forecasting day ahead electricity load using a multiple equation time series approach the quality of short term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market in this paper it is shown that a multiple equation time series model which is estimated by repeated application of ordinary least squares has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models the key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra day dependencies although the model is built using data for the queensland region of australia the method is completely generic and applicable to any load forecasting problem the model s forecasting ability is assessed by means of the mean absolute percentage error mape for day ahead forecast the mape returned by the model over a period of 11 years is an impressive 1 36 the forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the australia energy market operator aemo the performance of the model developed in this paper is superior to all benchmarks and outperforms the aemo forecasts by about a third in terms of the mape criterion

the non linear link between electricity consumption and temperature in europe a threshold panel approach this paper investigates the relationship between electricity demand and temperature in the european union we address this issue by means of a panel threshold regression model on 15 european countries over the last two decades our results confirm the non linearity of the link between electricity consumption and temperature found in more limited geographical areas in previous studies by distinguishing between north and south countries we also find that this non linear pattern is more pronounced in the warm countries finally rolling regressions show that the sensitivity of electricity consumption to temperature in summer has increased in the recent period

estimating financial risk measures for options abstract this paper proposes a simulation lattice procedure to estimate financial risk measures for option positions the framework proposed can be applied to many different kinds of options including exotic and vanilla options it can take account of early exercise features heavy tails in underlying processes estimate different risk measures including var expected shortfall and spectral risk measures and in a limited way it can be generalized to accommodate multiple factors it avoids many of the limitations of existing approaches and in particular avoids the problems associated approaches based on delta gamma and similar approximations it also generates some interesting results about the risk measures of some illustrative options positions

accuracy in the determination of liability many legal rules notably rules of procedure and evidence are concerned with achieving accuracy in the outcome of adjudication in this article we study accuracy in the conventional model of law enforcement we consider why reducing error in determining liability is socially valuable and how error and its reduction affect the optimal probability and magnitude of sanctions this abstract was borrowed from another version of this item

combining day ahead forecasts for british electricity prices abstract this paper considers how well the approach of combining forecasts extends to the context of electricity prices with the increasing popularity of regime switching and time varying parameter models for predicting power prices the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features we address this question with a backtesting analysis on british day ahead prices furthermore given the volatility of power prices and concerns about accurate forecasting under extreme price excursions we evaluate the results using various error metrics including expected shortfall the comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy overall our results support combining for both accurate operational planning and risk management

derivative pricing using multivariate affine generalized hyperbolic distributions abstract in this paper we use multivariate affine generalized hyperbolic magh distributions introduced by schmidt et al 2006 to show how to price multidimensional derivatives when the underlying asset follows a magh distribution we also illustrate the approach using market data from the bovespa sao paulo stock exchange and the exchange rate of the brazilian real vs us dollar to price some multidimensional derivatives

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

moral rules the moral sentiments and behavior toward a theory of an optimal moral system how should moral sanctions and moral rewards the moral sentiments involving feelings of guilt and of virtue be employed to govern individuals behavior if the objective is to maximize social welfare in the model that we examine guilt is a disincentive to act and virtue is an incentive because we assume that they are negative and positive sources of utility we also suppose that guilt and virtue are costly to inculcate and are subject to certain constraints on their use we show that the moral sentiments should be used chiefly to control externalities and further that guilt is best to employ when most harmful acts can successfully be deterred whereas virtue is best when only a few individuals can be induced to behave well we also contrast the optimal use of guilt and virtue to optimal pigouvian taxation and discuss extensions of our analysis

empirical martingale simulation for asset prices this paper proposes a simple modification to the standard monte carlo simulation procedure for computing the prices of derivative securities the modification imposes the martingale property on the simulated sample paths of the underlying asset price this procedure is referred to as the empirical martingale simulation ems the ems ensures that the price estimated by simulation satisfies the rational option pricing bounds the ems yields a substantial error reduction for the price estimate and can be easily coupled with the standard variance reduction methods simulation studies are conducted for european and asian call options using both the black and scholes and garch option pricing frameworks the results indicate that the ems yields substantial variance reduction particularly for in and at the money or longer maturity options the option price estimate based on the ems is found to exhibit a minor small sample bias only in few occasions an analysis of the trade off between computing time and price accuracy reveals that the ems dominates the conventional simulation methods

