house price dynamics with dispersed information abstract we use a user cost model to study how dispersed information affects the equilibrium house price in the model agents are disparately informed about local economic conditions consume housing services and speculate on price changes optimists who expect high house price growth buy in anticipation of capital gains pessimists who expect capital losses prefer to rent because of short selling constraints on housing pessimistic expectations are not incorporated in the price of owned houses and the equilibrium price is higher and more volatile relative to the benchmark case of common information we present evidence supporting the model s predictions in a panel of us cities

seasonal factors and outlier effects in rate of return on electricity spot prices in australia s national electricity market this article documents seasonal patterns and other characteristics of electricity spot prices in the australian national electricity market nem over a 7 year sample period the goal is to investigate more specifically the influence of seasonalities and outliers noted in the body of literature on electricity prices the results confirm that electricity prices exhibit significant time of day and day of week effects and monthly and yearly effects are significant to a lesser degree extremely high spikes in the price series are an important characteristic of electricity prices and are shown to be a highly significant component of returns behaviour negative prices are unusual in financial time series data but occur in australian electricity prices and are found to be influential on returns the implications of these finding are that seasonal and outlier effects should not be ignored in efforts to model electricity prices

discovering price load relationships in california s electricity market this paper reports on characterizing recent price behavior in the califomia electricity market market participants that is producers consumers and traders are highly motivated by the potential for profits to develop strategies to explore and exploit the limits of system operation these strategies should be reflected in the market as different price to load relationships we show that a number of regimes i e characteristic behaviors exist in the price time series and provide a brief analysis of each regime knowledge of the number of regimes their characteristics and switching dynamics allows insight into the market and power system performance

when supply meets demand the case of hourly spot electricity prices we use a supply demand framework to model the hourly day ahead price of electricity the spot price based on publicly available information with the model we can forecast the level and probability of a spike in the spot price defined as the spot price above a certain threshold several european countries have recently started publishing day ahead forecasts of the available supply this paper shows this forecasted capacity is quite successful in predicting spot price movements 24 h ahead

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

stochastic convenience yield and the pricing of oil contingent claims this paper develops and empirically tests a two factor model for pricing financial and real assets contingent on the price of oil the factors are the spot price of oil and the instantaneous convenience yield the parameters of the model are estimated using weekly oil futures contract prices from january 1984 to november 1988 and the model s performance is assessed out of sample by valuing futures contracts over the period november 1988 to may 1989 finally the model is applied to determine the present values of one barrel of oil deliverable in one to ten years time

construction of an efficient portfolio of power purchase decisions based on risk diversification tradeoff abstract we present a methodology based on the tradeoff between risk and diversification in order to evaluate a purchase portfolio of energy where the assets refer to purchasing strategies of a retailer generator of electricity in three markets spot regulated and non regulated markets we use two measures of diversification i entropy based on factors constructed by principal components analysis and ii entropy based on asset risk in each case weights for each strategy are estimated by using the interior point method for which monthly forecasts of returns are calculated a year ahead for each market spot prices are modeled using an arima model and bilateral contracts are modeled using growth rates we compare risk diversified portfolios with mean variance portfolio although diversification does not necessarily mean a lower risk we show that the mean variance portfolio s risk is not always lower than the risk diversified approaches also we show that diversification converges to one for the highest risk portfolio but this does not happen in the case of entropy based on factors because one asset can participate in more than one principal component clearly the mean variance approach is unable to perform a diversified allocation these results are useful for retailer generators who want combine the criteria of risk and diversification

weak form efficiency in the kuala lumpur and singapore stock markets previous weak form efficiency tests of the kuala lumpur and singapore stock markets have mixed findings but mostly have suggested close conformity with a random walk using a disparate type of sample data over a longer and diverse time period this paper seeks to replicate the earlier investigations of these two thin markets in order to verify whether the previous results may be sample specific the distribution of daily stock returns is also examined to determine the validity of using statistical tests based on the normality assumption results tend predominately to confirm independence of serial stock returns and indicate distinctly nonnormal distributions

