Using 9283 stocks listed on the NYSE, AMEX, and NASDAQ, we document return reversals and show that overnight jump returns negatively predict short-term returns in both positive and negative episodes. This relationship remains robust across subperiods, including times of market distress, and across quintile portfolios formed on firm-specific factors. Although a frictionless long–short strategy following jump days generates 1.05% positive abnormal returns with the magnitude declining as the holding period expands, the same strategy based on extreme deciles of lagged overnight jump returns yields a 0.6% risk-adjusted loss over a one-month horizon, implying that the overreaction is transient. Overall, the findings suggest that price discovery is contingent on an active trading environment as well, and reversal patterns following overnight jumps are consistent with temporary mispricing that tends to diminish once continuous trading resumes.

Intraday correlation dynamics poses challenges to financial econometricians, especially in recently popular high frequency domain, due to non-synchronous trading and market microstructure noise. Traditional models fail to address the issues inherent to the nature of the data, which is riddled with noisy signals and missing values. We employ a recently developed method based on Generalized Autoregressive Score (GAS) framework and State-Space Modeling to remedy these characteristics of high frequency data and estimate intraday correlations in Turkish equity market Borsa Istanbul. Our findings reveal that average intraday conditional correlation rises as trading commences and lingers around certain altitude for some time, with the eigenvalues associated with market factor becoming progressively more dominant. An upward trend closes out the trading day on Mondays, which we attribute to the US market opening, whereas the rest of the week does not show a generalizable closing-time effect. Assessment of the findings across different market conditions and days of the week reveals elevated correlation levels in volatile markets as well as a distinguishable path for the beginning of the week. Beyond the scholarly contribution, the methodology can be used as a nowcasting tool and the findings are of interest to various parties like high-frequency traders, risk and portfolio managers and regulatory agencies in formulating their high frequency trading practices, hedging, portfolio construction schemes and margin requirements, respectively.