infonet-economy

We address the maximization of a project's expected net present value when the activity durations and cash flows are described by a discrete set of alternative scenarios with associated occurrence probabilities. In this setting, the choice of scenario-independent activity start times frequently leads to infeasible schedules or severe losses in revenues. We suggest to determine an optimal target processing time policy for the project activities instead. Such a policy prescribes an activity to be started as early as possible in the realized scenario, but never before its (scenario-independent) target processing time. We formulate the resulting model as a global optimization problem and present a branch-and-bound algorithm for its solution. Extensive numerical results illustrate the suitability of the proposed policy class and the runtime behavior of the algorithm.

We formulate the service composition problem as a multi-objective stochastic program which simultaneously optimizes the following quality of service (QoS) parameters: workflow duration, service invocation costs, availability, and reliability. All of these quality measures are modelled as decision-dependent random variables. Our model minimizes the average value-at-risk (AVaR) of the workflow duration and costs while imposing constraints on the workflow availability and reliability. AVaR is a popular risk measure in decision theory which quantifies the expected shortfall below some percentile of a loss distribution. By replacingthe random durations and costs with their expected values, our risk-aware model reduces to the nominal problem formulation prevalent in literature. We argue that this nominal model can lead to overly risky decisions. Finally, we report on the scalability properties of our model.

This paper introduces the 3SP Design Space Exploration System. 3SP automatically quantifies acceleration opportunities for programs across a wide range of heterogeneous architectures to allow designers to identify promising implementation platforms before investing in a particular hardware/ software codesign. 3SP uses a novel program execution model to integrate comprehensive hardware characteristics including clock speed, number of execution units, issue rates, bandwidths and latencies with software program execution, parallelism, control and data flow measurements to estimate codesign performance for evaluating opportunities for hardware acceleration.

We propose a novel regime-switching approach for modeling electricity spot prices that takes into account the relation between spot and forward prices. Additionally the model is able to reproduce spikes and negative prices. Market prices are based on an observed forward curve. We distinguish between a base regime and an upper as well as a lower spike regime. The model parameters are calibrated using historical hourly price forward curves for EEX Phelix and the dynamics of hourly spot prices. The model is compared with common time series approaches like ARMA and GARCH.

We present a medium-term planning model for hydropower production based on multistage stochastic programming (MSP). The model determines a production schedule for a planning horizon of one year where decisions are made on generation, pumping or spillover. While in reality a production schedule must be determined with (at least) hourly time resolution, the MSP model cannot decide in hourly steps due to the curse of dimensionality. To overcome this issue, we exploit a special aggregation technique based on occupation levels for prices.

The system under consideration consists of several interconnected seasonal reservoirs and produced electricity is sold on the spot market. Thus, we consider stochastic inflows and stochastic electricity prices which are modeled with a regime-switching approach to take also extreme price movements (spikes) into account. Scenario paths are generated with Monte Carlo simulation and then aggregated to scenario trees which serve as input for the stochastic optimization problem.

In an extension, we use the approach for the valuation of non-standard power contracts by means of indifference pricing. In a first step, an optimal production schedule is determined where the objective function consists of a mix of risk and return that can be weighted according to the decision maker's preferences. In a second step, another optimization model is solved that includes also one or more contracts for the delivery of electricity. The model minimizes the price the producer must demand for the delivery of electricity subject to the constraint that the resulting mix of risk and return with inclusion of the contracts is not worse than without (for the case that electricity is sold only at the spot market). The demand for the contracts may depend on price levels and can be stochastic, which requires also a corresponding model for the load. Results are presented for a multi-reservoir system located in the Alps.

We propose a novel regime-switching approach for the simulation of electricity spot prices that is inspired by the class of fundamental models and takes into account the relation between spot and forward prices. Additionally the model is able to reproduce spikes and negative prices. Market prices are derived given an observed forward curve. We distinguish between a base regime and an upper as well as a lower spike regime. The model parameters are calibrated using historical hourly price forward curves for EEX Phelix and the dynamic of hourly spot prices. We further evaluate different time series models such as ARMA and GARCH that are usually applied for modeling electricity prices and conclude a better performance of the proposed regime-switching model.

We analyze the impact of renewable energies, wind and photovoltaic, on the formation of day-ahead electricity prices at EEX. We give an overview of the policy decisions concerning the promotion of renewable energy sources in Germany, and discuss their consequences on day-ahead prices. An analysis of electricity spot prices reveals that the introduction of renewable energies enhances extreme price changes. In the frame of a dynamic fundamental model, we show that there has been a continuous electricity price adaption process to market fundamentals. Furthermore, the fundamental drivers of prices differ among hours with different load profiles. Our results imply that renewable energies decrease market spot prices and have implications on the traditional fuel mix for electricity production.

In this paper we develop models for the client rate and the volumes of non-maturing accounts. We test the hypothesis that movements in the client rate are dependent upon the market rates regime. We find that the responsiveness of the client rate is symmetric to changes in the short rate, but asymmetric to changes in the longer market rates. Furthermore, the speed of adjustment of the client rate is faster when there is substantial deviation from the equilibrium relationship linking client rate and market rates. We also show that volumes can be explained by the spread between the client rate and the market rates.

This paper develops and applies a novel estimation procedure for quantile regressions with time-varying coefficients based on a fully parametric, multifactor specification. The algorithm recursively filters the multifactor dynamic coefficients with a Kalman filter and parameters are estimated by maximum likelihood. The likelihood function is built on the Skewed-Laplace assumption. In order to eliminate the non-differentiability of the likelihood function, it is reformulated into a non-linear optimization problem with constraints. A relaxed problem is obtained by moving the constraints into the objective, which is then solved numerically with the Augmented Lagrangian Method. In the context of an application to electricity prices, the results show the importance of modelling the time-varying features and the explicit multi-factor representation of the latent coefficients is consistent with an intuitive understanding of the complex price formation processes involving fundamentals, policy instruments and participant conduct.

We demonstrated the value of a well specified dynamic model for quantile estimation by means of an application to electricity price risk. Electricity prices are a commodity in which price formation is nonlinear in its relationship to fundamentals, dynamic in the relative influences of drivers, with further complications introduced by policy interventions for supporting specific technologies and opportunities for participant conduct to be influential at high and low prices. Despite these complications careful consideration of the shape of the supply function with its concave, flat and convex regions, together with the information that is available to market participants day ahead allows plausible expectations for the price dynamics to be considered, and these explain very well the signs and significance of the parameters in the estimated models. Nevertheless, the models need to have a detailed specification with the various quantiles being related to multiple factors through coefficients which have dynamic properties themselves related to some of the exogenous factors. This modelling requirement motivates the development of quantile models that need fully parametric specifications to capture dynamics through exogenous factors and time-varying coefficients.

A novel general methodology has therefore been developed in which time-varying multi factor coefficients are recursively estimated with a Kalman filter using maximum likelihood. Since the likelihood function is non-differentiable, the problem is re-formulated as a non-linear optimization with constraints, and furthermore re-formulated again by moving the constraints into the objective function to solve an augmented Lagrangian method. With careful selection of starting values, maximum likelihood estimates were thereby acquired. As a general approach, we would expect this to be useful in many applications of risk management and quantile estimation where there is dynamic complexity in price formation and plausible exogenous price drivers.