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

stochastic joint optimization of wind generation and pumped storage units in an electricity market one of the main characteristics of wind power is the inherent variability and unpredictability of the generation source even in the short term to cope with this drawback hydro pumped storage units have been proposed in the literature as a good complement to wind generation due to their ability to manage positive and negative energy imbalances over time this paper investigates the combined optimization of a wind farm and a pumped storage facility from the point of view of a generation company in a market environment the optimization model is formulated as a two stage stochastic programming problem with two random parameters market prices and wind generation the optimal bids for the day ahead spot market are the ldquohere and nowrdquo decisions while the optimal operation of the facilities are the recourse variables a joint configuration is modeled and compared with an uncoordinated operation a realistic example case is presented where the developed models are tested with satisfactory results

deregulated power prices comparison of diurnal patterns we examine electrical power price and in particular its daily and average weekday vs weekend pattern of change for 14 deregulated markets power price in deregulated markets shows fundamentally different patterns north american markets show a monotonic diurnal weekday price pattern while all other markets studied show more than one price peak deregulated power markets differ in maximum vs minimum daily average price and in average weekday to weekend price in turn creating a different incentive for a consumer to time shift power consuming activities markets differ in the extent to which a small fraction of the days shapes the average diurnal pattern and value of price deregulated markets show a wide variation in the correlation between load and price some deregulated markets most notably britain and spain show patterns that are predictable and consistent and hence that can encourage a customer to shape consumption behaviors other markets for example south australia have patterns that are inconsistent and irregular and hence are hard for a customer to interpret a customer in such a market will have a higher incentive to escape risk through hedging mechanisms

patterns in japanese common stock returns day of the week and turn of the year effects stock markets in the united states and foreign countries exhibit a strong weekly seasonal 1 an empirical regularity for which no theoretical explanation has been found one s belief in this phenomenon would be strengthened if it is known to also occur in capital markets separated from those in the united states by distance institutional arrangements and culture this paper extends the results found in 5 and more closely examines the day to day stock market returns for japan while we find a weekly seasonal in japan its nature is significantly different in a statistical sense from the american one for example the lowest mean return in japan occurs on tuesday not monday as in the united states thus while the day of the week effect for american stocks has been casually called the weekend effect it should not be referred to in this way for japanese stocks the next part of the paper examines causes for the unique japanese seasonal we investigate whether the results in japan are associated with those in the united states and in particular consider whether the low tuesday return in japan and the low monday return in the united states are due to time zone differ ences the settlement process and the measurement error problem also are treated we next consider the relationship between foreign exchange returns and stock market returns the seasonal in daily foreign exchange returns does not offset the seasonal in daily stock market returns thus both japanese and american investors confront a day of the week effect in the japanese stock mar ket our data also allow us to investigate the turn ofthe year effect japanese stock returns in january are significantly above the returns during the rest of the year however unlike some reports using u s data see 12 and 13 we find no interaction in japan between the monday effect and the january effect

anomalies or illusions evidence from stock markets in eighteen countries abstract this paper examines five seasonal patterns in stock markets of eighteen countries the weekend turn of the month end of december monthly and friday the thirteenth effects we find a daily seasonal in nearly all the countries but a weekend effect in only nine countries interestingly the daily seasonal largely disappears in the 1980s the last trading day of the month has large returns and low variance in most countries many countries have large december pre holiday and inter holiday returns the january returns are large in most countries and a significant monthly seasonal exists in ten countries

a model for energy pricing with stochastic emission costs we use a supply demand approach to value energy products exposed to emission cost uncertainty we find closed form solutions for a number of popularly traded energy derivatives such as forwards european call options written on spot prices and european call options written on forward contracts our modeling approach is to first construct noisy supply and demand processes and then equate them to find an equilibrium price this approach is very general while still allowing for sensitivity analysis within a valuation setting our assumption is that in the presence of emission costs traditional supply growth will slow down causing output prices of energy products to become more costly over time however emission costs do not immediately cause output price appreciation but instead expose individual projects particularly those with high emission outputs to much more extreme risks through the cost side of their profit stream our results have implications for hedging and pricing for producers operating in areas facing a stochastic emission cost environment

