Martin Odening and Jan Hinrichs Abstract: This article examines problems that may occur when conventional Value-at-Risk (VaR) estimators are used to quantify market risks in an agricultural context. For example, standard VaR methods, such as variance-covariance method or historical simulation, can fail when the return distribution is fat tailed. This problem is aggravated when long-term VaR for...

In this paper, genetic programming and artificial neural networks are employed to forecast two different exchange rates, US dollar/Japanese Yen and US dollar/Taiwan dollar. Extended forecasts (that go beyond one-step-ahead) obtained using the computational techniques were compared with naïve random walk predictions of the two exchange rates. Sixteen-step-ahead forecasts obtained using genetic p...

Delays caused by uncertainty in weather forecasts can be reduced by improving traffic flow management decisions. This paper presents a methodology for traffic flow management under uncertainty in convective weather forecasts. An algorithm for assigning departure delays and reroutes to aircraft is presented. Departure delay and route assignment are executed at multiple stages, during which, upda...

This paper studies the properties and determinants of managers’ multi-year financial forecasts. We ask whether, by how much, and why biases in managers’ forecasts of revenues, expenses and profits depend on the forecasting horizon and the verifiability of firms’ assets. Since public companies rarely divulge their internal multi-year financial projections to outsiders, we use the oneto five-year...

A parametric approach to estimating and forecasting Value-at-Risk (VaR) and expected shortfall (ES) for a heteroscedastic financial return series is proposed. The well-known GJR–GARCH form models the volatility process, capturing the leverage effect. To capture potential skewness and heavy tails, the model assumes an asymmetric Laplace form as the conditional distribution of the series. Further...

Skillful medium-range weather forecasts are critical for water resources planning and management. This study aims to improve 15-day-ahead accumulated precipitation forecasts by combining biweekly weather and disaggregated climate forecasts. A combination scheme is developed to combine reforecasts from a numerical weather model and disaggregated climate forecasts from ECHAM4.5 for developing 15-...

An ideal band-pass filter removes the frequency components of a time series that lie within a particular range of frequencies. In practice, it is difficult to construct an "ideal" band-pass filter as it requires an infinite number of data points. Therefore, an approximation to an ideal filter is used to extract the components of a time series in a particular frequency range, such as business cy...

Forecast DNI values in hourly resolution for one day ahead are evaluated by a comparison with pyrheliometer ground measurements. Three months of such day ahead forecasts from four different providers for a site close to Questa, NM, USA are analyzed firstly by calculating the RMSE and the mean bias error. Secondly, cumulative distributions of the DNI forecast errors are calculated as they better...

We introduce an effective and computationally fast approach to combine conditional quantile forecasts. The approach uses the information of the relevant loss function for the quantile problem associated to each candidate model in order to define forecast combination weights in a dynamic fashion. Two important advantages of the proposed method are that i) does not require numerical optimization ...

This paper assesses the forecast performance of a set of VAR models under a growing number of restrictions. With a maximum forecast horizon of 12 years, we show that the farther the horizon is, the more structured and restricted VAR models have to be to produce accurate forecasts. Indeed, unrestricted VAR models, not subjected to integration or cointegration, are poor forecasters for both short...