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

liquidity constraints and deforestation the limitations of payments for ecosystem services abstract a popular environmental policy is to pay forest owners for avoiding deforestation on their land this is an example of payments for ecosystem services pes this paper shows that liquidity constraints can limit the effectiveness of pes programs if an individual would have cut down trees to sell them his opportunity costs are more front loaded than the stream of pes income if credit constrained he might decline the program even if the net present value npv of the pes income exceeds the npv of his opportunity costs i present evidence consistent with this prediction using data on forest owners in uganda

strategic interactions and portfolio choice in money management theory and evidence the author study portfolio choice of strategic fund managers in the presence of a peer based under performance penalty while the penalty generates herding behavior correlated trading among managers is exacerbated when a strategic setting is considered the equilibrium portfolios are driven by the least restricted manager who may vary according to the realization of returns the author compare model predictions to evidence from the colombian pension fund management industry where six asset managers are incharge of portfolio allocation for the mandatory contributions of the working population these managers are subject to a peer based under performance penalty known as the minimum return guarantee mrg the author study trading behavior by managers before and after a change in the strictness of the mrg in june 2007 the evidence suggests that a tighter mrg results in more trading in the direction of peers a behavior that is more pronounced for under performing managers is how that these findings areconsistent with the qualitative and quantitative predictions of the theoretical model

time dependent correlations in electricity markets in the last years many electricity markets were subjected to deregulated operation where prices are set by the action of market participants in this form producers and consumers rely on demand and price forecasts to decide their bidding strategies allocate assets negotiate bilateral contracts hedge risks and plan facility investments a basic feature of efficient market hypothesis is the absence of correlations between price increments over any time scale leading to random walk type behavior of prices so arbitrage is not possible however recent studies have suggested that this is not the case and correlations are present in the behavior of diverse electricity markets in this paper a temporal quantification of electricity market correlations is made by means of detrended fluctuation and allan analyses the approach is applied to two canadian electricity markets ontario and alberta the results show the existence of correlations in both demand and prices exhibiting complex time dependent behavior with lower correlations in winter while higher in summer relatively steady annual cycles in demand but unstable cycles in prices are detected on the other hand the more significant nonlinear effects measured in terms of a multifractality index are found for winter months while the converse behavior is displayed during the summer period in terms of forecasting models our results suggest that nonlinear recursive models e g feedback nns should be used for accurate day ahead price estimation in contrast linear models can suffice for demand forecasting purposes

predictability of price movements in deregulated electricity markets abstract in this paper we investigate predictability of electricity prices in the canadian provinces of alberta and ontario as well as in the us mid c market using scale dependent detrended fluctuation analysis spectral analysis and the probability distribution analysis we show that the studied markets exhibit strongly anti persistent properties suggesting that their dynamics can be predicted based on historic price records across the range of time scales from 1 h to one month for both canadian markets the price movements reveal three types of correlated behavior which can be used for forecasting the discovered scenarios remain the same on different time scales up to one month as well as for on and off peak electricity data these scenarios represent sharp increases of prices and are not present in the mid c market due to its lower volatility we argue that extreme price movements in this market should follow the same tendency as the more volatile canadian markets the estimated values of the pareto indices suggest that the prediction of these events can be statistically stable the results obtained provide new relevant information for managing financial risks associated with the dynamics of electricity derivatives over time frame exceeding one day

commodity currencies and commodity prices modelling static and time varying dependence abstractthis article employs the copula approach to study the relationship between exchange rates and commodity prices for large commodity exporters using data for the nominal exchange rates of four commodity currencies australian canadian and new zealand dollars and norwegian krone against the us dollar and the relevant country specific commodity price indices constructed on a daily basis we find 1 a positive dependence between the values of commodity currencies and commodity indices i e a commodity index increases when a respective currency appreciates and provides several explanations for this finding 2 no major asymmetries in the tail dependence for most pairs of exchange rates and commodity indices and 3 a pronounced increase in the time varying tail dependence following the global financial crisis

