Nos derniers articles

This paper focuses on predicting #corporate #default #risk using frailty correlated default #models with subjective judgments. The study uses a #bayesian approach with the Particle Markov Chain #montecarlo algorithm to analyze data from #us public non-financial firms between 1980 and 2019. The findings suggest that the volatility and mean reversion of the hidden factor have a significant impact on the default intensities of the firms.

"... the new Climate Risk Division will integrate climate risks into its supervision of regulated entities, support the industry’s growth in managing climate risks, coordinate with international, national, and state regulators, develop internal capacity on climate-related financial risks, support the capacity-building of peer regulators on climate-related supervision, and ensure fair access to financial services for all communities, especially those most impacted by climate change. "

This study proposes a new approach to the analysis of #systemicrisk in #financialsystems, which is based on the #probability amount of exogenous shock that can be absorbed by the system before it deteriorates, rather than the size of the impact that exogenous events can exhibit. The authors use a linearized version of DebtRank to estimate the onset of financial distress, and compute localized and uniform exogenous shocks using spectral graph theory. They also extend their analysis to heterogeneous shocks using #montecarlo#simulations. The authors argue that their approach is more general and natural, and provides a standard way to express #failure#risk in financial systems.

"In this paper we propose efficient #bayesian Hamiltonian #montecarlo method for estimation of #systemicrisk#measures , LRMES, SRISK and ΔCoVaR, and apply it for thirty global systemically important banks and for eighteen largest #us#financialinstitutions over the period of 2000-2020. The systemic risk measures are computed based on the Dynamic Conditional Correlations model with generalized asymmetric #volatility. A policymaker may choose to rank the firms using some quantile of their systemic risk distributions such as 90, 95, or 99% depending on #risk preferences with higher quantiles being more conservative."

"... compares two industries where legislative requirements differ, but it finds the same pattern: the ideals of enterprise risk management are being not implemented in practice."

"By identifying research gaps and conceptualizing a research agenda, this paper continues to serve the academia to broaden the research field of risk disclosure, esp. for banks."

"... we contribute both empirically and conceptually to a better understanding of the nexus of AI and regulation and the underlying normative decisions. A comparison of the scientific proposals with the proposed European AI regulation illustrates the specific approach of the regulation, its strengths and weaknesses."

"... banks with robust pre-crisis regulatory capital ratios are less risky (have a lower insolvency risky) relative to less-capitalised banks amid the crisis period. This suggests that the post 2007-09 Basel reforms have succeeded, to some extent, in strengthening the risk-resilience of banks during the Covid-19 economic fallout."

"The possibilities of a Keynesian-Knightian synthesis as a way forward are considered by comparing these signposts. It is argued that, although there is some common ground between Knight and Keynes, there are fundamental differences particularly associated with Keynes’s concept of weight of argument."

"The integration of data in a geographic information system enables the visualization and spatialization of risk, but also each of its components."