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

profit sharing and productivity microeconomic evidence from the united states profit sharing has often been suggested as a potential solution to principal agent problems in the workplace the relationship between productivity and profit sharing is examined using a panel dataset drawn from 2 976 publicly held companies over the 1971 85 period alternatively using firm intercept and first difference specifications the regression results indicate that the adoption of profit sharing is associated with a 2 5 4 2 percent increase in productivity in addition the size of the effect increases with the proportion of employees participating in profit sharing these results hold under a variety of specifications and do not appear to be explained by accompanying changes in management or personnel policies copyright 1992 by royal economic society

evaluating interval forecasts a complete theory for evaluating interval forecasts has not been worked out to date most of the literature implicitly assumes homoskedastic errors even when this is clearly violated and proceed by merely testing for correct unconditional coverage consequently i set out to build a consistent framework for conditional interval forecast evaluation which is crucial when higher order moment dynamics are present the new methodology is demonstrated in an application to the exchange rate forecasting procedures advocated in risk management

forecasting portfolio value at risk with nonparametric lower tail dependence estimates abstract we propose to forecast the value at risk of bivariate portfolios using copulas which are calibrated on the basis of nonparametric sample estimates of the coefficient of lower tail dependence we compare our proposed method to a conventional copula garch model where the parameter of a clayton copula is estimated via canonical maximum likelihood the superiority of our proposed model is exemplified by analyzing a data sample of nine different bivariate and one nine dimensional financial portfolio a comparison of the out of sample forecasting accuracy of both models confirms that our model yields economically significantly better value at risk forecasts than the competing parametric calibration strategy

optimal integrated generation bidding and scheduling with risk management under a deregulated daily power market in the deregulated power industry a generation company genco sells energy and ancillary services primarily through bidding at a daily market developing effective strategies to optimize hourly bid curves for a hydrothermal power system to maximize profits becomes one of the most important tasks of a genco this paper presents a unified framework for optimizing energy and reserve bidding strategies under a deregulated market in view of high volatilities of market clearing prices mcp the hourly mcps and reserve prices are modeled as discrete random variables whose probability mass functions are predicted with a classification based neural network approach the mean variance method is applied to manage bidding risks where a risk penalty term related to mcp and reserve price variances is added to the objective function to avoid buying too much power from the market at high prices a genco may also require covering at least a certain percentage of its own customer load this self scheduling requirement is modeled similar to the system demand in traditional unit commitment problems the formulation is a stochastic mixed integer optimization with a separable structure an optimization based algorithm combining lagrangian relaxation and stochastic dynamic programming is presented to optimize bids for both energy and reserve markets numerical testing based on an 11 unit system in new england market shows that the method can significantly reduce profit variances and thus reduce bidding risks near optimal energy and reserve bid curves are obtained in 4 5 minutes on a 600 hz pentium iii pc efficient for daily use

robust estimation and forecasting of the long term seasonal component of electricity spot prices when building stochastic models for electricity spot prices the problem of uttermost importance is the estimation and consequent forecasting of a component to deal with trends and seasonality in the data while the short term seasonal components daily weekly are more regular and less important for valuation of typical power derivatives the l ong term seasonal components ltsc seasonal annual are much more diffi cult to tackle surprisingly in many academic papers dealing with electricity spot price modeling the importance of the seasonal decomposition is neglected and the problem of forecasting it is not considered with this paper we want to fill the gap and present a thorough study on estimation and forecasting of the ltsc of electricity spot prices we consider a battery of models based on fourier or wavelet decomposition combined with linear or exponential decay we find that all considered wavelet base d models are significantly better in terms of forecasting spot prices up to a year ahead than all considered sine based models this result questions the validity and usefulness of stochastic models of spot electricity prices built on sinusoidal long term seasonal components

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

union vs nonunion wage norm shifts empirical investigations of wage determination have often produced autocorrelated residuals from time series wage equations runs of overor underprediction have usually been regarded as weaknesses in specification to be corrected or explained away in 1980 however george perry suggested that such runs represent an important if neglected characteristic of american wage setting he argued that norms of wage change develop in the labor market these norms according to perry change discretely there are periods of more or less wage pushiness aggregate wage indexes can be influenced even if norm shifts are not fully reflected everywhere providing those sectors that are affected have sufficient weight in the indexes an obvious division in the labor market is between the union and nonunion sectors there is reason to believe that while there has been a downward shift in wage norms recently the impact has been concentrated in the union sector see my 1985 article indeed the union sector is probably inherently more prone to norm shifts than the nonunion