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

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

modelling interregional links in electricity price spikes abnormally high price spikes in spot electricity markets represent a significant risk to market participants as such a literature has developed that focuses on forecasting the probability of such spike events moving beyond simply forecasting the level of price many univariate time series models have been proposed to dealwith spikes within an individual market region this paper is the first to develop a multivariate self exciting point process model for dealing with price spikes across connected regions in the australian national electricity market the importance of the physical infrastructure connecting the regions on the transmission of spikes is examined it is found that spikes are transmitted between the regions and the size of spikes is influenced by the available transmission capacity it is also found that improved risk estimates are obtained when inter regional linkages are taken into account

bidding in sequential electricity markets the nordic case abstract for electricity market participants trading in sequential markets with differences in price levels and risk exposure it is relevant to analyze the potential of coordinated bidding we consider a nordic power producer who engages in the day ahead spot market and the hour ahead balancing market in both markets clearing prices and dispatched volumes are unknown at the time of bidding however in the balancing market the market participant faces an additional risk of not being dispatched taking into account the sequential clearing of these markets and the gradual realization of market prices we formulate the bidding problem as a multi stage stochastic program we investigate whether higher risk exposure may cause hesitation to bid into the balancing market furthermore we quantify the gain from coordinated bidding and by deriving bounds on this gain assess the performance of alternative bidding strategies used in practice

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

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

hedging local volume risk using forward markets nordic case abstract with focus on the nordic electricity market this paper develops hedging strategies for an electricity distributor who manages price and volume risk from fixed price agreements on stochastic electricity load whereas the distributor trades in the spot market at area prices the financial contracts used for hedging are settled against the system price area and system prices are correlated with electricity load as are price differences in practice however this is often disregarded here we develop a joint model for the area price the system price and the load accounting for correlations and we suggest various strategies for hedging in the presence of local volume risk we benchmark against a strategy that ignores correlation and hedges at expected load as is common practice in the industry using data from 2013 and 2014 for two danish bidding areas we show that our best hedging strategy reduces gross loss by 5 8 and 13 6 and increases gross profit by 3 8 and 9 5 respectively although this is partly due to the inclusion of correlation we show that performance improvement is mainly driven by the choice of risk measure

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 statistical approach for interval forecasting of the electricity price electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market rather than forecasting the value market participants are sometimes more interested in forecasting the prediction interval of the electricity price forecasting the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions in this paper a novel data mining based approach is proposed to achieve two major objectives 1 to accurately forecast the value of the electricity price series which is widely accepted as a nonlinear time series 2 to accurately estimate the prediction interval of the electricity price series in the proposed approach support vector machine svm is employed to forecast the value of the price to forecast the prediction interval we construct a statistical model by introducing a heteroscedastic variance equation for the svm maximum likelihood estimation mle is used to estimate model parameters results from the case studies on real world price data prove that the proposed method is highly effective compared with existing methods such as garch models

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

short term congestion forecasting in wholesale power markets short term congestion forecasting is highly important for market participants in wholesale power markets that use locational marginal prices lmps to manage congestion accurate congestion forecasting facilitates market traders in bidding and trading activities and assists market operators in system planning this study proposes a new short term forecasting algorithm for congestion lmps and other power system variables based on the concept of system patterns combinations of status flags for generating units and transmission lines the advantage of this algorithm relative to standard statistical forecasting methods is that structural aspects underlying power market operations are exploited to reduce forecast error the advantage relative to previously proposed structural forecasting methods is that data requirements are substantially reduced forecasting results based on a nyiso case study demonstrate the feasibility and accuracy of the proposed algorithm

generation scheduling in a deregulated system the norwegian case the paper gives an overview of the deregulated electricity supply system in norway with emphasis on the generation scheduling approach procedures and tools for long medium and short term scheduling are described scheduling is carried out by each individual utility since scheduling is based on profit maximizing the spot market price is a very important factor this price is varying and uncertain however a part of the paper describes how to deal with this by considering the spot market price as an exogenously given stochastic variable it is shown how to extend the common water value computations so as to handle this case implications to the medium and short term scheduling are also discussed

simulating oligopolistic pool based electricity markets a multiperiod approach this paper simulates a pool based electricity market and identifies equilibrium patterns a multiperiod framework that requires the use of continuous and binary variables is considered producers can be either price takers or price makers although price makers and their behavior characterize the market the elasticity of the loads is modeled through piecewise constant price demand curves the market operator uses a detailed network constrained market clearing auction that results in hourly market clearing prices the behavior of the market participants and the market itself are characterized through a repetitive simulation procedure the tool presented in this paper is particularly useful for the market regulator that may use it to monitor the market and to identify the exercise of market power by producers results from a realistic case study are presented and discussed

