a semiparametric approach to short term oil price forecasting abstract in this paper it is shown how the garch properties of oil price changes can be employed to forecast the oil price distribution over short term horizons the forecasting methodology is semiparametric and it is based on the bootstrap approach the results of an out of sample forecasting exercise carried out using the brent oil price series suggest that the forecasting approach can be used to obtain a performance measure for the forward price in addition to compute interval forecasts for the oil price

order flow transaction clock and normality of asset returns the goal of this paper is to show that normality of asset returns can be recovered through a stochastic time change clark 1973 addressed this issue by representing the price process as a subordinated process with volume as the lognormally distributed subordinator we extend clark s results and find the following i stochastic time changes are mathematically much less constraining than subordinators ii the cumulative number of trades is a better stochastic clock than the volume for generating virtually perfect normality in returns iii this clock can be modeled nonparametrically allowing both the time change and price processes to take the form of jump diffusions the relations among trading volume stock prices and price volatility the subject of empirical and theoretical studies over many years have lately received renewed attention with the increased availability of high frequency data a vast amount of research has focused on issues such as news arrivals volume and price changes or volatility moves usually outside any framework of general or even partial equilibrium is the normality of returns a key issue for example in the mean variance paradigm for portfolio choice or the recent study of the problems of risk management e g in value at risk verified at any time horizon the evidence accumulated from a number of studies that document the presence of leptokurtosis and skewness in the distribution of returns of a wide variety of financial assets suggests that the answer is no studies as early as for example fama 1965 showed that daily returns are more long tailed than the normal density with the distribution of returns approaching normality as the holding period is extended to one month in the same manner volatility smiles and other observed

model complexity and out of sample performance evidence from s p 500 index returns abstract we apply a range of out of sample specification tests to more than forty competing stochastic volatility models to address how model complexity affects out of sample performance using daily s p 500 index returns model confidence set estimations provide strong evidence that the most important model feature is the non affinity of the variance process despite testing alternative specifications during the turbulent market regime of the global financial crisis of 2008 we find no evidence that either finite or infinite activity jump models or other previously proposed model extensions improve the out of sample performance further applications to value at risk demonstrate the economic significance of our results furthermore the out of sample results suggest that standard jump diffusion models are misspecified

risk neutral parameter shifts and derivatives pricing in discrete time we obtain a large class of discrete time risk neutral valuation relationships or preference free derivatives pricing models by imposing a simple restriction on the state price density process the risk neutral stock return and forward rate dynamics are obtained by changing only a location parameter which can be determined independent of the preference and true location parameters the gaussian models of rubinstein 1976 brennan 1979 and c mera 2003 and the gamma model of heston 1993 are all special cases the model provides simple relationships between expected returns and state price density parameters analogous to the diffusion case

option pricing for a jump diffusion model with general discrete jump size distributions we obtain a closed form solution for pricing european options under a general jump diffusion model that can incorporate arbitrary discrete jump size distributions including nonparametric distributions such as an empirical distribution the flexibility in the jump size distribution allows the model to better capture leptokurtic features found in real world data the model uses a discrete time framework and leads to a pricing formula that is provably convergent to the continuous time price as the discretization is increased the solution is easy to implement with fast convergence properties numerical results illustrate the efficiency and accuracy of the proposed model and highlight its robustness and flexibility this paper was accepted by noah gans stochastic models and simulation

the variance gamma v g model for share market returns a new stochastic process termed the variance gamma process is proposed as a model for the uncertainty underlying security prices the unit period distribution is normal conditional on a variance that is distributed as a gamma variate its advantages include long tailedness continuous time specification finite moments of all orders elliptical multivariate unit period distributions and good empirical fit the process is pure jump approximable by a compound poisson process with high jump frequency and low jump magnitudes applications to option pricing show differential effects for options on the money compared to in or out of the money copyright 1990 by the university of chicago