minimization of imbalance cost trading wind power on the short term power market present power markets are designed for trading conventional generation for wind generation to participate in a short term energy market lengthy wind power production forecasts are required although wind speed forecasting techniques are constantly improving wind speed forecasts are never perfect and resulting wind power forecast errors imply imbalance costs for wind farm owners in this paper a new method for minimization of imbalance costs is developed stochastic programming is used to generate optimal wind power production bids for a short term power market a wind power forecast error is represented as a stochastic process the imbalance costs resulting from this strategy are then compared to the case when wind power production bids on a short term power market are based directly on a wind speed forecast

profit sharing and employment stability this paper tests the hypothesis that workers whose compensation packages contain a profit sharing component are less susceptible to layoff in the face of negative shocks to product demand than are workers paid a fixed time based wage the theory is tested on two data sets one a household survey and the other a survey of small businesses conducted by the authors the characteristics of profit sharing among small businesses by and large meet the theoretical requirements for stabilizing employment and the authors do find evidence in both samples to support the hypothesis the evidence however is of borderline statistical significance and is therefore more suggestive than definitive

spot and derivative pricing in the eex power market using spot and futures price data from the german eex power market we test the adequacy of various one factor and two factor models for electricity spot prices the models are compared along two different dimensions 1 we assess their ability to explain the major data characteristics and 2 the forecasting accuracy for expected future spot prices is analyzed we find that the regime switching models clearly outperform its competitors in almost all respects the best results are obtained using a two regime model with a gaussian distribution in the spike regime furthermore for short and medium term periods our results underpin the frequently stated hypothesis that electricity futures quotes are consistently greater than the expected future spot a situation which is denoted as contango

modeling extreme dependence between european electricity markets electricity spot prices are characterized by sudden large movements followed a few days later by an equally large movement in the opposite direction these phenomena are called spikes upward movements and drops downward movements recent research has suggested that the dynamics of the electricity spot prices can be accurately described by hidden markov regime switching mrs models regime switch models separate the ordinary dependence and the extreme spike or drop dependence this is a crucial point since it is the extreme dependence that is of interest when computing risks we fit a large number of mrs models to six european electricity markets eex powernext apx power uk nl nord pool system sweden in order to analyze the nature of the prices the estimated regimes are then used to analyze the probability of an extreme event and the conditional probability for one market to experience an extreme event conditional that another market is experiencing the same event we find that the frequency of extreme events is positively related to the amount of renewable energy sources in the power system it can be argued that the dependence results in this paper give an indication of what future dependence for even more integrated markets with more renewable energy will be the integration of markets will lead to efficient sharing of reserves while the additional renewable energy will increase the need for reserves the results are of interest when computing financial risks e g var or expected shortfall when designing reserves but also as an indication of the degree of integration between markets

dependence structure in the australian electricity markets new evidence from regular vine copulae abstract in this study regular vine copula was used to investigate the dependence structure of electricity prices at the state level in the australian national electricity market nem during three periods related to the adoption and abolition of the carbon tax in the pre carbon period we found evidence of tail dependence separately in the northern and southern nem but not across them during the carbon period the joint spike in the northern nem disappeared and the tail dependence in the southern nem decreased in the post carbon period the best dependence structure turned out to be a flexible structure of the regular vine which exactly matches the geographical infrastructure connectedness of transmission wires besides both upper and lower tail dependences were found in all adjacent states after the abolition of the carbon tax suggesting a more integrated market regarding tail dependence our findings have substantial implications for risk management in the nem especially for those participants exposed to multiple states

stochastic price modeling of high volatility mean reverting spike prone commodities the australian wholesale spot electricity market it is commonly known that wholesale spot electricity markets exhibit high price volatility strong mean reversion and frequent extreme price spikes this paper employs a basic stochastic model a mean reverting model and a regime switching model to capture these features in the australian national electricity market nem comprising the interconnected markets of new south wales queensland south australia and victoria daily spot prices from 1 january 1999 to 31 december 2004 are employed the results show that the regime switching model outperforms the basic stochastic and mean reverting models electricity prices are also found to exhibit stronger mean reversion after a price spike than in the normal period and price volatility is more than fourteen times higher in spike periods than in normal periods the probability of a spike on any given day ranges between 5 16 in nsw and 9 44 in victoria

