interval representing the LSTM's parameter uncertainty. Finally, resulting death rates are showed through a back …

In this study we consider the risk estimation as a stochastic process based on the Sample Quantile Process (SQP …) - which is a generalization of the Value-at-Risk calculated on a rolling sample. Using SQP's, we are able to show and quantify … the pro-cyclicality of the current way financial institutions measure their risk. Analysing 11 stock indices, we show that …

This paper provides a coherent method for scenario aggregation addressing model uncertainty. It is based on divergence … the definition of five fundamental criteria that serve as a basis for our method. Standard risk measures, such as value-at-risk …

Despite well-known shortcomings as a risk measure, Value-at-Risk (VaR) is still the industry and regulatory standard … for the calculation of risk capital in banking and insurance. This paper is concerned with the numerical estimation of the … interpreted as a measure of model uncertainty induced by possible dependence scenarios …

We develop a transparent Bayesian approach to quantify uncertainty about linear stochastic discount factor models. We … and increases with model dimensions. We apply our approach to quantify the time series of model uncertainty in North … American, European, and Asian Pacific stock markets. Model uncertainty is countercyclical across these major stock markets. It …