on the market impact of wind energy forecasts this paper presents an analysis of how day ahead electricity spot prices are affected by day ahead wind power forecasts demonstration of this relationship is given as a test case for the western danish price area of the nord pool s elspot market impact on the average price behaviour is investigated as well as that on the distributional properties of the price by using a non parametric regression model to assess the effects of wind power forecasts on the average behaviour the non linearities and time variations in the relationship are captured well and the effects are shown to be quite substantial furthermore by evaluating the distributional properties of the spot prices under different scenarios the impact of the wind power forecasts on the price distribution is proved to be considerable the conditional price distribution is moreover shown to be non gaussian this implies that forecasting models for electricity spot prices for which parameters are estimated by a least squares techniques will not have gaussian residuals hence the widespread assumption of gaussian residuals from electricity spot price models is shown to be inadequate for these model types the revealed effects are likely to be observable and qualitatively similar in other day ahead electricity markets significantly penetrated by wind power

investigating the behavior and characteristics of the madrid stock exchange abstract this study examines the efficiency and characteristics of the nine major indexes of the madrid stock exchange from 1941 through 1992 the findings do not support weak form efficiency among the general bank construction investment metal working and chemical indexes results of the utility food and communications index tests are not consistent

is there a monthly effect in stock market returns evidence from foreign countries abstract in a recent paper ariel documents a monthly pattern for u s stock market returns our paper examines this pattern of returns in four other countries we find only weak evidence supporting this phenomenom in these foreign markets just one country exhibits a significant seasonal consistent with ariel s work however we do find stronger evidence of a last day of the month effect in addition there is evidence of a country unique monthly pattern i e one that is not consistent with the u s pattern

an intertemporal general equilibrium model of asset prices this paper develops a continuous time general equilibrium model of a simple but complete economy and uses it to examine the behavior of asset prices in this model asset prices and their stochastic properties are determined endogenously one principal result is a partial differential equation which asset prices must satisfy the solution of this equation gives the equilibrium price of any asset in terms of the underlying real variables in the economy in this paper we develop a general equilibrium asset pricing model for use in applied research an important feature of the model is its integration of real and financial markets among other things the model endogenously determines the stochastic process followed by the equilibrium price of any financial asset and shows how this process depends on the underlying real variables the model is fully consistent with rational expectations and maximizing behavior on the part of all agents our framework is general enough to include many of the fundamental forces affecting asset markets yet it is tractable enough to be specialized easily to produce specific testable results furthermore the model can be extended in a number of straightforward ways consequently it is well suited to a wide variety of applications for example in a companion paper cox ingersoll and ross 7 we use the model to develop a theory of the term structure of interest rates many studies have been concerned with various aspects of asset pricing under uncertainty the most relevant to our work are the important papers on intertemporal asset pricing by merton 19 and lucas 16 working in a continuous time framework merton derives a relationship among the equilibrium expected rates of return on assets he shows that when investment opportunities are changing randomly over time this relationship will include effects which have no analogue in a static one period model lucas considers an economy with homogeneous individuals and a single consumption good which is produced by a number of processes the random output of these processes is exogenously determined and perishable assets are defined as claims to all or a part of the output of a process and the equilibrium determines the asset prices our theory draws on some elements of both of these papers like merton we formulate our model in continuous time and make full use of the analytical tractability that this affords the economic structure of our model is somewhat similar to that of lucas however we include both endogenous production and

