Volatility Modeling in Financial Market

This Reprint brings together the editorial and all articles published in the Special Issue, "Volatility Modeling in Financial Market". It offers a broad view of contemporary volatility and financial risk modelling, combining classical econometric approaches with newer computational and machine-learning methods. Its contributions examine equities, exchange rates, corporate bonds, cryptocurrencies, derivatives, and credit portfolios, showing how volatility, dependence, asymmetry, tail risk, and market synchronisation can be measured and interpreted across financial settings. This collection highlights the relevance of ARCH/GARCH-family models, DCC-GARCH, EGARCH, asymmetric GARCH specifications, realised-measure approaches, and multiplicative error models, while presenting hybrid and data-driven frameworks such as ANFIS, normalising flows, machine learning, and deep learning. The studies address both methodological questions and applied problems, including volatility forecasting, derivatives and spot market volatility, corporate bond risk, cryptocurrency risk assessment, credit risk prediction, and the impact of political and trade-related shocks on international stock market co-movement. By combining theoretical, methodological, and empirical perspectives, this Reprint shows that modern volatility modelling is no longer confined to a single modelling tradition. Instead, it requires interpretability, flexibility, robustness, and sensitivity to the market context. This collection will be of interest to researchers, practitioners, and graduate students working in financial econometrics, risk management and asset pricing.