\section{Introduction}
\label{sec:introduction}

When one security suffers a violent price move, does the shock stay local or
does it spread? The answer shapes almost everything that happens inside a
trading day: how fast prices aggregate information, how much liquidity
providers must charge for standing in the market, how execution algorithms
should schedule parent orders, and whether an idiosyncratic event can grow
into a market-wide dislocation. Regulators confront the same question from the
other side: episodes such as May 6, 2010 showed that modern equity markets can
move from normal functioning to synchronized collapse within minutes, faster
than any human intervention. Yet the empirical record on intraday shock
transmission is built almost entirely from short samples, small panels, or
single episodes. Whether the propagation mechanism itself has changed over the
three decades in which trading moved from exchange floors to fragmented,
algorithmic, ETF-intermediated electronic markets is, to our knowledge,
undocumented.

The tension motivating this paper is that theory predicts change in both
directions. Faster, cheaper, more competitive markets should absorb ordinary
idiosyncratic shocks more efficiently: liquidity provision reacts within
seconds, arbitrage capital is abundant in normal times, and information is
incorporated nearly continuously. But the same institutions that supply this
efficiency---high-speed market making with tight inventory limits, cross-asset
arbitrage, index and basket intermediation---withdraw or transmit pressure
precisely when many securities move together. Modern markets may therefore
absorb ordinary shocks more efficiently while transmitting extreme shocks more
rapidly and discontinuously.

We ask how the speed, breadth, persistence, and asymmetry of intraday shock
propagation across U.S.\ equities evolved over the past three decades, and
under which market conditions an initially idiosyncratic shock becomes
sectoral or market-wide.

Our laboratory is a panel of \DataRows{} one-minute OHLCV bars, originally
sourced from Finnhub.io, covering
\DataTickers{} tickers---including delisted securities---from January 1992
through March 2026. The data are audited exhaustively before use: observed
trading dates match the official exchange calendar exactly over thirty-four
years, timestamp conventions are verified on both sides of every
daylight-saving transition, and integrity violations are negligible
(\DataOHLCViol{} impossible OHLC bars in \DataRowsBn{} billion). Because the
source provides bars only for minutes with trades, and identifies securities
by ticker alone, the empirical design works with liquidity-screened,
lagged-rank universes, ticker spells rather than tickers, and detected
corporate-action windows; Section~\ref{sec:data} documents every screen.

Three findings organize the paper. First, \emph{markets absorb isolated
idiosyncratic shocks---and we can measure how completely}. We detect
\NEventsRealMM{} million one-minute events in which a single top-500 stock
moves by at least six robust standard deviations of its own residual return
while the rest of the market is calm. Relative to matched within-stock
placebos, the abnormal share of other securities responding within 5 to 120
minutes is economically zero (point estimates of $-1$ to $-3\times10^{-4}$
with day-block bootstrap standard errors of about $1\times10^{-4}$), in every
decade, for positive and negative shocks alike. Idiosyncratic events die where
they land.

Second, \emph{the transmission of common information has become nearly
instantaneous}. In 1996 the average pairwise correlation of one-minute returns
among the hundred largest stocks was \EppsNinetySixOne{}---economically zero,
the classical Epps effect---while thirty-minute returns already comoved at
\EppsNinetySixThirty. By 2015 the one-minute correlation was
\EppsFifteenOne{} against \EppsFifteenThirty{} at thirty minutes: the Epps
curve has flattened almost completely. Information that once took half an hour
to spread across the cross-section now spreads within the minute.

Third, \emph{tail risk has migrated from the security to the minute}. Ordinary
extreme moves became rare: the frequency of one-minute returns exceeding 1\%
fell from roughly \ExtremeGtOneNineties{} per 10,000 in the 1990s to
\ExtremeGtOneTens{} per 10,000 in the 2010s---a factor of about
\ExtremeDropFactor. But synchronized extremes did not: minutes in which more
than 2\% of the universe jumps beyond $4\sigma$ simultaneously---systemic
co-jump minutes---spike by orders of magnitude in 2008 and 2020, and the
frequency of $|r|>5\%$ minute returns rose from \ExtremeGtFivePreCovid{} per
10,000 in 2017--2019 to \ExtremeGtFiveCovid{} in 2020, a factor of
\ExtremeCovidFactor. The joint pattern---absorption of the idiosyncratic,
synchronization of the systemic---is the modern form of market fragility.

Identification receives explicit attention throughout. The central threat is
that securities respond to common information rather than to each other. Our
shock definition residualizes against a leave-one-out market factor with
strictly lagged loadings and scales; isolation filters require a calm market
and fewer than 2\% simultaneous extremes at the event minute; every event is
compared to a placebo in the same stock at the same minute of day on a calm
day; and the pre-event window shows no abnormal response, ruling out
anticipation or mechanical selection. What the isolation filters remove---the
simultaneous-extreme minutes---we do not discard but study separately as the
systemic co-jump sample. Common-information contamination is thus measured,
not assumed away.

Mechanically, we ask \emph{where} propagation should hide if it exists:
responses concentrated among the shocked stock's closest lagged-correlation
neighbors, cumulative rather than point-in-time responses, and state
dependence across calm and stressed markets and across shock magnitudes.
Section~\ref{sec:mechanisms} reports these probes.

The paper contributes to four literatures. To the contagion literature, which
measures transmission at daily or lower frequency for small panels, we
contribute event-level evidence from the full U.S.\ cross-section at the
minute scale over thirty-four years. To the market-microstructure and
high-frequency-trading debate, which contrasts normal-times liquidity gains
with episodic fragility, we contribute the missing time series: propagation
and synchronization measured uniformly across the entire structural
transformation of U.S.\ trading. To the co-jump econometrics literature, we
contribute a long-sample account of how systemic co-jump frequency and
severity evolved. And to the measurement literature, we document what OHLCV
bars can and cannot identify about liquidity and transmission, with every
limitation quantified.

Section~\ref{sec:data} describes and audits the data.
Section~\ref{sec:measurement} defines shocks, outcomes, and networks.
Section~\ref{sec:design} presents the empirical design.
Section~\ref{sec:results} reports the descriptive transformation of the market
and the baseline propagation results. Section~\ref{sec:mechanisms} examines
networks, cumulative responses, and co-jumps. Section~\ref{sec:robustness}
summarizes robustness, and Section~\ref{sec:conclusion} concludes.