Current applications of Graph Neural Networks in citywide short-term crash risk prediction have been limited by a … administrative purposes, limiting the use of short-term crash risk predictions by practitioners. This paper applies a gated localised … coefficient, and others), which enhance the GLDNet capability to identify crash risk on both central and peripherical areas. In …

, heavy tails, and nonellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio … optimization with active risk monitoring. The former selects optimal portfolio weights. The later, independently, initiates market … leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk …

This paper investigates whether multivariate crash risk is priced in the cross- section of expected stock returns …. Motivated by a theoretical asset pricing model, we capture the multivariate crash risk of a stock by a combined measure based on …. We find that stocks with a high exposure to joint crashes of the market and the momentum factor bear a risk premium which …

This paper investigates whether multivariate crash risk (MCRASH), defined as exposure to extreme realizations of … returns than stocks with low MCRASH. The premium is not explained by linear factor exposures, alternative downside risk … measures or stock characteristics. Extending market-based definitions of crash risk to other well-established factors helps to …

environment interspersed by corrections increasing in amplitude and frequency. This calls for more adaptive dynamic risk … risk that should accurately be estimated is crash risk.This article applies the Log-Periodic Power Law Singularity (LPPLS …) model of endogenous asset price bubbles to monitor crash risk. The model is calibrated to 15 years market history for five …

to hedge downside risks associated with portfolios. Our findings are of interest to regulators, risk managers, investors …