forecasting the joint distribution of australian electricity prices using dynamic vine copulae abstract we consider the problem of modelling and forecasting the distribution of a vector of prices from interconnected electricity markets using a flexible class of drawable vine copula models where we allow the dependence parameters of the constituting bivariate copulae to be time varying we undertake in sample and out of sample tests using daily electricity prices and evidence that our model provides accurate forecasts of the underlying distribution and outperforms a set of competing models in their abilities to forecast one day ahead conditional quantiles of a portfolio of electricity prices our study is conducted in the australian national electricity market nem which is the most efficient power auction in the world electricity prices exhibit highly stylised features such as extreme price spikes price dependency between regional markets correlation asymmetry and non linear dependency the developed approach can be used as a risk management tool in the electricity retail industry which plays an integral role in the apparatus of modern energy markets electricity retailers are responsible for the efficient distribution of electricity while being exposed to market risk with extreme magnitudes

profit related pay prose discovered this paper estimates that in 1984 43 percent of british private sector establishments had some form of profit related pay regression results do not show that these establishments had statistically significant better financial performance cross ta bulations do not suggest that the establishments with income sharing schemes had noticeably greater employment growth or better industrial relations copyright 1988 by royal economic society

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

modelling price and volatility inter relationships in the australian wholesale spot electricity markets this paper examines the inter relationships of wholesale spot electricity prices among the four regional electricity markets in the australian national electricity market nem namely new south wales queensland south australia and victoria using the constant conditional correlation and tse and tsui s tse y k tsui a k c 2002 a multivariate generalised autoregressive conditional heteroscedasticity model with time varying correlations journal of business and economic statistics 20 3 351 362 and engle s engle r 2002 dynamic conditional correlation a sample class of multivariate generalized autoregressive conditional heteroskedasticity models journal of business and economic statistics 20 3 339 350 dynamic conditional correlation multivariate garch models tse and tsui s tse y k tsui a k c 2002 a multivariate generalised autoregressive conditional heteroscedasticity model with time varying correlations journal of business and economic statistics 20 3 351 362 dynamic conditional correlation multivariate garch model which takes account of the student t specification produces the best results at the univariate garch 1 1 level the mean equations indicate the presence of positive own mean spillovers in all four markets and little evidence of mean spillovers from the other lagged markets in the dynamic conditional correlation equation the highest conditional correlations are evident between the well connected markets indicating the presence of strong interdependence between these markets with weaker interdependence between the not so well interconnected markets

the simple macroeconomics of profit sharing this paper is in the spirit of the temporary equilibrium approach to macroeconomics it basically extends that framework to cover a profit sharing system and then compares the macroeconomic characteristics with those of the more familiar wage system a first preliminary step is to demonstrate how a microeconomic model of monopolistic competition can be built up into a junior member of the keynesian macro family the methodology is to create from first principles including a careful formulation of a monopolistically competitive product market structurea natural underpinning for the standard aggregate demand specification the primary goal of the paper is to apply the integrated monopolistic competitionkeynesian type apparatus described above to investigate the macroeconomic properties of a profit sharing economy the existence of a consistent general framework covering both cases invites meaningful comparisons that indicate clearly why an economy based on profit sharing principles possesses natural immunity to stagflation by contrast the wage economya system we have largely accepted without critically examining its macroeconomic consequences is more prone to suffer from unemployment and inflation the policy implications for aggregate demand management in wage and share systems are analyzed and contrasted in writing this paper my philosophy has been to not shirk from using those reasonable parameterizations and functional forms which yield nice crisp results and permit me to focus sharply on the essential logic of basic issues it is certainly possible to present the main results in a somewhat more general formulation as the astute reader will appreciate but i fear only at some cost of distracting attention from those central features i wish to highlight

profit sharing employment stability and wage growth the authors conjecture that profit sharing reduces turnover and thus increases expected returns to firm specific human capital investments so that the optimal levels of skill acquisition and investment in firm specific skills rise and ultimately increase productivity empirical evidence from nlsy data on white men in nonunion jobs between 1988 and 1994 supports this hypothesis employees participating in profit sharing plans were less likely than non participants to separate from their jobs they also received training more frequently and for longer durations finally the authors show that profit sharing was related to higher wage growth indicating a faster rate of skill accumulation

