|
on Market Microstructure |
| By: | Ioane Muni Toke |
| Abstract: | It has been suggested that marked point processes might be good candidates for the modeling of financial high-frequency data. A special class of point processes, Hawkes processes, has been the subject of various investigations in the financial community. In this paper, we propose to enhance a basic order book simulator with limit and market orders arrival times following mutually (unsymmetrically) exciting Hawkes processes. Modeling is based on empirical observations on interval times between orders that we verify on several markets (equity, bond futures, index futures). We show that this simple feature enables a much more realistic treatment of the bid-ask spread of the simulated order book. |
| Date: | 2010–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1003.3796&r=mst |
| By: | Gabriella Vaglica; Fabrizio Lillo; Rosario N. Mantegna |
| Abstract: | Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders we fit hidden Markov models to the time series of the sign of the tick by tick inventory variation of market members of the Spanish Stock Exchange. Our methodology probabilistically detects trading sequences, which are characterized by a net majority of buy or sell transactions. We interpret these patches of sequential buying or selling transactions as proxies of the traded hidden orders. We find that the time, volume and number of transactions size distributions of these patches are fat tailed. Long patches are characterized by a high fraction of market orders and a low participation rate, while short patches have a large fraction of limit orders and a high participation rate. We observe the existence of a buy-sell asymmetry in the number, average length, average fraction of market orders and average participation rate of the detected patches. The detected asymmetry is clearly depending on the local market trend. We also compare the hidden Markov models patches with those obtained with the segmentation method used in Vaglica {\it et al.} (2008) and we conclude that the former ones can be interpreted as a partition of the latter ones. |
| Date: | 2010–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1003.2981&r=mst |
| By: | Zhi-Qiang Jiang (ECUST); Wei-Xing Zhou (ECUST) |
| Abstract: | We provide an empirical investigation aimed at uncovering the statistical properties of intricate stock trading networks based on the order flow data of a highly liquid stock (Shenzhen Development Bank) listed on Shenzhen Stock Exchange during the whole year of 2003. By reconstructing the limit order book, we can extract detailed information of each executed order for each trading day and demonstrate that the trade size distributions for different trading days exhibit power-law tails and that most of the estimated power-law exponents are well within the L{\'e}vy stable regime. Based on the records of order matching among investors, we can construct a stock trading network for each trading day, in which the investors are mapped into nodes and each transaction is translated as a direct edge from the seller to the buyer with the trade size as its weight. We find that all the trading networks comprise a giant component and have power-law degree distributions and disassortative architectures. In particular, the degrees are correlated with order sizes by a power-law function. By regarding the size executed order as its fitness, the fitness model can reproduce the empirical power-law degree distribution. |
| Date: | 2010–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1003.2459&r=mst |