short term variations and long term dynamics in commodity prices in this article we develop a two factor model of commodity prices that allows meanreversion in short term prices and uncertainty in the equilibrium level to which prices revert although these two factors are not directly observable they may be estimated from spot and futures prices intuitively movements in prices for long maturity futures contracts provide information about the equilibrium price level and differences between the prices for the short and long term contracts provide information about short term variations in prices we show that although this model does not explicitly consider changes in convenience yields over time this short term long term model is equivalent to the stochastic convenience yield model developed in gibson and schwartz 1990 we estimate the parameters of the model using prices for oil futures contracts and apply the model to some hypothetical oil linked assets to demonstrate its use and some of its advantages over the gibson schwartz 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

numerical simulation on property tax reform evidence from china this article analyses the property tax reform in china in a computable general equilibrium model that recognizes the interaction between and among housing markets in different provinces and macroec

institutions property taxation and local government finance in china china s rapid urbanisation has prompted its government to explore new sustainable sources of public revenue to finance the continued demand for urban infrastructure and services property tax advocates have sought to take advantage of the real estate booms that have occurred since economic liberalisation by actively campaigning for a real property levy as an appropriate policy choice against this background this study evaluates the prospect of implementing market value based property tax reforms in mainland china based on the new institutional economics perspective it posits property tax as an institutional arrangement which requires complementary mechanisms in land registration property appraisal tax administration social security and dispute resolution property tax reforms would not only necessitate technical changes but would also have extensive social political and legal repercussions for chinese society

modeling real time balancing power market prices using combined sarima and markov processes this paper describes modeling of real time balancing power market prices by using combined seasonal auto regressive integrated moving average sarima and discrete markov processes the combination of such processes allows generation of price series with periods where no demand for balancing power exists the purpose of the model is simulation of prices to construct scenario trees representing possible realization of the stochastic prices such scenario trees can be used in planning models based on stochastic optimization to generate bid sequences to the balancing market the spread of the prices in the tree and the shape of the scenarios are of central importance model parameter estimation methods reflecting the demands on scenario trees have therefore been used the proposed model is also applied to data from the nordic power market the conclusion of this paper is that the developed model is appropriate for modeling real time balancing power prices

hmm filtering and parameter estimation of an electricity spot price model abstract in this paper we develop a model for electricity spot price dynamics the spot price is assumed to follow an exponential ornstein uhlenbeck ou process with an added compound poisson process in this way the model allows for mean reversion and possible jumps all parameters are modulated by a hidden markov chain in discrete time they are able to switch between different economic regimes representing the interaction of various factors through the application of reference probability technique adaptive filters are derived which in turn provide optimal estimates for the state of the markov chain and related quantities of the observation process the em algorithm is applied to find optimal estimates of the model parameters in terms of the recursive filters we implement this self calibrating model on a deseasonalised series of daily spot electricity prices from the nordic exchange nord pool on the basis of one step ahead forecasts we found that the model is able to capture the empirical characteristics of nord pool spot prices

option valuation of claims on real assets the case of offshore petroleum leases this paper extends financial option theory by developing a methodology for the valuation of claims on a real asset an offshore petroleum lease several theoretical and practical problems not present in applying option pricing theory to financial assets are addressed most importantly we show the necessity of combining option pricing techniques with a model of equilibrium in the market for the underlying asset petroleum reserves the advantages of this approach over conventional discounted cash flow techniques are emphasized the methodological development provides important insights for both company behavior and government policy promising empirical results are reported