dynamic stochastic copula models estimation inference and applications we propose a new dynamic copula model where the parameter characterizing dependence follows an autoregressive process as this model class includes the gaussian copula with stochastic correlation process it can be viewed as a generalization of multivariate stochastic volatility models despite the complexity of the model the decoupling of marginals and dependence parameters facilitates estimation we propose estimation in two steps where rst the parameters of the marginal distributions are estimated and then those of the copula parameters of the latent processes volatilities and dependence are estimated using efficient importance sampling eis we discuss goodness of t tests and ways to forecast the dependence parameter for two bivariate stock index series we show that the proposed model outperforms standard competing models

modeling and forecasting multivariate electricity price spikes abstract we consider the problem of forecasting the occurrence of extreme prices in the australian electricity markets from high frequency spot prices in particular we are interested in the simultaneous occurrence of such so called spikes in two or more markets our approach is based on a novel dynamic model for multivariate binary outcomes which allows the latent variables driving these observed outcomes to follow a vector autoregressive process furthermore the model is constructed using a copula representation for the joint distribution of the resulting innovations this has several advantages over the standard multivariate probit model first it allows for nonlinear dependence between the error terms second the distribution of the latent errors can be chosen freely third the computational burden can be greatly reduced making estimation feasible in higher dimensions and for large samples the model is applied to spikes in half hourly electricity prices in four interconnected australian markets the multivariate model provides a superior fit compared to single equation models and generates better forecasts for spike probabilities furthermore evidence of spillover dynamics between the markets is revealed

union wage concessions in the 1980s the importance of firm specific factors this paper evaluates the effects of firm performance and firm characteristics on concession outcomes over the years 1980 87 across similar firms the author finds concessions were inversely related to stock price and employment growth concessions were also most likely in small firms in firms paying high wages and in firms with relatively low union coverage the effect of firm performance and firm characteristics on the likelihood of concessions was uniform across concessions of differing severity and was stable in magnitude over the eight year period

spot price modeling and the valuation of electricity forward contracts the role of demand and capacity we propose a model where wholesale electricity prices are explained by two state variables demand and capacity we derive analytical expressions to price forward contracts and to calculate the forward premium we apply our model to the pjm england and wales and nord pool markets our empirical findings indicate that volatility of demand is seasonal and that the market price of demand risk is also seasonal and positive both of which exert an upward seasonal pressure on the price of forward contracts we assume that both volatility of capacity and the market price of capacity risk are constant and find that depending on the market and period under study it could either exert an upward or downward pressure on forward prices in all markets we find that the forward premium exhibits a seasonal pattern during the months of high volatility of demand forward contracts trade at a premium during months of low volatility of demand forwards can either trade at a relatively small premium or even in some cases at a discount i e they exhibit a negative forward premium

lump sum payments and profit sharing plans in the union sector of the united states economy lump sum payments and profit sharing plans became increasingly prevalent in union contracts in the u s economy in the 1980s this paper analyzes the relationship between lump sums profit sharing labor costs and employment in firm level panel data for the union sector of the u s economy in general the firm level data suggest that profit sharing has a statistically significant effect of lowering labor cost growth profit sharing also appears to increase employment growth and reduce employment variability but these results are not statistically significant this evidence is generally consistent with hypotheses regarding the introduction of performance related pay weitzman 1984 and 1985 the effects of lump sums on the other hand are not consistent with hypotheses regarding performance related pay lump sums do not appear to lower labor cost growth or increase employment growth and increase rather than decrease employment variability copyright 1993 by royal economic society

caviar conditional autoregressive value at risk by regression quantiles value at risk var has become the standard measure of market risk employed by financial institutions for both internal and regulatory purposes var is defined as the value that a portfolio will lose with a given probability over a certain time horizon usually one or ten days despite its conceptual simplicity its measurement is a very challenging statistical problem and none of the methodologies developed so far give satisfactory solutions interpreting the var as the quantile of future portfolio values conditional on current information we propose a new approach to quantile estimation which does not require any of the extreme assumptions invoked by existing methodologies such as normality or i i d returns the conditional autoregressive value at risk or caviar model moves the focus of attention from the distribution of returns directly to the behavior of the quantile we specify the evolution of the quantile over time using a special type of autoregressive process and use the regression quantile framework introduced by koenker and bassett to determine the unknown parameters since the objective function is not differentiable we use a differential evolutionary genetic algorithm for the numerical optimization utilizing the criterion that each period the probability of exceeding the var must be independent of all the past information we introduce a new test of model adequacy the dynamic quantile test applications to simulated and real data provide empirical support to this methodology and illustrate the ability of these algorithms to adapt to new risk environments