option valuation with systematic stochastic volatility we use an extension of the equilibrium framework of rubinstein 1976 and brennan 1979 to derive an option valuation formula when the stock return volatility is both stochastic and systematic our formula incorporates a stochastic volatility process as well as a stochastic interest rate process in the valuation of options if the mean volatility and covariance processes for the stock return and the consumption growth are predictable our option valuation formula can be written in preference free form further many popular option valuation formulae in the literature can be written as special cases of our general formula in this paper we investigate the valuation of individual stock options when the volatility of the underlying stock return is stochastic and has a systematic component which is related to the stochastic volatility of the consumption growth or the market return such an investigation is timely and of significant interest because there is now a considerable amount of evidence that the volatility of stock prices is not only stochastic but that it is also highly correlated with the volatility of the market as a whole for example see black 1975 conrad kaul and gultekin 1991 jarrow and rosenfeld 1984 jorion 1988 and ng engle and rothschild 1992 furthermore empirical work by black and scholes 1972 gultekin rogalski and tinic 1982 and whaley 1982 has shown that the empirical biases inherent in the black scholes option prices are different for options on high and low risk stocks because low risk stocks are mainly large firm stocks with stochastic return volatilities that are highly correlated with the return volatility of the market and high risk stocks are mainly small firm stocks with a lessimportant systematic volatility component incorporating systematic volatility effects in option valuation models has the potential to reduce the empirical biases exhibited by prices computed from the black scholes formula we use equilibrium arguments that extend the consumption based representative agent framework of rubinstein 1976 brennan 1979 and stapleton and subrahmanyam 1984 to allow individual asset returns and the consumption growth to exhibit stochastic volatility in particular we permit the volatility of individual asset returns to consist of a systematic component that is related to the consumption or market volatility in addi

genco s risk constrained hydrothermal scheduling this paper presents a stochastic midterm risk constrained hydrothermal scheduling algorithm in a generation company genco the objective of a genco is to maximize payoffs and minimize financial risks when scheduling its midterm generation of thermal cascaded hydro and pumped storage units the proposed schedule will be used by the genco for bidding purposes to the iso the optimization model is based on stochastic price based unit commitment the proposed genco solution may be used to schedule midterm fuel and natural water inflow resources for a few months to a year the proposed stochastic mixed integer programming solution considers random market prices for energy and ancillary services as well as the availability of natural water inflows and generators in monte carlo scenarios financial risks associated with uncertainties are considered by applying expected downside risks which are incorporated explicitly as constraints variable time steps are adopted to avoid the exponential growth in solution time and memory requirements when considering midterm constraints a single water to power conversion function is used instead of several curves for representing water head and discharge parameters piecewise linearized head dependant water to power conversion functions are used for computational efficiency illustrative examples examine gencos midterm generation schedules risk levels fuel and water usage and hourly generation dispatches for bidding in energy and ancillary services markets the paper shows that gencos could decrease their financial risks by adjusting expected payoffs

the multinomial option pricing model and its brownian and poisson limits the cox ross and rubinstein binomial model is generalized to the multinomial case limits are investigated and shown to yield the blacks scholes formula in the case of continuous sample paths for formula in the case of complete market structures in the discontinuous case a merton type formula is shown to result provided jump probabilities are replaced by their corresponding arrow debreu prices article published by oxford university press on behalf of the society for financial studies in its journal the review of financial studies

variance stabilizing transformations for electricity spot price forecasting most electricity spot price series exhibit price spikes these extreme observations may significantly impact the obtained model estimates and hence reduce efficiency of the employed predictive algorithms for markets with only positive prices the logarithmic transform is the single most commonly used technique to reduce spike severity and consequently stabilize the variance however for datasets with very close to zero like the spanish or negative like the german prices the log transform is not feasible what reasonable choices do we have then to address this issue we evaluate 16 variance stabilizing transformations within a comprehensive forecasting study involving two model classes regression models neural networks and 12 datasets from diverse power markets we show that the probability integral transform combined with the standard gaussian distribution yields the best approach significantly better than many of the considered alternatives

general equilibrium with endogenously incomplete financial markets abstract the present paper studies a class of general equilibrium economies with imperfectly competitive financial intermediaries and price taking consumers intermediaries optimally choose the securities they issue and the bid ask spread they charge financial intermediation is costly and hence markets are endogenously incomplete an appropriate equilibrium concept is developed and existence is proved competitive equilibria for this class of economies display full indexation of securities payoffs and monetary neutrality even if intermediaries are restricted to issue nominal securities and financial markets turn out to be incomplete this is in sharp contrast with the indeterminacy and non neutrality results established in the literature for incomplete markets economies with exogenously given nominal securities journal of economic literaturclassification numbers d52 g20

