On the overhaul of the spillover index: connectedness amongst European financial institutions

Global market interconnectedness: how risks travel during global financial and non-financial crises

This study aims to investigate the dynamic interconnectedness and risk transmission mechanisms among major global equity indices over a comprehensive period (1997–2024). By spanning nearly three decades, the research distinguishes how systemic risk propagates during both financial and non-financial global crises. The analysis uses a Time-Varying Parameter Vector Autoregressive (TVP-VAR) framework. This methodology allows for the capture of evolving connectedness patterns and shifts in spillover intensity across different market regimes, providing a granular view of how return shocks transition between stability and turbulence. The results reveal that financial crises, notably the 2008 Global Financial Crisis, are characterized by intense linkages centered around developed markets (S&P 500, DAX, FTSE 100), which act as primary transmitters of systemic risk. In contrast, emerging markets (KOSPI, IBOVESPA) predominantly function as shock absorbers. Non-financial crises display distinct transmission signatures: the COVID-19 pandemic triggered universal spillovers, while geopolitical events, such as the Russia-Ukraine war, localized risk within European indices (DAX, CAC 40). Additionally, the Dotcom Bubble underscored the role of sector-specific volatility, with the NASDAQ serving as the epicenter of contagion. This research contributes to the literature by providing a long-term comparative taxonomy of risk transmission across diverse crisis types. By isolating the idiosyncratic behaviors of markets during financial versus non-financial shocks, the findings offer critical insights for institutional investors and policymakers regarding portfolio diversification and the limits of financial resilience in an increasingly integrated global economy.

INTERCONNECTEDNESS AND SYSTEMIC RISK AMONG LISTED RUSSIAN FINANCIAL INSTITUTIONS

This paper investigates the structure and dynamics of interconnectedness and systemic risk in the Russian financial system, considering both banks and non- bank financial institutions, based on data for listed Russian financial institutions spanning the pre- and post- 2022 sanctions periods. Our contribution to the existing literature is twofold. First, unlike studies focused predominantly on the banking sector or cross- border spillovers, we examine the within- country topology of interconnectedness across the full spectrum of listed institutions, covering stock exchange, insurance, asset management, leasing, microfinance, and diversified holding categories. Second, we integrate a comprehensive network- econometric framework combining TVPVAR connectedness, the Network Volatility Index with its volatility and contagion decomposition, ΔCoVaR and Marginal Expected Shortfall, and local projection impulse response analysis to study the bi- directional feedback between systemic risk and network topology. Our results demonstrate that the Russian financial system is a concentrated network in which a small core of dominant institutions, including Sberbank, VTB Bank, and Moscow Exchange, generates the majority of system- wide spillovers, while other institutions function predominantly as absorbers. The post- 2022 sanctions regime has intensified rather than fragmented internal interdependencies, and the persistent dominance of contagion over volatility effects indicates a strong internal transmission mechanism. Besides, we find asymmetric feedback whereby systemic risk shocks reshape network topology more persistently than the reverse, with Sberbank and VTB Bank strengthening their net- emitter status after 2022. Our findings emphasize the need for macroprudential policy in Russia to target network architecture rather than individual institutions' volatility.

Networked Volatility: Linking Oil, Petrobrás and VALE in Times of Crisis

Regime-Dependent Integration, Connectedness and Contagion Between India and Global Equity Markets

The present study examined the dynamics of equity-market integration among India and five major global economies: China, Hong Kong SAR, Japan, the United Kingdom and the United States. Daily data were analysed for the period from January 2002 to December 2025. This study employs Johansen co-integration and the Granger causality test, along with a DCC-GARCH model and the Diebold–Yilmaz connectedness approach, to estimate time-varying conditional correlations across crisis regimes. The findings reveal a single long-run co-integrating relationship in the pre-COVID-19 period (2002–2019) that weakens to none when the post-COVID-19 period (2020–2025) is investigated in isolation, suggesting that the intense early-pandemic coupling became moderated as monetary-policy cycles diverged. The Granger causality test showed that the United States consistently and unidirectionally drives the Indian market, while India’s pre-crisis role as a transmitter to Asian markets fades after the pandemic. The DCC-GARCH indicated that India’s conditional correlations with selected economies rose sharply during the 2008 and 2020 crises, peaking with Hong Kong SAR (0.64). The DY connectedness framework reinforced this pattern. Systemwide connectedness rose sharply during both crises, exceeding 57%, compared to roughly 45% in calmer phases. The United States emerged as the key net transmitter of shocks, and India acted as a net receiver.

The Degree of Interconnectedness Between Cryptocurrency and Stock Markets: A Dynamic Wavelet Analysis

Grounded in the theoretical frameworks of safe haven and hedging asset theory, alongside wavelet-based time-frequency analysis, this study investigates the dynamic interconnectedness between three major cryptocurrencies and six global stock markets spanning both developed and emerging economies. Unlike prior wavelet studies that rely predominantly on graphical interpretation, this paper advances the literature by complementing graphical outputs with numerical results, offering a more rigorous and reproducible analytical foundation. Using daily price data from January 2018 to October 2024, the study applies both univariate and multivariate wavelet techniques to capture return co-movements across multiple time horizons. The univariate analysis reveals significant variance in stock returns concentrated at high frequencies, particularly over 2–4-day cycles, with pronounced fluctuations during the COVID-19 pandemic. In emerging markets such as Nigeria, additional volatility is attributed to political instability and macroeconomic crises. The multivariate analysis further demonstrates that observed co-movements between cryptocurrencies and stock markets are largely driven by interdependence rather than contagion. The paper’s findings are relevant to portfolio diversification strategies across both developed and emerging markets for investors combining stock and cryptocurrency assets.

Bibliometric and Empirical Analysis of Volatility Dynamics in Emerging Markets: Policy Uncertainty, Global Spill-through, and Modern Forecasting Approaches

As financial globalization intensifies, equity volatility in emerging markets (EM) is getting more complicated and non-symmetric. The EM volatility literature from 2010 to 2026 is comprehensively analysed in this study by applying systematic literature review (SLR) and PRISMA protocol. The detailed bibliometric mapping carried out as part of the study reveals that, while a stationary model is prevalent in the literature, newer forecasting methods such as Dynamic Model Averaging (DMA) can easily identify regime shifts and structural breaks and GARCH-MIDAS can combine low and high frequency data. The results show that Economic Policy Uncertainty (EPU), international liquidity cycles, and energy shocks are the primary drivers of next-generation systematic volatility, along with the Covid-19 pandemic, geopolitical risks and cryptocurrency diffusion channels. The research aspires to fill the void of a more proactive risk management approach to the risks of international interconnectedness networks and to the creation of more resilient policy mechanisms to face up to the risks of digital contagion channels, in providing an empirically based strategic decision-support guide for policy makers and portfolio managers.