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on Market Microstructure |
| By: | Gustavo Fruet Dias (School of Economics, University of East Anglia); Fotis Papailias (King’s Business School, King’s College London); Cristina Scherrer (Department of Finance, London School of Economics) |
| Abstract: | We investigate information processing in the stochastic process driving stock’s volatility (volatility discovery). We apply fractionally cointegration techniques to de-compose the estimates of the market-speciï¬ c integrated variances into an estimate of the common integrated variance of the efficient price and a transitory component. The market weights on the common integrated variance of the efficient price are the volatility discovery measures. We relate the volatility discovery measure to the price discovery framework and formally show their roles on the identiï¬ cation of the inte-grated variance of the efficient price. We establish the limiting distribution of the volatility discovery measures by resorting to both long span and in-ï¬ ll asymptotics. The empirical application is in line with our theoretical results, as it reveals that trading venues incorporate new information into the stochastic volatility process in an individual manner and that the volatility discovery analysis identiï¬ es a distinct information process than that based on the price discovery analysis. |
| Keywords: | long memory, fractionally cointegrated vector autoregressive model, realized measures, market microstructure, price discovery, high-frequency data, double asymptotics |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:uea:ueaeco:2024-01&r=mst |
| By: | Michael J. Fleming; Isabel Krogh; Claire Nelson |
| Abstract: | A commonly used measure of market liquidity is market depth, which refers to the quantity of securities market participants are willing to buy or sell at particular prices. The market depth of U.S. Treasury securities, in particular, is assessed in many analyses of market functioning, including this Liberty Street Economics post on liquidity in 2023, this article on market functioning in March 2020, and this paper on liquidity after the Global Financial Crisis. In this post, we review the many measurement decisions that go into depth calculations and show that inferences about the evolution of Treasury market depth, and hence liquidity, are largely invariant with respect to these decisions. |
| Keywords: | Treasury securities; liquidity; order book |
| JEL: | G1 |
| Date: | 2024–02–12 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednls:97743&r=mst |
| By: | George Tzagkarakis (IRGO - Institut de Recherche en Gestion des Organisations - UB - Université de Bordeaux - Institut d'Administration des Entreprises (IAE) - Bordeaux); Frantz Maurer (Kedge BS - Kedge Business School, IRGO - Institut de Recherche en Gestion des Organisations - UB - Université de Bordeaux - Institut d'Administration des Entreprises (IAE) - Bordeaux); J.P. Nolan |
| Abstract: | Optimal sampling period selection for high-frequency data is at the core of financial instruments based on algorithmic trading. The unique features of such data, absent in data measured at lower frequencies, raise significant challenges to their statistical analysis and econometric modelling, especially in the case of heavy-tailed data exhibiting outliers and rare events much more frequently. To address this problem, this paper proposes a new methodology for optimal sampling period selection, which better adapts to heavy-tailed statistics of high-frequency financial data. In particular, the novel concept of the degree of impulsiveness (DoI) is introduced first based on alpha-stable distributions, as an alternative source of information for characterising a broad range of impulsive behaviours. Then, a DoI-based generalised volatility signature plot is defined, which is further employed for determining the optimal sampling period. The performance of our method is evaluated in the case of risk quantification for high-frequency indexes, demonstrating a significantly improved accuracy when compared against the well-established volatility-based approach. © 2023, The Author(s). |
| Keywords: | High-frequency indexes, Alpha-stable models, Degree of impulsiveness, Optimal sampling period |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04425500&r=mst |