does sunspot monetary policy matter abstract in this note we examine the effectiveness of pure random monetary policies i e changes in the money supply with no direct redistributive effects conditioned only on some extrinsic source of uncertainty in the framework of a general infinite horizon economy with uncertainty we show that these policies affect the agents insurance opportunities and hence have in general real effects when markets are incomplete and some nominal assets are traded the result is shown to extend though in a slightly weaker form to the case in which monetary policy is conditioned only on the intrinsic uncertainty in the economy

forecasting electricity prices and their volatilities using unobserved components abstract the liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities both in the short and long term thus there is a need of providing methodology that is able to deal with the most important features of electricity price series which are well known for presenting not only structure in conditional mean but also time varying conditional variances in this work we propose a new model which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices these common factors are jointly estimated as well as their relationship with the original vector of series and the dynamics affecting both their conditional mean and variance the estimation of the model is carried out under the state space formulation the new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the spanish zone of the iberian market several simplified alternative models are also considered as benchmarks to illustrate that the proposed approach is superior to all of them in terms of explanatory and predictive power

forward contracting and selling price determination for a retailer to procure the electric energy to be sold to its clients a retailer has to face two major difficulties while buying electric energy it must cope with uncertain pool prices or sign forward contracts at higher average prices while selling electricity it should handle the uncertainty of the end user demand and the fact that customers might choose a different retailer if the selling price is not competitive enough we propose a risk constrained stochastic programming framework to decide which forward contracts the retailer should sign and at which price it must sell electricity so that its expected profit is maximized at a given risk level the developed model takes into account the elastic behavior of end users with respect to the selling price offered by the retailer a realistic case study illustrates the methodology proposed

leverage effect volatility feedback and self exciting market disruptions equity index volatility variation and its interaction with the index return can come from three distinct channels first index volatility increases with the market s aggregate financial leverage second positive shocks to systematic risk increase the cost of capital and reduce the valuation of future cash flows generating a negative correlation between the index return and its volatility regardless of financial leverage finally large negative market disruptions show self exciting behaviors this article proposes a model that incorporates all three channels and examines their relative contribution to index option pricing and stock option pricing for different types of companies

jump diffusion option valuation in discrete time we develop a simple discrete time model to value options when the underlying process follows a jump diffusion process multivariate jumps are superimposed on the binomial model of cox ross and rubinstein 1979 to obtain a model with a limiting jump diffusion process this model incorporates the early exercise feature of american options as well as arbitrary jump distributions it yields an efficient computational procedure that can be implemented in practice as an application of the model we illustrate some characteristics of the early exercise boundary of american options with certain types of jump distributions jump diffusion processes are popular in option valuation models these processes were incorporated by merton 1976 into the theory of option valuation and constitute an important alternative to the lognormal diffusion process assumed in the black and scholes 1973 model in this paper we develop a tractable discrete time model for valuing options with these processes many authors have suggested that incorporating jumps in option valuation models may explain some of the large empirical biases exhibited by the black scholes model 1 however in many cases closed form solutions for valuing options are not available for example this is true when there is a positive probability of early exercise when the jump distribution is neither lognormal nor discrete or when the option is based on the term structure of interest rates see ahn and thompson 1988 and jarrow and madan 1991 in these important cases the model developed in this paper can be used to value options another aim of this paper is to provide a model which relies on only simple mathematics to price options with jump diffusion processes it is hoped that the key economic relationships underlying jump diffusion models are transparent in this model we provide enough details to make the procedure readily accessible

implications of quantal response statistical equilibrium abstract this paper explores the foundations and properties of the quantal response statistical equilibrium qrse model developed by scharfenaker and foley 2017 the qrse model provides a behavioral foundation for the formation of aggregate economic outcomes in social systems characterized by negative feedbacks it can approximate a wide range of commonly encountered theoretical distributions that have been identified as economic statistical equilibrium and displays qualitatively similar behavior to the subbotin and asymmetric subbotin distributions that range from the laplace to the normal distribution in the limit asymmetry in the frequency distributions of economic outcomes can be understood as arising from the unfulfilled expectations of entropy constrained decision makers this paper demonstrates the logic of the qrse model in an application to us stock market data dating back to 1926 the model provides a parsimonious explanation for the distribution of rates of return on private equities as well as a behavioral foundation for asset price fluctuations

