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Horizon Increases (horizon + increase)

Distribution by Scientific Domains

Mathematics and Statistics	50%
Business, Economics, Finance and Accounting	50%

Selected Abstracts

A refined deterministic linear program for the network revenue management problem with customer choice behavior

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 6 2008
Sumit Kunnumkal
Abstract We present a new deterministic linear program for the network revenue management problem with customer choice behavior. The novel aspect of our linear program is that it naturally generates bid prices that depend on how much time is left until the time of departure. Similar to the earlier linear program used by van Ryzin and Liu (2004), the optimal objective value of our linear program provides an upper bound on the optimal total expected revenue over the planning horizon. In addition, the percent gap between the optimal objective value of our linear program and the optimal total expected revenue diminishes in an asymptotic regime where the leg capacities and the number of time periods in the planning horizon increase linearly with the same rate. Computational experiments indicate that when compared with the linear program that appears in the existing literature, our linear program can provide tighter upper bounds, and the control policies that are based on our linear program can obtain higher total expected revenues. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008 [source]

Updating ARMA predictions for temporal aggregates

JOURNAL OF FORECASTING, Issue 4 2004
Sergio G. Koreisha
Abstract This article develops and extends previous investigations on the temporal aggregation of ARMA predications. Given a basic ARMA model for disaggregated data, two sets of predictors may be constructed for future temporal aggregates: predictions based on models utilizing aggregated data or on models constructed from disaggregated data for which forecasts are updated as soon as the new information becomes available. We show that considerable gains in efficiency based on mean-square-error-type criteria can be obtained for short-term predications when using models based on updated disaggregated data. However, as the prediction horizon increases, the gain in using updated disaggregated data diminishes substantially. In addition to theoretical results associated with forecast efficiency of ARMA models, we also illustrate our findings with two well-known time series. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Futures hedging under mark-to-market risk

THE JOURNAL OF FUTURES MARKETS, Issue 4 2003
Donald Lien
This article introduces mark-to-market risk into the conventional futures hedging framework. It is shown that a hedger concerned with maximum daily loss will considerably reduce his futures position when the risk is taken into account. In case of a moderate hedge horizon, the hedger will hedge approximately 80% of his spot position. The effect of mark-to-market risk decreases very slowly as the hedge horizon increases. If the hedger is concerned with average daily loss, the effect is minimal for a moderate hedge horizon. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:389,398, 2003 [source]

EXPONENTIAL SMOOTHING AND NON-NEGATIVE DATA

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 4 2009
Muhammad Akram
Summary The most common forecasting methods in business are based on exponential smoothing, and the most common time series in business are inherently non-negative. Therefore it is of interest to consider the properties of the potential stochastic models underlying exponential smoothing when applied to non-negative data. We explore exponential smoothing state space models for non-negative data under various assumptions about the innovations, or error, process. We first demonstrate that prediction distributions from some commonly used state space models may have an infinite variance beyond a certain forecasting horizon. For multiplicative error models that do not have this flaw, we show that sample paths will converge almost surely to zero even when the error distribution is non-Gaussian. We propose a new model with similar properties to exponential smoothing, but which does not have these problems, and we develop some distributional properties for our new model. We then explore the implications of our results for inference, and compare the short-term forecasting performance of the various models using data on the weekly sales of over 300 items of costume jewelry. The main findings of the research are that the Gaussian approximation is adequate for estimation and one-step-ahead forecasting. However, as the forecasting horizon increases, the approximate prediction intervals become increasingly problematic. When the model is to be used for simulation purposes, a suitably specified scheme must be employed. [source]