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

high stock returns before holidays existence and evidence on possible causes on the trading day prior to holidays stocks advance with disproportionate frequency and show high mean returns averaging nine to fourteen times the mean return for the remaining days of the year over one third of the total return accruing to the market portfolio over the 1963 1982 period was earned on the eight trading days which each year fall before holiday market closings examination of hourly pre holiday stock returns reveals high returns throughout the day pre holiday stock returns in the post test 1983 1986 period are also examined this paper explores a seeming empirical regularity in the pattern of daily stock index returns surrounding holidays the trading day prior to holidays on average exhibits high positive returns over one third of the return earned by the market over the twenty year test period accrued on the eight trading days which annually fall before holidays intimations of the pre holiday strength have appeared in the academic literature in an examination of the frequency of dow jones industrial average advances on days surrounding weekends during the 1901 to 1932 period fields 1934 finds a disproportionate frequency of advances on trading days preceding long holiday weekends roll 1983a finds high returns accruing to small firms on the trading day prior to new year s day lakonishok and smidt 1984 note that prices also rise in all deciles of market capitalization on the last trading day before christmas and conclude that the high christmas returns of large companies might be considered another mystery a number of stock market advisors have also noted the pre holiday strength merrill 1966 finds a disproportionate frequency of dow jones industrial average advances on days preceding holidays during the 1897 to 1965 period and fosback 1976 has noted high preholiday returns in sp further hourly returns and closing bid ask data are examined to determine when high returns accrue on preholidays tests are also conducted showing that pre holiday strength is not

wheeling of power under deregulated environment of power system a bibilographical survey the power industry is moving rapidly from regulated conventional setup to a deregulated environment there is an urgent need to keep a track of international experiences and activities taking place in this emerging field this paper gives a bibliographical survey and general backgrounds of research and development in the field of power system wheeling under deregulated environment based on over 170 published articles wheeling in general and the establishment of wheeling rate in particular are subjects of extensive debate today this will be very much useful for all countries especially those developing countries which are moving toward the unbundling of electricity supply industry esi the collected literature have been divided into many section so that new researchers do not face any difficulty for obtaining literature particularly in the area of wheeling of power under deregulated environment

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

economic impact of electricity market price forecasting errors a demand side analysis several techniques have been proposed in the literature to forecast electricity market prices and improve forecast accuracy however no studies have been reported examining the economic impact of price forecast inaccuracies on forecast users therefore in this paper the application of electricity market price forecasts to short term operation scheduling of two typical and inherently different industrial loads is examined and the associated economic impact is analyzed using electricity market price forecasts as the expected next day electricity prices optimal operating schedules and the associated costs are determined for each load these costs are compared with those of a perfect price forecast scenario in which actual prices are used to determine the operating schedules numerical results and discussions are provided based on price forecasts with different error characteristics

classification of future electricity market prices forecasting short term electricity market prices has been the focus of several studies in recent years although various approaches have been examined achieving sufficiently low forecasting errors has not been always possible however certain applications such as demand side management do not require exact values for future prices but utilize specific price thresholds as the basis for making short term scheduling decisions in this paper classification of future electricity market prices with respect to pre specified price thresholds is introduced two alternative models based on support vector machines are proposed in a multi class multi step ahead price classification context numerical results are provided for classifying prices in ontario s and alberta s markets

a hybrid system econometric model for electricity spot prices considering spike sensitivity to forced outage distributions with the current trend in deregulation all electricity markets have been subject to volatile electricity prices typically in peak season as markets mature new financial and operational risk management instruments are becoming available in order to price such instruments a model for the underlying price process is required in this paper a hybrid model is described that contains aspects of the power system as well as the historical time series of spot prices observed in the market by including both aspects of the problem a model with both economic and system based aspects is created its modular design allows easy adaptation to different markets based on generating system reliability load patterns and price histories the spot price histories are manifest in two probability distributions for these prices whereas the system specifics are included via load and generation models which are calibrated to the climate and system of interest

turn of month and pre holiday effects on stock returns some international evidence abstract this study examines turn of month and pre holiday effects on international markets turn of month effects are significant in canada the uk australia switzerland and west germany pre holiday effects are significant in canada japan hong kong and australia the absence of these effects in certain markets suggests that they originate from country specific institutional practices all countries exhibiting pre holiday effects do so before local holidays only hong kong does so before us holidays this reinforces the conclusion that such anomalies are not generated solely by american institutions