short term load forecasting based on a semi parametric additive model short term load forecasting is an essential instrument in power system planning operation and control many operating decisions are based on load forecasts such as dispatch scheduling of generating capacity reliability analysis and maintenance planning for the generators overestimation of electricity demand will cause a conservative operation which leads to the start up of too many units or excessive energy purchase thereby supplying an unnecessary level of reserve on the other hand underestimation may result in a risky operation with insufficient preparation of spinning reserve causing the system to operate in a vulnerable region to the disturbance in this paper semi parametric additive models are proposed to estimate the relationships between demand and the driver variables specifically the inputs for these models are calendar variables lagged actual demand observations and historical and forecast temperature traces for one or more sites in the target power system in addition to point forecasts prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data the proposed methodology has been used to forecast the half hourly electricity demand for up to seven days ahead for power systems in the australian national electricity market the performance of the methodology is validated via out of sample experiments with real data from the power system as well as through on site implementation by the system operator

tempered stable and tempered infinitely divisible garch models abstract in this paper we introduce a new garch model with an infinitely divisible distributed innovation this model which we refer to as the rapidly decreasing tempered stable rdts garch model takes into account empirical facts that have been observed for stock and index returns such as volatility clustering non zero skewness and excess kurtosis for the residual distribution we review the classical tempered stable cts garch model which has similar statistical properties by considering a proper density transformation between infinitely divisible random variables we can find the risk neutral price process thereby allowing application to option pricing we propose algorithms to generate scenarios based on garch models with cts and rdts innovations to investigate the performance of these garch models we report parameter estimates for the dow jones industrial average index and stocks included in this index to demonstrate the advantages of the proposed model we calculate option prices based on the index

forecasting quantiles of day ahead electricity load accurate load forecasting plays a crucial role in the decision making process of many market participants but probably is most important for the dispatch planning of an electricity market operator despite the competitive forecast accuracy achieved by existing point forecast models point forecasts can only provide limited information relating to the expected level of future load to account for the uncertainty of future load and provide a more complete picture of the future load conditions for dispatch planning purposes quantile forecasts can be useful this paper proposes a computationally efficient approach to forecasting the quantiles of electricity load which is then applied to forecasting in the national electricity market of australia the proposed model performs competitively in comparison with one industry standard and two recently proposed quantile forecasting methods one of the main advantages of the proposed approach is the ease with the number of covariates can be expanded this is a particularly important feature in the context of load forecasting where large numbers of important drivers are usually necessary to provide accurate load forecasts

foster hart optimal portfolios we reinvestigate the classic portfolio optimization problem where the notion of portfolio risk is captured by the foster hart risk a new bankruptcy proof reserve based measure of risk extremely sensitive to left tail events foster and hart 2009 to include financial market frictions induced by market microstructure we employ a general ex ante transaction cost function with fixed linear and quadratic penalty terms in the objective function we represent the us equity market by the dow jones industrial average djia index and study the performance of the foster hart optimal djia portfolio in order to capture the skewed and leptokurtotic nature of real life stock returns we model the returns of the djia constituents as an arma garch process with multivariate normal tempered stable innovations we demonstrate that the foster hart optimal portfolio s performance is superior to those obtained under several techniques currently in use in academia and industry

probabilistic forecasts of the magnitude and timing of peak electricity demand adequate capacity planning requires accurate forecasts of the future magnitude and timing of peak electricity demand electricity demand is affected by the day of the week seasonal variations holiday periods feast days and the weather a model that provides probabilistic forecasts of both magnitude and timing for lead times of one year is presented this model is capable of capturing the main sources of variation in demand and uses simulated weather time series including temperature wind speed and luminosity for producing probabilistic forecasts of future peak demand having access to such probabilistic forecasts provides a means of assessing the uncertainty in the forecasts and can lead to improved decision making and better risk management

