Airline Codeshare Alliances

The paper juxtaposes the challenges that airline codeshare alliances create for analytical information systems on the one hand and their motivation from a marketing perspective on the other. The authors review the state-of-the-art literature on potential marketing benefits and analyze the impact on airline planning systems. In this regard, revenue management systems are of particular interest. Based on a simulation study, the authors infer a severe impact of decentralized codeshare controls as currently widely implemented in the industry on revenue management performance. In the scenarios examined, complementary codesharing reduces alliance-wide revenues by up to 1 %. Losses increase when a carrier experiences high local demand or a high degree of codeshare demand, and disseminate over the whole network. Virtual codeshares also cause losses of 0.3 % to 1.5 % depending on the discount level offered by the marketing carrier and on the demand structure. Finally, the authors formulate a set of managerial implications based on these findings.     

Consumer views of self-service technologies

This study examined how customers perceived and classified a set of 12 self-service technologies (SSTs) based on multidimensional scaling. The authors describe first, how the classifications developed by Lovelock are perceived by consumers and then, how the individual SSTs map onto those classifications. Results of the study show that 67% of the variance in classification is explained by two dimensions of customization/standardization and separability/inseparability. The authors also propose a typology for the SSTs based on their groupings in the classification framework. The authors discuss the managerial implications of the findings and suggest directions for future academic research.     

Option pricing under fast‐varying long‐memory stochastic volatility

Recent empirical studies suggest that the volatility of an underlying price process may have correlations that decay slowly under certain market conditions. In this paper, the volatility is modeled as a stationary process with long‐range correlation properties in order to capture such a situation, and we consider European option pricing. This means that the volatility process is neither a Markov process nor a martingale. However, by exploiting the fact that the price process is still a semimartingale and accordingly using the martingale method, we can obtain an analytical expression for the option price in the regime where the volatility process is fast mean reverting. The volatility process is modeled as a smooth and bounded function of a fractional Ornstein–Uhlenbeck process. We give the expression for the implied volatility, which has a fractional term structure.     

Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution

The realized-GARCH framework is extended to incorporate the two-sided Weibull distribution, for the purpose of volatility and tail risk forecasting in a financial time series. Further, the realized range, as a competitor for realized variance or daily returns, is employed as the realized measure in the realized-GARCH framework. Sub-sampling and scaling methods are applied to both the realized range and realized variance, to help deal with inherent micro-structure noise and inefficiency. A Bayesian Markov Chain Monte Carlo (MCMC) method is adapted and employed for estimation and forecasting, while various MCMC efficiency and convergence measures are employed to assess the validity of the method. In addition, the properties of the MCMC estimator are assessed and compared with maximum likelihood, via a simulation study. Compared to a range of well-known parametric GARCH and realized-GARCH models, tail risk forecasting results across seven market indices, as well as two individual assets, clearly favour the proposed realized-GARCH model incorporating the two-sided Weibull distribution; especially those employing the sub-sampled realized variance and sub-sampled realized range.     

Spatial dependencies and spatial drift in public transport seasonal ticket revenue data

When firms' customers are located in spatially dispersed areas, it can be difficult to manage service quality on a geographically small scale because the relative importance of service quality might vary spatially. Moreover, standard approaches discussed so far in the marketing science literature usually neglect spatial effects, such as spatial dependencies (spatial autocorrelation for example) and spatial drift (spatial non-stationarity). We propose a comprehensive approach based on spatial econometric methods that covers both issues. Based on the real company data on seasonal ticket revenue of a local public transport service company, we show that addressing such spatial effects of service data can improve management's ability to implement programs aimed at enhancing seasonal ticket revenue. In particular, the article shows how a spatial revenue response function might be specified.     

Exploring the interaction of firm-level change and national institutional environments in shaping employment and union relations: a comparative case study of four European airlines

Drawing on the example of the airline industry, this paper explores in a longitudinal comparative case study the question of how firm-level changes and national institutional environments interact in shaping employee and union relations. Adding to previous research in comparative institutional analysis and comparative employment relations, we illustrate that the way in which industry pressures and national-level effects play out to influence employee and union relations depends on firm-level changes, mainly in the form of firm growth, acquisitions and the foundation of new subsidiaries. We show in particular that depending on firm-level changes, the very same firm might engage differently with a given institutional context at different points in time. Hence, our work illustrates the importance of firm growth, acquisitions and the foundation of new subsidiaries in explaining the shifting interaction between the firm and its institutional environment, and its implications for changing employee and union relations within firms.     

Regional economic impact of oil spills

This article demonstrates an approach of coupling an environmental model to I-O analysis which aims to quantify the regional economic impact of an environmental accident. The model is implemented with the data of a potential oil spill interacting with the salmon aquaculture industry in Northern Norway. The production loss in salmon aquaculture and the regional income impact is computed and discussed. The approach used in this article could be a model for estimating the regional socio-economic impact of environmental factors like water and air pollution.     

Estimating the output gap in real time: A factor model approach

By using a dynamic factor model, we can substantially improve the reliability of real-time output gap estimates for the U.S. economy. First, we use a factor model to extract a series for the common component in GDP from a large panel of monthly real-time macroeconomic variables. This series is immune to revisions to the extent that revisions are due to unbiased measurement errors or idiosyncratic news. Second, our model is able to handle the unbalanced arrival of the data. This yields favorable nowcasting properties and thus starting conditions for the filtering of data into a trend and deviations from a trend. Combined with the method of augmenting data with forecasts prior to filtering, this greatly reduces the end-of-sample imprecision in the gap estimate. The increased precision has economic importance for real-time policy decisions and improves real-time inflation forecasts.