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Data Analysis for Risk Management – Economics, Finance and Business
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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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Keywords
- accounting systems
- accuracy ratio
- bancassurance
- Banking Sector
- bankruptcy risk
- Bayesian approach
- Business Cycle
- CEE
- Central and Eastern European countries
- Copula
- credit risk models
- credit scorecard development
- credit scoring
- currency misalignment
- DCC
- DCoVaR
- default
- doubly stochastic assumption
- EGARCH
- Emerging Markets
- ESG
- exchange rate volatility
- Finance
- Financial Stability
- frontier markets
- GARCH
- Gini coefficient
- Insurance
- leverage
- liquidity crisis
- MES
- n/a
- Neural Network
- new definition of default
- open source
- parametric models
- Poisson process
- Poland
- public hospitals
- public management
- quantile regression
- Quantitative methods
- R
- Risk factors
- Risk management
- ROC curve
- SRISK
- stakeholders’ engagement
- stock exchange
- Survey research
- systemic illiquidity
- Systemic Risk
- tail dependence
- thema EDItEUR::K Economics, Finance, Business and Management
- thema EDItEUR::K Economics, Finance, Business and Management::KF Finance and accounting
- value at risk
- volatility
Links
DOI: 10.3390/books978-3-7258-1415-2Editions