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on Market Microstructure |
| By: | Antonio Mele |
| Abstract: | In a market with informationally connected traders, the dynamics of volume, price informativeness, price volatility, and liquidity are severely affected by the information linkages every trader experiences with his peers. We show that in the presence of information linkages among traders, volume and price informativeness increase. Moreover, we find that information linkages improve or damage market depth, and lower or boost the traders' profits, according to whether these linkages convey positively or negatively correlated signals. Finally, our model predicts patterns of trade correlation consistent with those identified in the empirical literature: trades generated by neighbor traders are positively correlated and trades generated by distant traders are negatively correlated. |
| Date: | 2008–10 |
| URL: | http://d.repec.org/n?u=RePEc:fmg:fmgdps:dp620&r=mst |
| By: | Ying Chen; Wolfgang Härdle; Uta Pigorsch |
| Abstract: | With the recent availability of high-frequency Financial data the long range dependence of volatility regained researchers' interest and has lead to the consideration of long memory models for realized volatility. The long range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as e.g. one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory phenomena point towards short memory models of volatility with nonstationarities, such as structural breaks or regime switches, that spuriously generate a long memory pattern (see e.g. Diebold and Inoue, 2001; Mikosch and Starica, 2004b). In this paper we adopt this view on the dependence structure of volatility and propose a localized procedure for modeling realized volatility. That is at each point in time we determine a past interval over which volatility is approximated by a local linear process. Using S&P500 data we find that our local approach outperforms long memory type models in terms of predictability. |
| Date: | 2009–01 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2009-003&r=mst |
| By: | Laib, Fodil; Radjef, MS |
| Abstract: | The aim of this work is to show how automated traders can operate a futures market. First, we established some hypothesises on the properties of the ’correct’ price pattern which translates accurately the underlying moves in the supply/demand balance and the nominal price, then mathematical measures were derived allowing to estimate the efficiency of a given trading strategy. As a starting step, we applied our approach to a simplified market setup where only two automated traders, a producer and a consumer, can trade. They receive a stream of forecasts on supply and demand levels and they should react instantaneously by adjusting these forecasts, then issuing sale and buy orders. Later, we suggested a parameterized trading strategy for the two automatons. Finally, we obtained by simulation the optimal parameters of this strategy in some particular cases. |
| Keywords: | Automated traders; optimal strategies; agent based |
| JEL: | C02 D40 C73 |
| Date: | 2008–05–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:12965&r=mst |