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

electricity price curve modeling and forecasting by manifold learning this paper proposes a novel nonparametric approach for the modeling and analysis of electricity price curves by applying the manifold learning methodology locally linear embedding lle the prediction method based on manifold learning and reconstruction is employed to make short term and medium term price forecasts our method not only performs accurately in forecasting one day ahead prices but also has a great advantage in predicting one week ahead and one month ahead prices over other methods the forecast accuracy is demonstrated by numerical results using historical price data taken from the eastern u s electric power markets

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

are seasonal anomalies real a ninety year perspective in recent years there has been a proliferation of empirical studies documenting unexpected or anomalous regularities in security rates of return in addition to the widely studied relation between firm size and rate of return 1 these include seasonal regularities related to the time of the day harris 1986 the day of the week see ball and bowers 1986 cross 1973 french 1980 gibbons and hess 1981 jaffe and westerfield 1985 keim and stambaugh 1984 and lakonishok and levi 1982 the time of the month ariel 1987 and the turn of the year see haugen and lakonishok 1988 jones pearce and wilson 1987 lakonishok

skewness persistence in common stock returns recent empirical studies have found ex post common stock returns to be consistently positively skewed the frequency of positive skewness in this study is found to be relatively stable over varying time periods from 1961 to 1980 however the skewness of individual stocks and portfolios of stocks does not persist across different time periods positively skewed equity portfolios in one period are not likely to be positively skewed in the next time period past positively skewed returns do not predict future positively skewed returns

analysis of the probability distribution of lmp by central limit theorem the main goal of this paper is to provide an analytical study on the statistical behaviors of the electricity price at various conditions of the load and the network in order to achieve this goal a novel two step modeling is proposed for the electricity price at the first step it is proved that there is a linear relationship between the locational marginal prices lmp and the strategies of generating companies gencos in this linear relationship the lmp is decomposed to the weighted summation of the strategies of marginal units plus constant value at the second step the lmp is modeled by classic central limit theorem clt using this model an analytical relationship is derived between the price discovery mechanism and characteristics of its probability density function pdf as a result this paper shows analytically that increasing system load causes the price pdf deviates from the normal distribution on the other hand by decreasing system load the electricity price distribution tends toward the normal distribution the statistical studies on the real power markets data confirm these analytical results furthermore the simulation results of the ieee 300 bus test system illustrate the efficiency of the proposed model

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

valuing oil properties integrating option pricing and decision analysis approaches there are two major competing procedures for evaluating risky projects where managerial flexibility plays an important role one is decision analytic based on stochastic dynamic programming and the other is option pricing theory or contingent claims analysis based on the no arbitrage theory of financial markets in this paper we show how these two approaches can be profitably integrated to evaluate oil properties we develop and analyze a model of an oil property either a developed property or a proven but undeveloped reserve where production rates and oil prices both vary stochastically over time and at any time the decision maker may terminate production or accelerate production by drilling additional wells the decision maker is assumed to be risk averse and can hedge price risks by trading oil futures contracts we also describe extensions of this model that incorporate additional uncertainties and options discuss its use in exploration decisions and in evaluating a portfolio of properties rather than a single property and briefly describe other potential applications of this integrated methodology

day ahead price forecasting of electricity markets by mutual information technique and cascaded neuro evolutionary algorithm in a competitive electricity market price forecasts are important for market participants however electricity price is a complex signal due to its nonlinearity nonstationarity and time variant behavior in spite of much research in this area more accurate and robust price forecast methods are still required in this paper a combination of a feature selection technique and cascaded neuro evolutionary algorithm cnea is proposed for this purpose the feature selection method is an improved version of the mutual information mi technique the cnea is composed of cascaded forecasters where each forecaster consists of a neural network nn and an evolutionary algorithm ea an iterative search procedure is also incorporated in our solution strategy to fine tune the adjustable parameters of both the mi technique and cnea the price forecast accuracy of the proposed method is evaluated by means of real data from the pennsylvania new jersey maryland pjm and spanish electricity markets the method is also compared with some of the most recent price forecast techniques