Clipped wings: corporate social and environmental responsibility in the airline industry

Within the developed world, airlines have responded to the advice of advocates for corporate social and environmental responsibility (CSER) to use the intertwined CSER dimensions of economics, society and environment to guide their business activities. However, disingenuously, the advocates and regulators frequently pay insufficient attention to the economics which are critical to airlines’ sustainability and profits. This omission pushes airlines into the unprofitable domain of CSERplus. The author identifies alleged market inefficiencies and failures, examines CSERplus impacts on international competition and assesses the unintended consequences of the regulations. She also provides innovative ideas for future-proofing airlines.　 Clipped Wings is a treatise for business professionals featuring academic research as well as industry anecdotes. It is written for airlines (including their owners, employees, passengers and suppliers), airports, trade associations, policy makers, educators, students, consultants, CSERplus specialists and anyone who is concerned about the future of competitive airlines.

Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

Volatility and VaR Forecasting in the Madrid Stock Exchange

This paper provides an empirical study to assess the forecasting performance of a wide range of models for predicting volatility and VaR in the Madrid Stock Exchange. The models performance was measured by using different loss functions and criteria. The results show that FIAPARCH processes capture and forecast more accurately the dynamics of IBEX-35 returns volatility. It is also observed that assuming a heavy-tailed distribution does not improve models ability for predicting volatility. However, when the aim is forecasting VaR, we find evidence of that the Student’s t FIAPARCH outperforms the models it nests the lower the target quantile.