutive series are forecast using properly fitted arima models in turn the arima forecasts allow through the inverse wavelet transform reconstructing the future behavior of the price series and therefore to forecast prices results from the electricity market of mainland spain in year 2002 are reported