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Data Analysis for Risk Management – Economics, Finance and Business

This reprint concerns methods of data analysis for risk management in economics, finance, and business. The presented papers contain research on data analysis methods, including classical statistical methods, and machine learning methods that have emerged from statistics and are being effectively ap...

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Збережено в:
Формат: Online
Мова:Англійська
Опубліковано: MDPI - Multidisciplinary Digital Publishing Institute 2024
Предмети:
exchange rate volatility
currency misalignment
business cycle
central and eastern European countries
public management
risk management
public hospitals
financial stability
stakeholders’ engagement
survey research
Poland
systemic risk
systemic illiquidity
liquidity crisis
parametric models
quantitative methods
emerging markets
frontier markets
CEE
bancassurance
insurance
risk factors
default
bankruptcy risk
Poisson process
doubly stochastic assumption
ROC curve
accuracy ratio
leverage
new definition of default
credit risk models
Bayesian approach
ESG
volatility
GARCH
copula
tail dependence
banking sector
DCoVaR
MES
SRISK
quantile regression
EGARCH
DCC
value at risk
credit scorecard development
open source
R
neural network
stock exchange
accounting systems
finance
credit scoring
Gini coefficient
n/a
thema EDItEUR::K Economics, Finance, Business and Management
thema EDItEUR::K Economics, Finance, Business and Management::KF Finance and accounting
Онлайн доступ:ONIX_20240704_9783725814169_234
Теги: Додати тег
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Резюме:This reprint concerns methods of data analysis for risk management in economics, finance, and business. The presented papers contain research on data analysis methods, including classical statistical methods, and machine learning methods that have emerged from statistics and are being effectively applied using high-speed computers, considering the availability of big data.

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