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

time changed levy processes and option pricing abstract the classic black scholes option pricing model assumes that returns follow brownian motion but return processes differ from this benchmark in at least three important ways first asset prices jump leading to non normal return innovations second return volatilities vary stochastically over time third returns and their volatilities are correlated often negatively for equities time changed levy processes can simultaneously address these three issues we show that our framework encompasses almost all of the models proposed in the option pricing literature and it is straightforward to select and test a particular option pricing model through the use of characteristic function technology

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

which way the natural gas price an attempt to predict the direction of natural gas spot price movements using trader positions abstract this research provides a method of predicting direction of spot price movements in the natural gas market for the month succeeding from market participants positions in the futures market cumby and modest cumby r e modest d m 1987 testing for market timing ability a framework for forecast evaluation journal of financial economics 19 169 189 provide the backdrop for analyzing the futures market positions of large hedgers and speculators to arrive at conclusions of market price movements in the spot market this methodology is suggested as a means for municipalities entering the natural gas market to improve upon their ordering of quantities of gas for the ensuing months in order to take advantage of possibly foreseeable price trends

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

extracting market information from equity options with exponential levy processes levy processes have been successfully applied in the modeling of financial assets useful information such as implied volatility skewness and risk preferences can be derived from market option prices in this paper we advocate using esscher conjugate levy processes to estimate risk neutral and empirical densities more specifically we employ the exponential meixner and nig processes to calculate in closed form the pricing kernel in the equity market and then study the evolution of equity market behavior between 2002 and 2010 our empirical analysis using s p 500 options shows that the risk preferences of equity investors were signalling an anomaly in the market well before the subprime prime mortgage crisis august 2007 and the crisis of confidence that followed anticipating the downfall in equity markets in 2008 but then returning to normal levels in 2009

u s stock market crash risk 1926 2010 abstract this paper examines how well alternate time changed levy processes capture stochastic volatility and the substantial outliers observed in u s stock market returns over the past 85 years the autocorrelation of daily stock market returns varies substantially over time necessitating an additional state variable when analyzing historical data i estimate various one and two factor stochastic volatility levy models with time varying autocorrelation via extensions of the bates 2006 methodology that provide filtered daily estimates of volatility and autocorrelation the paper explores option pricing implications including for the volatility index vix during the recent financial crisis

implied migration rates from credit barrier models abstract the risk neutral credit migration process captures quantitative information which is relevant to the pricing theory and risk management of credit derivatives in this article we derive implied migration rates by means of a recently introduced credit barrier model which is calibrated on the basis of aggregate information such as credit migration rates and credit spread curves the model is characterized by an underlying stochastic process that represents credit quality and default events are associated to barrier crossings the stochastic process has state dependent volatility and jumps which are estimated by using empirical migration and default rates a risk neutralizing drift and forward liquidity spreads are estimated to consistently match the average spread curves corresponding to all the various ratings the implied migration rates obtained with our credit barrier model are then compared with those obtained via the kijima komoribayashi model

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

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

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

the relevance of financial policy when the asset market is incomplete equilibrium allocations are not invariant to changes in the financial policies of firms in the presence of secondary assets such as options whose payoffs depend nonlinearly on the price of equity the range of attainable reallocations of revenue varies as a firm alters its position in the asset market corporate financial policy is thus relevant when assets are nominal monetary policy implemented through open market operations is also effective

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

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

numerical approximations of optimal portfolios in mispriced asymmetric levy markets abstract we present numerical approximations of optimal portfolios in mispriced levy markets under asymmetric information for informed and uninformed investors having logarithmic preference we apply our numerical scheme to kou 2002 jump diffusion markets by deriving analytic formulas for the first two derivatives of the underlying portfolio objective function which depend only on the levy measure of the jump generating process optimal portfolios are then simulated using the box muller algorithm newton s method and incomplete beta functions convergence dynamics and trajectories of sample paths of optimal portfolios for both investors are presented at different levels of information asymmetry mispricing horizon asymmetry in the kou density jump intensity volatility mean reversion speed and sharpe ratios we also apply the proposed newton s algorithm to compute optimal portfolios for investors in variance gamma markets via instantaneous centralized moments of returns

