The present article deals with intra-horizon risk in models with jumps. Our general understanding of intra-horizon risk is along the lines of the approach taken in [BRSW04], [Ro08], [BMK09], [BP10], and [LV19]. In particular, we believe that quantifying market risk by strictly relying on...

The severity of extreme weather events is increasing due to climate change, giving rise to physical climate risk. However, physical climate risk is not only driven by the severity of individual hazards, but also by the interdependence of those hazards. This paper establishes bounds for the...

Heavy tails and volatility clusters are both stylized facts of financial returns that destabilize markets. The former are extreme events by definition and the latter can accelerate adverse market developments. This work disentangles the two sources and examines which one does the greater damage...

To ensure that central counterparties (“CCPs”) are safe in all market conditions the European Union (EU) has adopted legislation, commonly known as the European Market Infrastructure Regulation (“EMIR”) that deals with their organisational requirements, including prudential requirements...

We study risk sharing games with quantile-based risk measures and heterogeneous beliefs, motivated by the use of internal models in finance and insurance. Explicit forms of Pareto-optimal allocations and competitive equilibria are obtained by solving various optimization problems. For Expected...

Monetary risk measures classify a financial position by the minimal amount of external capital that must be added to the position to make it acceptable.We propose a new concept: intrinsic risk measures. The definition via external capital is avoided and only internal resources appear. An...

We introduce a class of quantile-based risk measures that generalize Value at Risk (VaR) and, likewise Expected Shortfall (ES), take into account both the frequency and the severity of losses. Under VaR a single confidence level is assigned regardless of the size of potential losses. We allow...

The recursive prediction and filtering formulas of the Kalman filter are difficult to implement in nonlinear state space models. For Gaussian linear state space models, or for models with qualitative state variables, the recursive formulas of the filter require the updating of a finite number of...