An affective approach to moral motivation

Over the last few years, there has been a surge of work in a new field called "moral psychology", which uses experimental methods to test the psychological processes underlying human moral activity. In this paper, I shall follow this line of approach with the aim of working out a model of how people form value judgements and how they are motivated to act morally. I call this model an "affective picture": 'picture' because it remains strictly at the descriptive level and 'affective' because it has an important role for affects and emotions. This affective picture is grounded on a number of plausible and empirically supported hypotheses. The main idea is that we should distinguish between various kinds of value judgements by focusing on the sort of state of mind people find themselves in while uttering a judgement. "Reasoned judgements" are products of rational considerations and are based on preliminary acceptance of norms and values. On the contrary, "basic value judgements" are affective, primitive and non-reflective ways of assessing the world. As we shall see, this analysis has some consequences for the traditional internalism-externalism debate in philosophy; it highlights the fact that motivation is primarily linked to "basic value judgements" and that the judgements we openly defend might not have a particular effect on our actions, unless we are inclined to have an emotional attitude that conforms to them.

Modeling Dependence Structure and Forecasting Market Risk with Dynamic Asymmetric Copula

We investigate the dynamic and asymmetric dependence structure between equity portfolios from the US and UK. We demonstrate the statistical significance of dynamic asymmetric copula models in modelling and forecasting market risk. First, we construct “high-minus-low" equity portfolios sorted on beta, coskewness, and cokurtosis. We find substantial evidence of dynamic and asymmetric dependence between characteristic-sorted portfolios. Second, we consider a dynamic asymmetric copula model by combining the generalized hyperbolic skewed t copula with the generalized autoregressive score (GAS) model to capture both the multivariate non-normality and the dynamic and asymmetric dependence between equity portfolios. We demonstrate its usefulness by evaluating the forecasting performance of Value-at-Risk and Expected Shortfall for the high-minus-low portfolios. From back-testing, e find consistent and robust evidence that our dynamic asymmetric copula model provides the most accurate forecasts, indicating the importance of incorporating the dynamic and asymmetric dependence structure in risk management.