arXiv subjects

Wenying Yao

Publications and source records attributed to Wenying Yao.

2 recordsLinked to original sources

arXiv2026

Breaking news

This paper examines how regulatory interventions in high-frequency financial markets affect price discovery. We focus on Breaking news, where dynamic circuit breakers trigger trading halts immediately after the release of macroeconomic fundamentals. Within a high-frequency signal-in-noise model, we show that triggering rules complicate statistical inference for the price impact of news, rendering conventional non-parametric jump estimators inconsistent. Building on this insight, we develop a regression-based test for fundamental pricing that accounts for non-vanishing transition times. The test compares transition price changes to efficient jumps implied by observable factors. Our empirical analysis of CME E-mini S\&P 500 futures shows that Breaking news are associated with systematic deviations from fundamental pricing, predominantly in the form of overshooting. Our findings highlight a regulatory trade-off: the appeal of simple and transparent circuit breaker rules must be weighed against their cost of preventing fundamentals from being priced contemporaneously, thereby creating adverse incentives and introducing distortions.

econ.EM↗

arXiv2026

Uncovering Sparse Financial Networks with Information Criteria

Empirical measures of financial connectedness based on Forecast Error Variance Decompositions (FEVDs) often yield dense network structures that obscure true transmission channels and complicate the identification of systemic risk. This paper proposes a novel information-criterion-based approach to uncover sparse, economically meaningful financial networks. By reformulating FEVD-based connectedness as a regression problem, we develop a model selection framework that consistently recovers the active set of spillover channels. We extend this method to generalized FEVDs to accommodate correlated shocks and introduce a data-driven procedure for tuning the penalty parameter using pseudo-out-of-sample forecast performance. Monte Carlo simulations demonstrate the approach's effectiveness with finite samples and its robustness to approximately sparse networks and heavy-tailed errors. Applications to global stock markets, S&P 500 sectoral indices, and commodity futures highlight the prevalence of sparse networks in empirical settings.

econ.EM↗