Introduction to the Special Section Value Development from Middle Childhood to Early Adulthood: New Insights from Longitudinal and Genetically Informed Research

Research into values at an early age has only started recently, although it has expanded quickly and dynamically in the past years. The purpose of this article is twofold: First, it provides an introduction to a special section that aims to help fill the gap in value development research. The special section brings together four new longitudinal and genetically informed studies of value development from the beginning of middle childhood through early adulthood. Second, this article reviews recent research from this special section and beyond, aiming to provide new directions to the field. With new methods for assessing children's values and an increased awareness of the role of values in children's and adolescents' development, the field now seems ripe for an in-depth investigation. Our review of empirical evidence shows that, as is the case with adults, children's values are organized based on compatibilities and conflicts in their underlying motivations. Values show some consistency across situations, as well as stability across time. This longitudinal stability tends to increase with age, although mean changes are also observed. These patterns of change seem to be compatible with Schwartz's (1992) theory of values (e.g., if the importance of openness to change values increases, the importance of conservation values decreases). The contributions of culture, family, peers, significant life events, and individual characteristics to values are discussed, as well as the development of values as guides for behavior.

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.