Extreme movements in financial time series pose challenges for risk management and forecasting, particularly when their timing is irregular and difficult to anticipate. This study aims to develop a probabilistic framework for detecting and predicting such events using daily Bitcoin returns as a...

Conventional credit risk models understate tail risk by centering on mean default probabilities and neglecting distributional and sectoral heterogeneity. We propose a Quantile Probability of Default (QPD) framework based on unconditional quantile regressions estimated on flow default rates from...

We study a dynamic model of a non-life insurance portfolio. The foundation of the model is a compound Poisson process that represents the claims side of the insurer. To introduce clusters of claims appearing, e.g. with catastrophic events, this process is time-changed by a Lévy subordinator....