the dynamics of electricity demand in pakistan a panel cointegration analysis abstract this paper examines the dynamics of electricity demand in pakistan at the aggregate and sectoral levels over the period 1978 2012 panel cointegration test and fully modified ordinary least squares method is employed to determine the long run relationship between electricity consumption real income real price of electricity and domestic price of non energy products the results reveal that electricity demand is more responsive to changes in income than changes in prices at the aggregate and disaggregate levels short run and long run income elasticities are positive and statistically significant supporting the conservation hypothesis it appears from the results that long run price elasticities are negative and significant at the aggregate level and for the agriculture commercial residential sectors and public utilities the short run price elasticities are significant but low in magnitude which implies that changes in electricity price exert minimal effect on the electricity consumption in pakistan the domestic price of non energy products is positive and significant for aggregate sample in the short run the domestic price of non energy products exert significant negative positive impact on electricity demand in the agriculture industrial sectors the results thus provide important information to the agents operating in the electricity market regarding the income and pricing policies and helps in planning the future strategy of electricity demand management

carbon pricing and electricity markets the case of the australian clean energy bill abstract we examine the impact of the clean energy bill on the price behavior of electricity futures contracts in the australian national electricity market first we compute ex ante forward risk premiums in the pre tax period until june 2012 then derive market implied expectations about additional costs of the carbon pricing mechanism cpm on generators as well as pass through rates during the carbon tax july 2012 june 2014 and post tax after july 2014 periods our results suggest that the observed carbon premiums became increasingly higher once the carbon tax had been proposed and subsequently legislated in 2011 during periods where market participants could be relatively certain that the tax would be effective we find expected carbon pass through rates between 67 and 150 which seem to be inversely related to emission intensities in the regional markets our results are also a clear indication of strong policy uncertainty with regards to the cpm and suggest that in the future a stable and long term policy framework would be required for a carbon pricing mechanism to have its full effect

the impact of fine size and uncertainty on punishment and deterrence theory and evidence from the laboratory abstract increasing punishment is typically considered first choice to boost deterrence of unwarranted behavior such as false financial statements asset misappropriation stealing or corruption however if there is uncertainty on a potential violator s guilt judges and juries willingness to impose punishment may decrease in its magnitude thus increasing the magnitude of punishment may backfire when the reduced punishment probability is anticipated by potential violators based on a theoretical model our paper is the first to analyze the interdependency of violation and punishment behavior in a laboratory experiment and to contrast it to the standard partial equilibrium perspective on deterrence that considers the punishment probability to be independent of the fine size varying both the magnitude of fines and the degree of uncertainty shows that in case of legal uncertainty the deterrent effect of higher fines is far less pronounced than if the punishment probability was exogenous

a capacitated single item lot size model abstract wagner and whitin s inventory model is generalized to incorporate a limited production rate the model is conspicuous amongst related models in the literature as wagner and whitin s original algorithm may be applied the result requires restrictions on the cost coefficients that make conventional material requirements planning the model s natural field of application

crime in the lab detecting social interaction abstract according to the standard economic perspective criminal activities vary with the price of crime which depends on the severity of punishment and the detection probability an alternative view claims that crime is also influenced by social interaction phenomena such as peer pressure or neighbourhood effects detecting social interaction in the field is fraught with difficulties however in this paper we argue that given its extensive control possibilities experiments are well suited to show the potential existence of social interaction effects we present an experimental study where people can engage in criminal activities we find support for the importance of social interaction on average subjects steal the more the more others steal this behaviour can be interpreted in terms of reciprocity

forecasting scenarios for uk household expenditure and associated ghg emissions outlook to 2030 using the modelling tool elesa econometric lifestyle environment scenario analysis this paper describes forecast scenarios to 2030 for uk household expenditure and associated direct and indirect greenhouse gas ghg emissions for 16 expenditure categories using assumptions for real household disposable income real prices exogenous non economic factors exnef average uk temperatures and ghg intensities three future scenarios are constructed in each scenario real expenditure for almost all categories of uk expenditure continues to grow up to 2030 the exceptions being alcoholic beverages and tobacco and other fuels and gas and electricity in the low scenario leading to an increase in associated ghg emissions for most of the categories in the reference and high scenarios other than food and non alcoholic beverages alcoholic beverages and tobacco electricity other fuels and recreation and culture of the future ghg emissions about 30 is attributed to direct energy use by households and nearly 70 attributable to indirect energy uk policy makers therefore need to consider a range of policies if they wish to curtail emissions associated with household expenditure including for example economic measures such as taxes alongside measures that reflect the important contribution of exnef to changes in expenditure for most categories of consumption