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

risk premiums in the german day ahead electricity market this paper conducts an empirical analysis of risk premiums in the german day ahead electricity wholesale market we compare hourly price data of the european energy exchange eex auction and of the continuous over the counter otc market which takes place prior to the eex auction data provided by the energy exchange austria exaa has been used as a snapshot of the otc market two hours prior to the eex auction ex post analysis found market participants are willing to pay both significant positive and negative premiums for hourly contracts the largest positive premiums were paid for high demand evening peak hours on weekdays during winter months by contrast night hours on weekends featuring lowest demand levels display negative premiums additionally ex ante analysis found a strong positive correlation between the expected tightness of the system and positive premiums for this purpose a tightness factor has been introduced that includes expectations of fundamental factors such as power plant availability wind power production and demand hence findings by longstaff and wang 2004 can be supported that power traders in liberalised markets behave like risk averse rational economic agents

india s dependence on foreign oil will exceed 90 around 2025 the forecasting results based on two hybridized nmgm arima and nmgm bp models abstract few study has been conducted to forecast india s dependence on foreign oil although india is the world s third largest oil consumer and exporter making it a key player in the oil market this work is aimed to accurately forecast india s dependence on foreign oil to develop better forecasting techniques this study used linear auto regressive integrated moving average arima and nonlinear back propagation bp to correct nonlinear metabolic grey model nmgm forecasting residuals in three steps i integrating the metabolic idea with a nonlinear grey model to develop nmgm ii integrating the proposed nmgm with arima to develop nmgm arima and iii integrating the proposed nmgm with bp to develop nmgm bp to further improve forecasting accuracy this work forecasted the india s dependence on foreign oil from two perspectives i direct forecasting the change of ratio of imports oil to total oil consumption in india ii indirect forecasting i e the difference between the forecasted oil consumption and production was divided by the forecasted oil production the proposed nmgm arima and nmgm bp were used to fit the india s dependence on foreign oil for 1995 2017 and forecast the data for 2018 2030 the mean relative error of the proposed forecasting models was around 1 5 which could produce a convincing forecasting results our forecasting results show that india s dependence on foreign oil will reach 90 around 2025 which poses serious challenge to indian oil security and the global oil market

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

a real option based simulation model to evaluate investments in pump storage plants investments in pump storage plants are expected to grow especially due to their ability to store an excess of supply from wind power plants in order to evaluate these investments correctly the peculiarities of pump storage plants and the characteristics of liberalized power markets have to be considered the main characteristics of power markets are the strong power price volatility and the occurrence of prices spikes in this article a valuation model is developed capturing these aspects using power price simulation optimization of unit commitment and capital market theory this valuation model is able to value a future price based unit commitment planning that corresponds to future scope of actions also called real options the resulting real option value for the pump storage plant is compared with the traditional net present value approach because this approach is not able to evaluate scope of actions correctly it results in strongly smaller investment values and forces wrong investment decisions

are daily and weekly load and spot price dynamics in australia s national electricity market governed by episodic nonlinearity in this article we use half hourly spot electricity prices and load data for the national electricity market nem of australia for the period from december 1998 to june 2009 to test for episodic nonlinearity in the dynamics governing daily and weekly cycles in load and spot price time series data we apply the portmanteau correlation bicorrelation and tricorrelation tests introduced in hinich 1996 to the time series of half hourly spot prices and load demand from 7 12 1998 to 30 06 2009 using a fortran 95 program we find the presence of significant third and fourth order nonlinear serial dependence in the weekly load and spot price data in particular but to a much more marginal extent in the daily data

applying arma garch approaches to forecasting short term electricity prices abstract accurately modeling and predicting the mean and volatility of electricity prices can be of great importance to value electricity bid or hedge against the volatility of electricity prices and manage risk the paper applies various autoregressive moving average arma models with generalized autoregressive conditional heteroskedasticity garch processes namely arma garch models along with their modified forms arma garch in mean arma garch m to model and forecast hourly ahead electricity prices in total 10 different model structures are adopted and this paper thus conducts a comprehensive investigation on the arma garch based time series forecasting of electricity prices multiple statistical measures are employed to evaluate the modeling sufficiency and predication accuracy of the arma garch m methods the results show that the arma garch m models are in general an effective tool for modeling and forecasting the mean and volatility of electricity prices while arma sgarch m models are simple and robust and the arma gjrgarch m model is very competitive in addition we observe that hourly electricity prices exhibit apparent daily weekly and monthly periodicities and have the nonlinear and asymmetric time varying volatility together with an inverse leverage effect