We define and develop an approach for risk budgeting allocation -- a risk diversification portfolio strategy -- where risk is measured using a dynamic time-consistent risk measure. For this, we introduce a notion of dynamic risk contributions that generalise the classical Euler contributions and...

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...

VARs are a popular tool for forecasting and structural analysis, but ill-suited to handle occasionally binding constraints, like the effective lower bound on nominal interest rates. We extend the VAR framework by modeling interest rates as censored observations of a latent shadow-rate process,...

Measuring the value at risk(VaR) of crude oil futures poses a challenge for investors due to the high volatility of these prices. Describing the return in the tail distribution is crucial because these extreme values can trigger larger price fluctuations and market risks. Based on the...

This paper presents a new Expected Shortfall (ES) model based on the Quantum Harmonic Oscillator (QHO). It is used to estimate market risk in banks and other financial institutions according to Basel III standard. Predictions of the model agree with the empirical data which displays deviations...

We examine whether the uncertainty related to environmental, social, and governance (ESG) regulation developments is reflected in asset prices. We proxy the sensitivity of firms to ESG regulation uncertainty by the disparity across the components of their ESG ratings. Firms with high ESG...

We propose a model for risk adjustment, in the context of IFRS 17, for surrender risk. Surrender rates are assumed to follow a stochastic process, underpinned by data. The distribution of the present value of future individual cash flows is calculated. Using well-known techniques from the theory...

Estimation of the value-at-risk (VaR) of a large portfolio of assets is an important task for financial institutions. As the joint log-returns of asset prices can often be projected to a latent space of a much smaller dimension, the use of a variational autoencoder (VAE) for estimating the VaR...