judicial errors and crime deterrence theory and experimental evidence abstractthe economic theory of crime deterrence predicts that the conviction of an innocent individual type i error is as detrimental to deterrence as the acquittal of a guilty individual type ii error in this paper we qualify this result theoretically showing that in the presence of risk aversion loss aversion or type i error aversion type i errors have a stronger effect on deterrence than type ii errors we test these predictions with two experimental studies in which participants choose whether to steal from other individuals under alternative combinations of probabilities of judicial errors the results indicate that both types of errors have a significant impact on deterrence as predicted type i errors have a stronger impact on deterrence than type ii errors this asymmetry is entirely explained by differences in the expected utility gains from crime whereas nonexpected utility factors do not play a significant role

pricing discretely monitored asian options under levy processes we present methodologies to price discretely monitored asian options when the underlying evolves according to a generic levy process for geometric asian options we provide closed form solutions in terms of the fourier transform and we study in particular these formulas in the levy stable case for arithmetic asian options we solve the valuation problem by recursive integration and derive a recursive theoretical formula for the moments to check the accuracy of the results we compare the implementation of our method to monte carlo simulation implemented with control variates and using different parametric levy processes we also discuss model risk issues

momentum strategies based on reward risk stock selection criteria abstract in this paper we analyze momentum strategies that are based on reward risk stock selection criteria in contrast to ordinary momentum strategies based on a cumulative return criterion reward risk stock selection criteria include the standard sharpe ratio with variance as a risk measure and alternative reward risk ratios with the expected shortfall as a risk measure we investigate momentum strategies using 517 stocks in the s p 500 universe in the period 1996 2003 although the cumulative return criterion provides the highest average monthly momentum profits of 1 3 compared to the monthly profit of 0 86 for the best alternative criterion the alternative ratios provide better risk adjusted returns measured on an independent risk adjusted performance measure we also provide evidence on unique distributional properties of extreme momentum portfolios analyzed within the framework of general non normal stable paretian distributions specifically for every stock selection criterion loser portfolios have the lowest tail index and tail index of winner portfolios is lower than that of middle deciles the lower tail index is associated with a lower mean strategy the lowest tail index is obtained for the cumulative return strategy given our data set these findings indicate that the cumulative return strategy obtains higher profits with the acceptance of higher tail risk while strategies based on reward risk criteria obtain better risk adjusted performance with the acceptance of the lower tail risk

financial market models with levy processes and time varying volatility abstract asset management and pricing models require the proper modeling of the return distribution of financial assets while the return distribution used in the traditional theories of asset pricing and portfolio selection is the normal distribution numerous studies that have investigated the empirical behavior of asset returns in financial markets throughout the world reject the hypothesis that asset return distributions are normally distribution alternative models for describing return distributions have been proposed since the 1960s with the strongest empirical and theoretical support being provided for the family of stable distributions with the normal distribution being a special case of this distribution since the turn of the century specific forms of the stable distribution have been proposed and tested that better fit the observed behavior of historical return distributions more specifically subclasses of the tempered stable distribution have been proposed in this paper we propose one such subclass of the tempered stable distribution which we refer to as the kr distribution we empirically test this distribution as well as two other recently proposed subclasses of the tempered stable distribution the carr geman madan yor cgmy distribution and the modified tempered stable mts distribution the advantage of the kr distribution over the other two distributions is that it has more flexible tail parameters for these three subclasses of the tempered stable distribution which are infinitely divisible and have exponential moments for some neighborhood of zero we generate the exponential levy market models induced from them we then construct a new garch model with the infinitely divisible distributed innovation and three subclasses of that garch model that incorporates three observed properties of asset returns volatility clustering fat tails and skewness we formulate the algorithm to find the risk neutral return processes for those garch models using the change of measure for the tempered stable distributions to compare the performance of those exponential levy models and the garch models we report the results of the parameters estimated for the s p 500 index and investigate the out of sample forecasting performance for those garch models for the s p 500 option prices

