Data Analysis for Risk Management – Economics, Finance and Business - Krzysztof Jajuga; Józef Dziechciarz (MDPI - Multidisciplinary Digital Publishing Institute, 2024)
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.
Keywords: 650; 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