recent advances in electricity price forecasting a review of probabilistic forecasting abstract since the inception of competitive power markets two decades ago electricity price forecasting epf has gradually become a fundamental process for energy companies decision making mechanisms over the years the bulk of research has concerned point predictions however the recent introduction of smart grids and renewable integration requirements has had the effect of increasing the uncertainty of future supply demand and prices academics and practitioners alike have come to understand that probabilistic electricity price and load forecasting is now more important for energy systems planning and operations than ever before with this paper we offer a tutorial review of probabilistic epf and present much needed guidelines for the rigorous use of methods measures and tests in line with the paradigm of maximizing sharpness subject to reliability the paper can be treated as an update and a further extension of the otherwise comprehensive epf review of weron 1 or as a standalone treatment of a fascinating and underdeveloped topic that has a much broader reach than epf itself

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

residential appliances direct load control in real time using cooperative game the relatively fixed residential day ahead real time electricity price reflects insufficient information of the market so the response of residential energy management system ems to the real time pricing rtp is not complete and therefore retailers are exposed to the risk of price fluctuation in balance market direct load control dlc in cooperative game is proposed in this paper a cooperative game union comprised of some users and a retailer is established to minimize the union costs the union provides an opportunity to access the balance market indirectly for residents and to reduce risks and costs for the retailer in addition shapley value which embodies the fairness is used in union profits allocation the method that avoids bidding for residential users simplifies the thresholds of residents to participate in market furthermore the dlc union contributes to imbalance self management for the retailer which helps him avoid paying for the regulation cost involved in the deviation between the total quantity bought at markets and the actual consumption the achievement of union s goal respects the constraints set by users the method that alleviates the disturbance of dlc to residents meets the dual goals of being both fully responsive and non disruptive

deducing the implications of jump models for the structure of stock market crashes rallies jump arrival rates and extremes this article studies the structure of stock market crashes rallies their jump arrival rates and extremes large market moves are characterized in a pure jump modeling framework based on both raw and devolatized returns it is shown empirically that crashes are more severe in intensity than rallies and have higher arrival rates at the same time both left tail and right tail extreme events conform with frechet limit laws pure jump models which describe well the tail properties of market returns are identified via their levy measures the distribution of extreme events implied by our model s levy measure is closer to the actual realization of extremes than those of competing models finally there is information content in the levy measure of pure jump models for forward arrival rate of jumps

a jump diffusion model for option pricing brownian motion and normal distribution have been widely used in the black scholes option pricing framework to model the return of assets however two puzzles emerge from many empirical investigations the leptokurtic feature that the return distribution of assets may have a higher peak and two asymmetric heavier tails than those of the normal distribution and an empirical phenomenon called volatility smile in option markets to incorporate both of them and to strike a balance between reality and tractability this paper proposes for the purpose of option pricing a double exponential jump diffusion model in particular the model is simple enough to produce analytical solutions for a variety of option pricing problems including call and put options interest rate derivatives and path dependent options equilibrium analysis and a psychological interpretation of the model are also presented

estimating quadratic variation using realized variance this paper looks at some recent work on estimating quadratic variation using realised variance rv that is sums of m squared returns this econometrics has been motivated by the advent of the common availability of high frequency financial return data when the underlying process is a semimartingale we recall the fundamental result that rv is a consistent as m estimator of quadratic variation qv we express concern that without additional assumptions it seems difficult to give any measure of uncertainty of the rv in this context the position dramatically changes when we work with a rather general sv model which is a special case of the semimartingale model then qv is integrated variance and we can derive the asymptotic distribution of the rv and its rate of convergence these results do not require us to specify a model for either the drift or volatility functions although we have to impose some weak regularity assumptions we illustrate the use of the limit theory on some exchange rate data and some stock data we show that even with large values of m the rv is sometimes a quite noisy estimator of integrated variance

firm growth and laplace distribution the importance of large jumps abstract recent empirical studies have shown that firm growth rate distribution is not gaussian but closely follows a laplace distribution this robust feature of the growth rate distribution challenges existing models based on gibrat s model because it predicts a gaussian distribution first we analyze more than 100 000 japanese firms and empirically show that the laplace shape can be observed for the japanese firms then by using the theory of stochastic processes we theoretically show that the absence of jumps causes the discrepancy between gibrat s model and the laplace shape in particular based on the laplace shape and the law of proportionate effect we show that the firm growth process is a jump process in other words firm growth cannot be explained by the consequence of many small shocks but is determined by a few large jumps the widely observed laplace distribution reflects this jump property of firm growth dynamics