long run relations in european electricity prices this paper analyses the interdependencies existing in wholesale electricity prices in six major european countries the results of a robust multivariate long run dynamic analysis reveal the presence of four highly integrated central european markets france germany the netherlands and austria the trend shared by these four electricity markets appears to be common also to gas prices but not to oil prices the existence of a common long term dynamics among electricity prices and between electricity prices and gas prices can be explained by the similarity of the market design across europe and by the same marginal generation technology since standard unit root and cointegration tests are not robust to the peculiar characteristics of electricity prices time series we also develop a battery of robust inference procedures that should assure the reliability of our results copyright 2009 john wiley sons ltd

modeling special day effects for forecasting intraday electricity demand abstract we propose and apply a novel approach for modeling special day effects to predict electricity demand in korea notably we model special day effects on an hourly rather than a daily basis hourly specified predictor variables are implemented in the regression model with a seasonal autoregressive moving average sarma type error structure in order to efficiently reflect the special day effects the interaction terms between the hour of day effects and the hourly based special day effects are also included to capture the unique intraday patterns of special days more accurately the multiplicative sarma mechanism is employed in order to identify the double seasonal cycles namely the intraday effect and the intraweek effect the forecast results of the suggested model are evaluated by comparing them with those of various benchmark models for the following year the empirical results indicate that the suggested model outperforms the benchmark models for both special and non special day predictions

an empirical comparison of alternate regime switching models for electricity spot prices one of the most profound features of electricity spot prices are the price spikes markov regime switching mrs models seem to be a natural candidate for modeling this spiky behavior however in the studies published so far the goodness of fit of the proposed models has not been a major focus while most of the models were elegant their fit to empirical data has either been not examined thoroughly or the signs of a bad fit ignored with this paper we want to fill the gap we calibrate and test a range of mrs models in an attempt to find parsimonious specifications that not only address the main characteristics of electricity prices but are statistically sound as well we find that the most universal and robust structure is that of an independent spike 3 regime model with heteroscedastic diffusion type base regime dynamics and shifted spike regime distributions

forecasting daily supermarket sales using exponentially weighted quantile regression inventory control systems typically require the frequent updating of forecasts for many different products in addition to point predictions interval forecasts are needed to set appropriate levels of safety stock the series considered in this paper are characterised by high volatility and skewness which are both time varying these features motivate the consideration of forecasting methods that are robust with regard to distributional assumptions the widespread use of exponential smoothing for point forecasting in inventory control motivates the development of the approach for interval forecasting in this paper we construct interval forecasts from quantile predictions generated using exponentially weighted quantile regression the approach amounts to exponential smoothing of the cumulative distribution function and can be viewed as an extension of generalised exponential smoothing to quantile forecasting empirical results are encouraging with improvements over traditional methods being particularly apparent when the approach is used as the basis for robust point forecasting

a garch option pricing model with α stable innovations we develop an option pricing model which is based on a garch asset return process with stable innovations with truncated tails the approach utilizes a canonic martingale measure as pricing measure which provides the possibility of a model calibration to market prices the garch stable option pricing model allows the explanation of some well known anomalies in empirical data as volatility clustering and heavy tailedness of the return distribution finally the results of monte carlo simulations concerning the option price and the implied volatility with respect to different strike and maturity levels are presented

the case of negative day ahead electricity prices in recent years germany has significantly increased its share of electricity produced from renewable sources which is mainly due to the renewable energy act eeg the eeg substantially impacts the dynamics of intra day electricity prices by increasing the likelihood of negative prices in this paper we present a non gaussian process to model german intra day electricity prices and propose an estimation procedure for this model most importantly our model is able to generate extreme positive and negative spikes a simulation study demonstrates the ability of our model to capture the characteristics of the data

a note on using the hodrick prescott filter in electricity markets abstract recently nowotarski et al 2013 have found that wavelet based models for the long term seasonal component ltsc are not only better in extracting the ltsc from a series of spot electricity prices but also significantly more accurate in terms of forecasting these prices up to a year ahead than the commonly used monthly dummies and sine based models however a clear disadvantage of the wavelet based approach is the increased complexity of the technique as compared to the other two classes of ltsc models and the resulting need for dedicated numerical software which may not be readily available to practitioners in their work environments to facilitate this problem we propose here a much simpler yet equally powerful method for identifying the ltsc in electricity spot price series it makes use of the hodrick prescott hp filter a widely recognized tool in macroeconomics

