Xari Rovira

Curricula are an essential ingredient of academic activity in higher education institutions and so it is necessary to develop tools that help improve these curricula. The need to increase student employability has recently been highlighted as an objective for universities. This paper introduces a methodology to help assess curricula in the training of skills for the labour market. Using a combination of the technique for order preference by similarity to the ideal solution (TOPSIS) method and an adaptation of importance-performance analysis (IPA), this methodology measures the gap between graduate perceptions of their university training and of its workplace utility. A dataset of 15,339 graduate opinions was used to analyse the mismatch between the two perceptions. To measure these differences, an index was drawn up enabling quantitative comparisons among subject areas. A ranking is provided, as well as guidelines for enhancing curricula.

We propose a methodology to include night volatility estimates in the day volatility modeling problem with high-frequency data in a realized generalized autoregressive conditional heteroskedasticity (GARCH) framework, which takes advantage of the natural relationship between the realized measure and the conditional variance. This improves volatility modeling by adding, in a two-factor structure, information on latent processes that occur while markets are closed but captures the leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that includes intraday, day, and night volatility estimates is proposed and was empirically tested to confirm whether using night volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate models over those that do not include night volatility estimates.