spb/wp6_uqo

1% ============================================================================2\section{Introduction}3\label{sec:introduction}4% ============================================================================56Understanding how volatility propagates across asset classes is fundamental to modern portfolio management, risk assessment, and financial stability monitoring. The interconnectedness of global financial markets has increased substantially over the past two decades, driven by algorithmic trading, cross-border capital flows, exchange-traded funds linking disparate asset classes, and the emergence of cryptocurrency markets as a new store of value. These developments have intensified the channels through which volatility shocks in one market can rapidly transmit to others, raising important questions about the nature, magnitude, and time-varying dynamics of cross-asset volatility spillovers.78The seminal work of \citet{DieboldYilmaz2009,DieboldYilmaz2012,DieboldYilmaz2014} established a rigorous econometric framework for measuring volatility connectedness based on generalized forecast error variance decompositions from vector autoregressive (VAR) models. This approach decomposes the forecast error variance of each variable into contributions from shocks originating in all variables in the system, providing both aggregate and directional measures of spillovers. Subsequent applications have examined volatility transmission within equities \citep{Demirer2018}, across commodities and equity markets \citep{Kang2017}, and between traditional and crypto assets \citep{Bouri2019,Corbet2020}.910However, several gaps remain in the literature. First, most existing studies rely on daily or weekly data, which may obscure the rapid intraday dynamics of volatility transmission. High-frequency data at the 5-minute level captures the real-time propagation of shocks that daily data aggregates away. Second, the inclusion of cryptocurrency markets---particularly their evolution from isolated speculative instruments to increasingly integrated financial assets---has received limited attention within comprehensive multi-asset connectedness frameworks. Third, the regime-dependent nature of volatility transmission has not been systematically examined across the full spectrum of asset classes using consistent high-frequency data.1112This paper addresses these gaps by providing a comprehensive analysis of intraday volatility transmission across four major asset classes---equities, foreign exchange (FX), commodities, and cryptocurrencies---using 5-minute OHLCV data spanning January 2008 to March 2026. We construct daily realized volatility measures for 10 representative instruments and apply the \citet{DieboldYilmaz2012} generalized variance decomposition framework to quantify both static and time-varying spillover dynamics. Specifically, we address the following five research questions:1314\begin{enumerate}[label=\textbf{RQ\arabic*.}, leftmargin=2cm]15    \item How does intraday realized volatility propagate across equities, currencies, commodities, and crypto markets at the 5-minute frequency?16    \item Does the speed and magnitude of cross-asset volatility transmission vary across market regimes (\eg, the COVID-19 crash, the 2022 rate-hiking cycle, the crypto winter)?17    \item Which asset classes act as volatility originators versus receivers, and has this hierarchy shifted over time with the maturation of crypto markets?18    \item Can a high-frequency VAR/network connectedness model identify systemic risk buildups before major market dislocations?19    \item How do intraday volatility spillovers between VIX futures, equity index futures (ES, NQ), and Bitcoin futures differ during U.S.\ versus European versus Asian trading hours?20\end{enumerate}2122Our analysis yields several novel findings. First, U.S.\ equity index futures (E-Mini S\&P 500 and E-Mini Nasdaq 100) are the dominant volatility transmitters in the global system, with net directional spillovers of $+26.80\%$ and $+19.72\%$, respectively. The VIX, often perceived as a barometer of market fear, is in fact a net receiver of volatility shocks ($-23.37\%$), absorbing rather than originating cross-asset volatility. Second, the total spillover index exhibits substantial time variation, ranging from 57.22\% to 81.55\%, with sharp increases preceding and during market stress episodes. Third, regime analysis reveals dramatic shifts in the volatility hierarchy: during the COVID-19 crash, USD/JPY emerges as the dominant shock originator, consistent with safe-haven demand dynamics, while equity futures flip from transmitters to receivers. Fourth, Bitcoin demonstrates a gradual transition from volatility receiver to originator, particularly evident during the 2022 rate-hiking period, suggesting growing crypto--traditional market integration. Fifth, Bitcoin's intraday volatility pattern is remarkably uniform across global trading sessions, in stark contrast to the session-dependent patterns of equity and VIX markets.2324The remainder of this paper is organized as follows. \Cref{sec:literature} reviews the relevant literature. \Cref{sec:methodology} describes the econometric framework. \Cref{sec:data} presents the data and realized volatility construction. \Cref{sec:results} reports the empirical results. \Cref{sec:discussion} discusses the findings in the context of the research questions, and \Cref{sec:conclusion} concludes.25