component estimation for electricity prices procedures and comparisons abstract electricity price time series usually exhibit some form of nonstationarity corresponding to long term behavior one or more periodic components as well as dependence on calendar effects as a result modeling electricity prices requires accounting for both long term and periodic components in the literature several filtering procedures have been proposed but a standard has not yet been found furthermore since different procedures are applied in contexts that are not homogeneous with respect to data periods and final goals a fair comparison is difficult this work considers several methods for component estimation in a homogeneous framework and compares them according to specific criteria the final purpose is to find an estimation procedure that performs well independently of the intended market and that can be proposed as a reference for electricity price time series filtering

time series analysis for financial market meltdowns abstract there appears to be a consensus that the recent instability in global financial markets may be attributable in part to the failure of financial modeling more specifically it is alleged that current risk models have failed to properly assess the risks associated with large adverse stock price behavior in this paper we first discuss the limitations of classical time series models for forecasting financial market meltdowns then we set forth a framework capable of forecasting both extreme events and highly volatile markets based on the empirical evidence presented in this paper our framework offers an improvement over prevailing models for evaluating stock market risk exposure during distressed market periods

why do electricity prices jump empirical evidence from the nordic electricity market the paper empirically explores the possible causes behind electricity price jumps in the nordic electricity market nord pool a time series model a mixed garch earji jump model capturing the com

why higher punishment may reduce deterrence abstract assuming that the willingness to find a potentially innocent defendant guilty is decreasing in punishments we derive two results deterrence is decreasing when punishments are too high and deterrence maximizing punishments tend to increase in the quality of legal systems

the theory of optimal law enforcement the economic literature on optimal law enforcement is very recent becker s seminal paper on crime and law enforcement dates from 1968 and most papers which are the focus of this survey have been published in the last ten years the core result presented by becker is the following the probability and the severity of punishment deter crime therefore the fine should be maximal since it is a costless transfer whereas the probability of detection and conviction is costly much of the recent work has attempted to show why becker s result may not hold

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

the fine structure of asset returns an empirical investigation we investigate the importance of diffusion and jumps in a new model for asset returns in contrast to standard models we allow for jump components displaying finite or infinite activity and variation empirical investigations of time series indicate that index dynamics are devoid of a diffusion component which may be present in the dynamics of individual stocks this leads to the conjecture confirmed on options data that the risk neutral process should be free of a diffusion component we conclude that the statistical and risk neutral processes for equity prices are pure jump processes of infinite activity and finite variation

temperature and seasonality influences on spanish electricity load abstract deregulation of the spanish electricity market in 1998 and the possible listing of electricity or weather derivative contracts have encouraged the study of the relationship between electricity demand and weather in spain in this paper a transfer function intervention model is developed for forecasting daily electricity load from cooling and heating degree days the influence of weather and seasonality is proved and is significant even when the autoregressive effects and the dynamic specification of the temperature are taken into account the estimated general model shows a high predictive power the results and information presented in this paper could be of interest for current users and potential traders in the deregulated spanish electricity market

indications for a changing electricity demand pattern the temperature dependence of electricity demand in the netherlands abstract this study assesses the electricity demand pattern in the relatively temperate climate of the netherlands latitude 52 30 n daily electricity demand and average temperature during the period from 1970 until 2007 are investigated for possible trends in the temperature dependence of electricity demand we hypothesize that the increased use of cooling applications has shifted the temperature dependence of electricity demand upwards in summer months our results show significant increases in temperature dependence of electricity demand in may june september october and during the summer holidays during the period studied temperature dependence in these months has shifted from negative to positive meaning that a higher temperature now leads to an increased electricity demand in these months rather than a decreased demand as observed historically although electricity demand in countries with moderate summer temperatures such as the netherlands generally peaks in winter months and shows a minimum in summer months this trend may signal the development of an additional peak in summer especially given the expected climatic change as power generating capacity may be negatively influenced by higher temperatures due to decreasing process cooling possibilities an increasing electricity demand at higher temperatures may have important consequences for power generation capacity planning and maintenance scheduling

when are path dependent payoffs suboptimal abstract generalizing a result by cox and leland 2000 vanduffel et al 2009 this note shows that risk averse investors with fixed planning horizon prefer path independent payoffs in any financial market if the pricing kernel is a function of the underlying s price at the end of the planning horizon generally for every payoff which is not a function of the pricing kernel there is a more attractive alternative that depends solely on the pricing kernel at the end of the planning horizon

