scholarly journals Using Visual Analytics to Enhance Data Exploration and Knowledge Discovery in Financial Systemic Risk Analysis: The Multivariate Density Estimator

10.9776/14307 ◽  
2014 ◽  
Keyword(s):  
Risk Analysis ◽  
Knowledge Discovery ◽  
Systemic Risk ◽  
Visual Analytics ◽  
Data Exploration ◽  
Density Estimator ◽  
Multivariate Density

scholarly journals Integrating Systemic Risk and Risk Analysis Using Copulas

2018 ◽  
Vol 9 (4) ◽  
pp. 561-567 ◽  
Author(s):  
Stefan Hochrainer-Stigler ◽  
Georg Pflug ◽  
Ulf Dieckmann ◽  
Elena Rovenskaya ◽  
Stefan Thurner ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Systemic Risk

scholarly journals Visual Analytics of Stratigraphic Correlation for Multi-Attribute Well-Logging Data Exploration

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 98122-98135
Author(s):  
Yuhua Liu ◽  
Chen Shi ◽  
Qifan Wu ◽  
Rumin Zhang ◽  
Zhiguang Zhou
Keyword(s):  
Visual Analytics ◽  
Well Logging ◽  
Data Exploration ◽  
Stratigraphic Correlation

scholarly journals RiskVA: A visual analytics system for consumer credit risk analysis

2012 ◽  
Vol 17 (4) ◽  
pp. 440-451 ◽  
Author(s):  
Xiaoyu Wang ◽  
Dong Jeong ◽  
Remco Chang ◽  
William Ribarsky
Keyword(s):  
Risk Analysis ◽  
Credit Risk ◽  
Visual Analytics ◽  
Consumer Credit ◽  
Credit Risk Analysis ◽  
Consumer Credit Risk

scholarly journals Systemic Risk Modeling

2020 ◽  
Vol 20 (54) ◽  
Author(s):  
Raphael Espinoza ◽  
Miguel Segoviano ◽  
Ji Yan
Keyword(s):  
Theoretical Model ◽  
Empirical Model ◽  
Systemic Risk ◽  
Empirical Models ◽  
Equity Returns ◽  
Counterparty Risk ◽  
Risk Modeling ◽  
Cross Sectional ◽  
Fire Sales ◽  
Multivariate Density

We propose a framework to link empirical models of systemic risk to theoretical network/ general equilibrium models used to understand the channels of transmission of systemic risk. The theoretical model allows for systemic risk due to interbank counterparty risk, common asset exposures/fire sales, and a “Minsky" cycle of optimism. The empirical model uses stock market and CDS spreads data to estimate a multivariate density of equity returns and to compute the expected equity return for each bank, conditional on a bad macro-outcome. Theses “cross-sectional" moments are used to re-calibrate the theoretical model and estimate the importance of the Minsky cycle of optimism in driving systemic risk.

Sign in / Sign up

Export Citation Format

Share Document