Conference Agenda

We study the effects of central bank backstops on sovereign risk premia using the Eurosystem’s Transmission Protection Instrument (TPI) announced in July 2022. We develop a nonlinear non-Gaussian state-space model that decomposes euro area sovereign yields into expected short rates, a common term premium, and country-specific default, redenomination, liquidity, and convenience premia. Structural shocks are identified through heteroscedasticity and fat tails. Using euro area data from 2015 to 2025, we extract latent risk premia and assess the impact of the TPI using event-time and differences-in-differences designs. The results show that the TPI primarily increased the convenience value of sovereign bonds and reduced the volatility of a subset of shocks, while leaving other risk premia largely unchanged. Lower convenience-adjusted yields partially dampened the transmission of policy rate hikes to medium-term sovereign yields.

This paper examines the properties of the loss functions used for forecasting Value-at-Risk (VaR) and

Expected Shortfall (ES). We show that the weighted arithmetic average commonly used to construct a

forecast combination utilises the convexity property of the loss function only in case of Value-at-Risk. This

paper introduces a novel forecasting combination approach for Expected Shortfall, which is constructed using

weighted harmonic means. We show that only in this case the insurance against model risk is guaranteed.

To construct combination weights consistent with this aggregation result, we propose a novel forecast

combination for tail risk measures based on the Bagged Pretested Forecast Combination (BPFC) algorithm.

The combination weights assigned to candidate models are determined by their predictive performance

using the Model Confidence Set (MCS) test. Unlike many traditional combination methods, BPFC adapts

to changing market conditions while simultaneously facilitating model selection and improving forecast

stability. We evaluate the performance of forecasting combinations for VaR and ES within the framework of

consistent loss functions, highlighting the role of convexity in performance improvements. Our results show

that the advantages of combining forecasts are especially evident when there is substantial disagreement

among candidate models, a situation that commonly arises during turbulent financial periods.

To empirically validate our approach, we apply it to a dataset of 90 stocks spanning various market

capitalizations and covering periods of severe financial stress, including the Global Financial Crisis and the

COVID-19 pandemic. The results illustrate the ability of BPFC to dynamically select and combine the

most effective models from a pool of over 60 candidates, continuously adjusting weights based on model’s

forecasting performance and evolving market conditions.

Following several episodes of financial market turmoil in recent decades, changes in systemic risk have drawn growing attention. Therefore, we propose surveillance schemes for systemic risk, which allow to detect misspecified systemic risk forecasts in an “on-line” fashion. This enables daily monitoring of the forecasts while controlling for the accumulation of false test rejections. Such online schemes are vital in taking timely countermeasures to avoid financial distress. Our monitoring procedures allow multiple series at once to be monitored, thus increasing the likelihood and the speed at which early signs of trouble may be picked up. The tests hold size by construction, such that the null of correct systemic risk assessments is only rejected during the monitoring period with (at most) a pre-specified probability. Monte Carlo simulations illustrate the good finite-sample properties of our procedures. An empirical application to US banks during multiple crises demonstrates the usefulness of our surveillance schemes for both regulators and financial institutions.