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

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 substantial data revisions in the presence of model uncertainty a recent revision to the preliminary measurement of gdp e growth for 2003q2 caused considerable press attention provoked a public enquiry and prompted a number of reforms to uk statistical reporting procedures in this article we compute the probability of substantial revisions that are greater in absolute value than the controversial 2003 revision the predictive densities are derived from bayesian model averaging over a wide set of forecasting models including linear structural break and regime switching models with and without heteroscedasticity ignoring the nonlinearities and model uncertainty yields misleading predictives and obscures recent improvements in the quality of preliminary uk macroeconomic measurements

on the importance of the long term seasonal component in day ahead electricity price forecasting abstract in day ahead electricity price forecasting epf the daily and weekly seasonalities are always taken into account but the long term seasonal component ltsc is believed to add unnecessary complexity to the already parameter rich models and is generally ignored conducting an extensive empirical study involving state of the art time series models we show that i decomposing a series of electricity prices into a ltsc and a stochastic component ii modeling them independently and iii combining their forecasts can bring contrary to a common belief an accuracy gain compared to an approach in which a given time series model is calibrated to the prices themselves

cvar sensitivity with respect to tail thickness abstract the sensitivity of a risk measure with respect to the parameters of the hypothesized distribution is a useful tool in investigating the impact of marginal rebalancing decisions on the portfolio return distribution and also in the analysis of the asymptotic variability of the risk estimator we calculate the relative importance of the conditional value at risk cvar sensitivity with respect to tail thickness and scale of the portfolio return distribution in the case of regularly varying tails and in the case of exponential and faster than exponential decay we discuss the implications for asset return modeling and the asymptotic variability of the risk estimator

beyond sharpe ratio optimal asset allocation using different performance ratios abstract as the assumption of normality in return distributions is relaxed classic sharpe ratio and its descendants become questionable tools for constructing optimal portfolios in order to overcome the problem asymmetrical parameter dependent performance ratios have been recently proposed in the literature the aim of this note is to develop an integrated decision aid system for asset allocation based on a toolkit of eleven performance ratios a multi period portfolio optimization up covering a fixed horizon is set up at first bootstrapping of asset return distributions is assessed to recover all ratios calculations at second optimal rebalanced weights are achieved at third optimal final wealth is simulated for each ratios eventually we make a robustness test on the best performance ratios empirical simulations confirm the weakness in forecasting of sharpe ratio whereas asymmetrical parameter dependent ratios such as the generalized rachev sortino satchell and farinelli tibiletti ratios show satisfactorily robustness

a long term short term model for daily electricity prices with dynamic volatility abstract in this paper we introduce a new stochastic long term short term model for short term electricity prices and apply it to four major european indices namely to the german dutch uk and nordic one we give evidence that all time series contain certain periodic mostly annual patterns and show how to use the wavelet transform a tool of multiresolution analysis for filtering purpose the wavelet transform is also applied to separate the long term trend from the short term oscillation in the seasonal adjusted log prices in all time series we find evidence for dynamic volatility which we incorporate by using a bivariate garch model with constant correlation eventually we fit various models from the existing literature to the data and come to the conclusion that our approach performs best for the error distribution the normal inverse gaussian distribution shows the best fit

dynamic version of the economic lot size model this article originally appeared in management science october 1958 volume 5 number 1 pp 89 96 published by the institute of management sciences a forward algorithm for a solution to the following dynamic version of the economic lot size model is given allowing the possibility of demands for a single item inventory holding charges and setup costs to vary over n periods we desire a minimum total cost inventory management scheme which satisfies known demand in every period disjoint planning horizons are shown to be possible which eliminate the necessity of having data for the full n periods

