We propose a unique measure of information share based on the factor modeling of the price innovations, which we refer to as the Factor Information Share (FIS). We show that the proposed FIS is improved over two widely used measures by providing meaningful rationale and unique identifiability. Our simulation study suggests that FIS also leads to more accurate estimates of the market-specific contribution in the process of price discovery. The empirical results include both static and dynamic FIS of cross-listed Chinese banks traded on A-shares and H-shares. By incorporating the news sentiment, we find that positive news has a larger influence on A-shares’ FIS than negative news.

Recent research argues that if the cause of confusion between long memory and regime switching were properly controlled for, they could be effectively distinguished. Motivated by this idea, our study aims to distinguish between them of financial series. We firstly model long memory and regime switching via the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Markov Regime-Switching (MRS) models, respectively. Their finite-sample properties and the confusion are investigated via simulations. To control for the cause of this confusion, we propose the MRS–ARFIMA model. A Monte Carlo study shows that this framework can effectively distinguish between the pure ARFIMA and pure MRS processes. Furthermore, MRS–ARFIMA outperforms the ordinary ARFIMA model for data simulated from the MRS–ARFIMA process. Finally, empirical studies of hourly and five-minute Garman–Klass and realized volatility of the FTSE index is conducted to demonstrate the advantages and usefulness of the proposed MRS–ARFIMA framework compared with the ARFIMA and MRS models in practice.

We propose a new measure of price discovery, New Leadership Share (NLS), that attributes permanent information flow to individual markets using a uniquely identified structural moving average model. NLS quantifies each market’s contribution to permanent price innovations as a proportion of total informational leadership and offers key technical advantages, including uniqueness and adherence to standard statistical asymptotics. We derive closed-form solutions and analytical standard errors for bivariate markets and provide a framework that extends naturally to multiple markets without the variable ordering problem. Simulation results show that NLS consistently outperforms three widely used benchmarks. Empirical analysis of 2023 data finds that exchange-traded funds and front-month futures markets share equal leadership relative to the S&P 500 spot index.