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

flexible transmission expansion planning with uncertainties in an electricity market deregulation of the electric power industry has introduced new uncertainties for market participants and made planning of transmission expansion more difficult more flexible transmission expansion plans are needed to cope with the increased risks in this paper a novel planning approach is proposed to meet the above challenge in our approach the planning process is modeled as a mixed integer nonlinear programming minlp problem so that conflicting objectives can be optimized simultaneously to minimize planning risks our method identifies several scenarios based on statistics and expert knowledge the most flexible expansion plan is selected as the plan that has least adaptation cost the proposed method is tested with the ieee 14 bus system promising results are obtained to demonstrate the effectiveness of our method

dampened power law reconciling the tail behavior of financial security returns this article proposes a stylized model that reconciles several seemingly conflicting findings on financial security returns and option prices the model is based on a pure jump levy process wherein the jump arrival rate obeys a power law dampened by an exponential function the model allows for different degrees of dampening for positive and negative jumps and also for different pricing for upside and downside market risks calibration of the model to the s p 500 index shows that the market charges only a moderate premium on upward index movements but the maximally allowable premium on downward index movements

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

optimal hedge ratio estimation and hedge effectiveness with multivariate skew distributions this article proposes to use the three multivariate skew distributions generalized hyperbolic distribution multivariate skew normal distribution and multivariate skew student t distribution for estimating the minimum variance hedge ratio in a dynamic setting three criteria for measuring hedge effectiveness are employed hedging instrument effectiveness overall hedge effectiveness and relative to optimal hedge ratio effectiveness rhre three portfolios of spot and futures series are formed for empirical analysis the outcomes confirm that the three multivariate skew distributions are more helpful in deciding the minimum variance hedge ratio especially the generalized hyperbolic distribution than the symmetrical normal and student t distributions this outperformance is significant especially at critical market moments and it is indicated by three hedge effectiveness measures this advantage is held without the cost of lowering portfolio return in addition there is speculation possibility exist

on the non neutrality of money with incomplete markets abstract in this paper we show that if markets are incomplete and there are nominal assets whose payoff is denominated in money monetary policy will be in general non neutral the mechanism through which monetary policy operates is a change in the structure of the rates of return by changing the level of money prices monetary policy affects the payoffs of nominal assets this differs from previous arguments for non neutrality we also show that a consideration of this effect of monetary policy may allow us to claim the superiority from a welfare point of view of a random money growth rate over a deterministic one journal of economic literature classification numbers d52 e50 g10

an fft network for levy option pricing abstract this paper develops a simple network approach to american exotic option valuation under levy processes using the fast fourier transform fft the forward shooting grid fsg technique of the lattice approach is then generalized to expand the fft network to accommodate path dependent variables this network pricing approach is applicable to all levy processes for which the characteristic function is readily available in other words the log value of the underlying asset can follow finite activity or infinite activity levy processes with the powerful computation of fft the proposed network has a negligible additional computational burden compared to the binomial tree approach the early exercise policy and option values in the continuation region are determined in a way very similar to that of the lattice approach numerical examples using american style barrier lookback and asian options demonstrate that the fft network is accurate and efficient

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

pricing an option on revenue from an innovation an application to movie box office revenue we develop a model for valuing revenue streams from innovations the stochastic properties of revenue from innovations create a more difficult environment in which to value options than when the underlying is a security there is no initial revenue and cumulative revenue cannot decrease revenues from innovations are characterized by different lives and different rates of the resolution of uncertainty a common deterministic model for predicting revenue from an innovation is known as the bass model we embed the bass model in a gamma process resulting in a stochastic process with moments proportional to the mean of the bass model to illustrate this model we choose the valuation of options on movie box office revenue these options enable film distributors to manage the risk of a movie and they offer diversification opportunities for investors we develop the econometric methodology for ex ante parameter estimation and a bayesian updating scheme using markov chain monte carlo simulation as data after release become available call prices obtained using the maximum likelihood ml parameter estimates from the full data set closely approximate the average discounted value of ex post call payouts that would have occurred at option maturity

