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on Market Microstructure |
| By: | Lorenzo Dall’amico (LadHyX - Laboratoire d'hydrodynamique - CNRS - Centre National de la Recherche Scientifique - X - École polytechnique); Antoine Fosset (LadHyX - Laboratoire d'hydrodynamique - CNRS - Centre National de la Recherche Scientifique - X - École polytechnique, CFM - Capital Fund Management - Capital Fund Management); Jean-Philippe Bouchaud (CFM - Capital Fund Management - Capital Fund Management); Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - CNRS - Centre National de la Recherche Scientifique - X - École polytechnique, CFM - Capital Fund Management - Capital Fund Management) |
| Abstract: | Latent order book models have allowed for significant progress in our understanding of price formation in financial markets. In particular they are able to reproduce a number of stylized facts, such as the square-root impact law. An important question that is raised-if one is to bring such models closer to real market data-is that of the connection between the latent (unobservable) order book and the real (observable) order book. Here we suggest a simple, consistent mechanism for the revelation of latent liquidity that allows for quantitative estimation of the latent order book from real market data. We successfully confront our results to real order book data for over a hundred assets and discuss market stability. One of our key theoretical results is the existence of a market instability threshold, where the conversion of latent order becomes too slow, inducing liquidity crises. Finally we compute the price impact of a metaorder in different parameter regimes. |
| Date: | 2019–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02283821&r=all |
| By: | Can Yilmaz Altinigne; Harun Ozkan; Veli Can Kupeli; Zehra Cataltepe |
| Abstract: | Order book dynamics play an important role in both execution time and price formation of orders in an exchange market. In this study, we aim to model the limit order arrival rates in the vicinity of the best bid and the best ask price levels. We use limit order book data for Garanti Bank, which is one of the most traded stocks in Borsa Istanbul. In order to model the daily, weekly, and monthly arrival of limit order quantities, three different discrete probability distributions are tested: Geometric, Beta-Binomial and Discrete Weibull. Additionally, two theoretical models, namely, Exponential and Power law are also tested. We aim to model the arrival rates in the first fifteen bid and ask price levels. We use L1 norms in order to calculate the goodness-of-fit statistics. Furthermore, we examine the structure of weekly and monthly mean cancellation rates in the first ten bid and ask price levels. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.08308&r=all |
| By: | Chloe Tergiman (Penn State - Pennsylvania State University - Penn State System); Marie Claire Villeval (GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique - ENS Lyon - École normale supérieure - Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet [Saint-Étienne] - Université de Lyon - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | In a finitely repeated game with asymmetric information, we experimentally study how reputation and standard market mechanisms change the nature of fraudulent announcements by experts. While some lies can be detected ex post by investors, other lies remain deniable. Lying behavior suggests that individuals care more about the consequences of being caught, rather than the act of lying per se. Allowing for reputation reduces the frequency of lies that can be detected but has no impact on deniable lies: individuals simply hide their lies better and fraud persists. Competition without reputation increases risky lies and never protects investment. |
| Keywords: | Dishonesty,Reputation,Competition,Financial Markets |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-02292040&r=all |
| By: | Dmitry Zotikov; Anton Antonov |
| Abstract: | We propose a method for detection and prediction of native and synthetic iceberg orders on Chicago Mercantile Exchange. Native (managed by the exchange) icebergs are detected using discrepancies between the resting volume of an order and the actual trade size as indicated by trade summary messages, as well as by tracking order modifications that follow trade events. Synthetic (managed by market participants) icebergs are detected by observing limit orders arriving within a short time frame after a trade. The obtained icebergs are then used to train a model based on the Kaplan--Meier estimator, accounting for orders that were cancelled after a partial execution. The model is utilized to predict the total size of newly detected icebergs. Out of sample validation is performed on the full order depth data, performance metrics and quantitative estimates of hidden volume are presented. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.09495&r=all |