Said D. Magomedov

The paper asks what happened to the systemic risk of the Russian stock market after the rupture of external channels in 2022: did it decline together with observable integration, or did it relocate within the domestic market itself? Directional volatility connectedness in the Diebold-Yilmaz framework is estimated under a single protocol on two panels: 15 world stock indices over 19.09.2007–30.06.2026 (4,893 trading days) and 7 sectoral indices of the Moscow Exchange over 09.01.2008–30.06.2026 (4,616 days). External connectedness falls to its historical minimum by 2023–2026 (total index 56.7% against 68.6% in 2009–2019) and the TO and FROM measures of the RTS index shrink to 2.9% and 16.9%, while internal sectoral connectedness settles above its pre-crisis norm (67.4% against 56.2%) and the average inter-sectoral correlation rises from 0.51 to 0.62. The spread of the two levels changes sign for the first time in the sample; endogenous dating places the shift at 01.02.2022, and a unified risk-orientation index, built on a common denominator for internal and external sources, detects the onset of reorientation by late 2021. Systemic risk did not vanish; it changed its geometry, which calls for shifting monitoring emphasis from external indicators to internal cross-sectoral channels.

The popularity of cryptocurrency exchanges has surged in recent years, accompanied by the proliferation of new digital platforms and tokens. However, the issue of credit risk and the reliability of crypto exchanges remain critical, highlighting the need for indicators to assess the safety of investing through these platforms. This study examines a unique, hand-collected dataset of 228 cryptocurrency exchanges operating between April 2011 and May 2024. Using various machine learning algorithms, we identify the key factors contributing to exchange shutdowns, with trading volume, exchange lifespan, and cybersecurity scores emerging as the most significant predictors. Since individual machine learning models often capture distinct data characteristics and exhibit varying error patterns, we employ a forecast combination approach by aggregating multiple predictive distributions. Specifically, we evaluate several specifications of the generalized linear pool (GLP), beta-transformed linear pool (BLP), and beta-mixture combination (BMC). Our findings reveal that the beta-transformed linear pool and the beta-mixture combination achieve the best performances, improving forecast accuracy by approximately 4.1% based on a robust H-measure, which effectively addresses the challenges of misclassification in imbalanced datasets.