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

a general distribution for describing security price returns this paper introduces a generalized distribution called the gb2 distribution for describing security returns the distribution is extremely flexible containing a large number of well known distributions such as the lognormal log t and log cauchy distribu tions as special or limiting cases and allowing large even infinite higher moments this flexibility allows a direct representation of different degrees of fat tails in the distribution the properties of the gb2 make it useful in empirical estimation of security returns and in facilitating the development of option pricing models and other models that depend on the specification and mathematical manipulation of distributions copyright 1987 by the university of chicago

an empirical examination of the variance gamma model for foreign currency options we apply the variance gamma vg option pricing model to currency options the model is a pure infinite activity jump model we examine whether and to what extent this new model can improve the pricing quality for currency options over the existing modified black scholes model and the merton jump diffusion jd model we find that the vg model yields better out of sample pricing performance than the modified black scholes model or the jd model in addition a cross entropy analysis shows that the vg model is more consistent with the general criterion of utility maximization and optimal portfolio selection

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

impact of unit failure on forward contracting a generating unit can sell its production in the pool facing the financial risk inherent to volatile pool prices alternatively it can sell its production through the futures market at comparatively stable but smaller average prices unlike the pool forward contracts involve an obligation to sell during a specified time period and if the unit fails it should buy energy from the pool to meet its futures market commitments hence taking into account its forced outage rate the unit should determine the appropriate mix of pool and futures market involvement so that its expected profit is maximized for a prespecified risk level on profit variability within this context this paper analyzes the impact of the degree of unavailability of the generating unit on its forward contracting decisions a detailed case study illustrates the analysis performed

a jump model for fads in asset prices under asymmetric information abstract this paper addresses how asymmetric information fads and levy jumps in the price of an asset affect the optimal portfolio strategies and maximum expected utilities of two distinct classes of rational investors in a financial market we obtain the investors optimal portfolios and maximum expected logarithmic utilities and show that the optimal portfolio of each investor is more or less than its merton optimal our approximation results suggest that jumps reduce the excess asymptotic utility of the informed investor relative to that of uninformed investor and hence jump risk could be helpful for market efficiency as an indirect reducer of information asymmetry our study also suggests that investors should pay more attention to the overall variance of the asset pricing process when jumps exist in fads models moreover if there are very little or too much fads then the informed investor has no utility advantage in the long run

performance standards and optimal incentives abstract this paper analyzes incentive design when agents effort influences an uncertain output governed by a random process with semi heavy tails we find that the second best incentive contract pays an output increasing but bounded fee with a shape resembling performance standard contracts that pay a fixed salary plus a capped bonus in this contract the pay performance sensitivity around the standard increases decreases with the frequency with which performance is measured and with the kurtosis volatility parameter of the performance probability distribution we also find that the optimal maximum bonus increases with volatility but decreases with the kurtosis parameter of the performance distribution

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

direct data based decision making under uncertainty abstract in a typical one period decision making model under uncertainty unknown consequences are modeled as random variables however accurately estimating probability distributions of the involved random variables from historical data is rarely possible as a result decisions made may be suboptimal or even unacceptable in the future also an agent may not view data occurred at different time moments e g yesterday and one year ago as equally probable the agent may apply a so called time profile weights to historical data to address these issues an axiomatic framework for decision making based directly on historical time series is presented it is used for constructing data based analogues of mean variance and maxmin utility approaches to optimal portfolio selection

multimarket optimal bidding for a power producer this paper considers a profit maximizing thermal producer that participates in a sequence of spot markets namely day ahead automatic generation control agc and balancing markets the producer behaves as a price taker in both the day ahead market and the agc market but as a potential price maker in the volatile balancing market the paper provides a stochastic programming methodology to determine the optimal bidding strategies for the day ahead market uncertainty sources include prices for the day ahead and agc markets and balancing market linear price variations with the production of the thermal producer results from a realistic case study are reported and analyzed conclusions are duly drawn

optimal response of a thermal unit to an electricity spot market this paper addresses the optimal response of a thermal unit to an electricity spot market the objective is to maximize the unit profit from selling both energy and spinning reserve in the spot market the paper proposes a 0 1 mixed integer linear programming approach that allows a rigorous modeling of i nonconvex and nondifferentiable operating costs ii exponential start up costs iii available spinning reserve taking into account ramp rate restrictions and iv minimum up and down time constraints this approach overcomes the modeling limitations of dynamic programming approaches and is computationally efficient results from realistic case studies are reported