5,825 research outputs found
Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction
Assessing systemic risk in financial markets is of great importance but it
often requires data that are unavailable or available at a very low frequency.
For this reason, systemic risk assessment with partial information is
potentially very useful for regulators and other stakeholders. In this paper we
consider systemic risk due to fire sales spillover and portfolio rebalancing by
using the risk metrics defined by Greenwood et al. (2015). By using the Maximum
Entropy principle we propose a method to assess aggregated and single bank's
systemicness and vulnerability and to statistically test for a change in these
variables when only the information on the size of each bank and the
capitalization of the investment assets are available. We prove the
effectiveness of our method on 2001-2013 quarterly data of US banks for which
portfolio composition is available.Comment: 36 pages, 6 figures, Accepted on Journal of Economic Dynamics and
Contro
Systemic risk diagnostics: coincident indicators and early warning signals
We propose a novel framework to assess financial system risk. Using a dynamic factor framework based on state-space methods, we construct coincident measures (‘thermometers’) and a forward looking indicator for the likelihood of simultaneous failure of a large number of financial intermediaries. The indicators are based on latent macro-financial and credit risk components for a large data set comprising the U.S., the EU-27 area, and the respective rest of the world. Credit risk conditions can significantly and persistently de-couple from macro-financial fundamentals. Such decoupling can serve as an early warning signal for macro-prudential policy. JEL Classification: G21, C33credit portfolio models, financial crisis, frailty-correlated defaults, state space methods, systemic risk
A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications
Enterprise financial risk analysis aims at predicting the enterprises' future
financial risk.Due to the wide application, enterprise financial risk analysis
has always been a core research issue in finance. Although there are already
some valuable and impressive surveys on risk management, these surveys
introduce approaches in a relatively isolated way and lack the recent advances
in enterprise financial risk analysis. Due to the rapid expansion of the
enterprise financial risk analysis, especially from the computer science and
big data perspective, it is both necessary and challenging to comprehensively
review the relevant studies. This survey attempts to connect and systematize
the existing enterprise financial risk researches, as well as to summarize and
interpret the mechanisms and the strategies of enterprise financial risk
analysis in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. This paper provides a
systematic literature review of over 300 articles published on enterprise risk
analysis modelling over a 50-year period, 1968 to 2022. We first introduce the
formal definition of enterprise risk as well as the related concepts. Then, we
categorized the representative works in terms of risk type and summarized the
three aspects of risk analysis. Finally, we compared the analysis methods used
to model the enterprise financial risk. Our goal is to clarify current
cutting-edge research and its possible future directions to model enterprise
risk, aiming to fully understand the mechanisms of enterprise risk
communication and influence and its application on corporate governance,
financial institution and government regulation
Community Bank Assessment of Agricultural Portfolio Risk Exposure: The Literature and the Methods in Use
Agricultural Finance,
Multi-agent hybrid mechanism for financial risk management
Purpose: The goal of this study was to propose the multi-agent mechanism to forecast the
corporate financial distress.
Design/methodology/approach: This study utilized numerous methods, namely random
subspace method, discriminant analysis and decision tree to construct the multi-agent
forecasting model.
Findings: The study shows a superior forecasting performance.
Originality/value: The use of multi-agent model to predict the corporate financial distress.Peer Reviewe
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