Road freight transport and sustainability in Britain: 1984-2007

This report builds on a conceptual framework developed by McKinnon (2007) for analysing the performance and impacts of freight transport. This framework is used to analyse the performance of road freight transport by heavy goods vehicles (HGVs) in Britain over the period from 1984 to 2007 using data from the Continuing Survey of Road Goods Transport. The efficiency and intensity of HGV operations are assessed. The determinants that have led to the changes in HGV key variables (including length of haul, vehicle carrying capacity, lading factor, empty running) are considered. A forecast of the future level of HGV activity (in terms of vehicle kilometres travelled, fuel consumed and CO2 emissions) in 2020 is presented.

Evaluating monthly volatility forecasts using proxies at different frequencies

This paper analyses the forecastability of stock returns monthly volatility. The forecast obtained from GARCH and AGARCH models with Normal and Student's t errors are evaluated with respect to proxies for the unobserved volatility obtained through sampling at different frequencies. It is found that aggregation of daily multi-step ahead GARCH-type forecasts provide rather accurate predictions of monthly volatility.

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.