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on Market Microstructure |
| By: | Sait R. Ozturk (Erasmus University Rotterdam, the Netherlands); Michel van der Wel (Erasmus University Rotterdam, the Netherlands); Dick van Dijk (Erasmus University Rotterdam, the Netherlands) |
| Abstract: | We study why a majority of trades still happen during the pit hours, i.e. when the trading pit is open, even after the pit ceased to be a liquid and informative venue. We investigate the case of 30-year U.S. Treasury futures using a ten-years-long intraday data set which contains the introduction of the CME Globex platform as an example of sophistication in electronic trading. We use a structural model to estimate the time-variation in potential factors of the clustering of trading activity around the pit hours, namely price informativeness, information asymmetry and price impact of trades. We find evidence for a feedback mechanism between trading activity and these factors. Across the sample period, price informativeness during the afterhours is a consistently significant factor attracting trade activity. Information asymmetry has a negative effect on afterhours act ivity, particularly during the crisis years. The negative effect of price impact on afterhours activity ceases to be a significant factor from 2007 on, possibly due to improvements in order execution algorithms and electronic trading facilities. |
| Keywords: | Afterhours Trading; Market microstructure; Kalman filter |
| JEL: | C32 G14 |
| Date: | 2015–07–07 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20150082&r=mst |
| By: | Siem Jan Koopman (VU University Amsterdam); Rutger Lit (VU University Amsterdam); Andre Lucas (VU University Amsterdam) |
| Abstract: | We introduce a dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete stock price changes. The likelihood function for our model is analytically intractable and requires Monte Carlo integration methods for its numerical evaluation. The proposed methodology is applied to tick-by-tick data of four stocks traded on the New York Stock Exchange. We require fast simulation methods for likelihood evaluation since the number of observations per series per day varies from 1000 to 10,000. Complexities in the intraday dynamics of volatility and in the frequency of trades without price impact require further non-trivial adjustments to the dynamic Skellam model. In-sample residual diagnostics and goodness-of-fit statistics show that the final model provides a good fit to the data. An extensive forecasting study of intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling approaches. |
| Keywords: | non-Gaussian time series models; volatility models; importance sampling; numerical integration; high-frequency data; discrete price changes. |
| JEL: | C22 C32 C58 |
| Date: | 2015–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20150076&r=mst |
| By: | aniko oery (Yale); Andrzej Skrzypacz (Stanford University); William Fuchs (University of California - Berkeley) |
| Abstract: | We analyze price transparency in a dynamic market with private information and correlated values. Uninformed buyers compete inter- and intra-temporarily for a good that is sold by an informed seller suering a liquidity shock. We contrast public versus pri- vate price oers and show that equilibria coincide only if oers are infrequent. All equilibria with private oers Pareto-dominate the equilibrium with public oers. If not trading by a deadline im- poses an eciency loss, public oers induce a market breakdown for some time before the deadline; in contrast, trade never stops with private oers, creating a further benet of opacity. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed015:73&r=mst |