guilt in games a clear conscience is a good pillow why does this old proverb contain an insight the emotion of guilt holds a key psychologists report that the prototypical cause of guilt would be the infliction of harm loss or distress on a relationship partner roy baumeister arlene m stillwell and todd f heatherton 1994 245 june price tangney 1995 moreover guilt is unpleasant and may affect behavior to render the associated pangs counterfactual baumeister stillwell and heatherton state if people feel guilt for hurting their partners and for failing to live up to their expectations they will alter their behavior to avoid guilt in ways that seem likely to maintain and strengthen the relationship avoided guilt is the down of the sound sleeper s bolster how can guilt be modeled how are human interaction and economic outcomes influenced we offer a formal approach for providing answers start with an extensive game form which associates a monetary outcome with each end node say that player i lets player j down if as a result of i s choice of strategy j gets a lower monetary payoff than j expected to get before play started player i s guilt may depend on how much he lets j down player i s guilt may also depend on how much j believes i believes he lets j down we develop techniques to analyze equilibria when players are motivated in part by a desire to avoid guilt the intellectual home for our exercise is what has been called psychological game theory this framework originally developed by john geanakoplos david pearce and ennio stacchetti 1989 and recently extended by battigalli and dufwenberg 2005 henceforth b d allows players utilities to depend on beliefs about choices states of nature

computing core allocations in cooperative games with an application to cooperative procurement abstract cooperative game theory defines several concepts for distributing outcome shares in a cooperative game with transferable utilities one of the most famous solution concepts is the core which defines a set of outcome allocations that are stable such that no coalition has an incentive to leave the grand coalition in this paper we propose a general procedure to compute a core element or to detect that no core allocation exists which is based on mathematical programming techniques the procedure proposed in this paper can be applied to a wide class of cooperative games where the characteristic function is given by the optimum objective function value of a complex optimization problem for cooperative procurement which is an example from the field of supply chain management where some literature on the core concept already exists we prove the applicability and provide computational results to demonstrate that games with 150 players can be handled

density forecasting for long term peak electricity demand long term electricity demand forecasting plays an important role in planning for future generation facilities and transmission augmentation in a long term context planners must adopt a probabilistic view of potential peak demand levels therefore density forecasts providing estimates of the full probability distributions of the possible future values of the demand are more helpful than point forecasts and are necessary for utilities to evaluate and hedge the financial risk accrued by demand variability and forecasting uncertainty this paper proposes a new methodology to forecast the density of long term peak electricity demand peak electricity demand in a given season is subject to a range of uncertainties including underlying population growth changing technology economic conditions prevailing weather conditions and the timing of those conditions as well as the general randomness inherent in individual usage it is also subject to some known calendar effects due to the time of day day of week time of year and public holidays a comprehensive forecasting solution is described in this paper first semi parametric additive models are used to estimate the relationships between demand and the driver variables including temperatures calendar effects and some demographic and economic variables then the demand distributions are forecasted by using a mixture of temperature simulation assumed future economic scenarios and residual bootstrapping the temperature simulation is implemented through a new seasonal bootstrapping method with variable blocks the proposed methodology has been used to forecast the probability distribution of annual and weekly peak electricity demand for south australia since 2007 the performance of the methodology is evaluated by comparing the forecast results with the actual demand of the summer 2007 2008

prediction of daily peak electricity demand in south africa using volatility forecasting models abstract daily peak electricity demand forecasting in south africa using a seasonal autoregressive integrated moving average sarima model a sarima model with generalized autoregressive conditional heteroskedastic sarima garch errors and a regression sarima garch reg sarima garch model is presented in this paper the garch modeling methodology is introduced to accommodate the possibility of serial correlation in volatility since the daily peak demand data exhibits non constant mean and variance and multiple seasonality corresponding to weekly and monthly periodicity the proposed reg sarima garch model is designed in such a way that the predictor variables are initially selected using a multivariate adaptive regression splines algorithm the developed models are used for out of sample prediction of daily peak demand a comparative analysis is done with a piecewise linear regression model results from the study show that the reg sarima garch model produces better forecast accuracy with a mean absolute percent error mape of 1 42

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