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on Market Microstructure |
| By: | Axel Groß-Klußmann; Nikolaus Hautsch |
| Abstract: | We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of bid-ask spreads like the strong autocorrelation and discreteness of observations. We discuss theoretical properties of LMACP models and evaluate rolling window forecasts of quoted bid-ask spreads for stocks traded at NYSE and NASDAQ. We show that Poisson time series models significantly outperform forecasts from ARMA, ARFIMA, ACD and FIACD models. The economic significance of our results is supported by the evaluation of a trade schedule. Scheduling trades according to spread forecasts we realize cost savings of up to 13 % of spread transaction costs. |
| Keywords: | Bid-ask spreads, forecasting, high-frequency data, stock market liquidity, count data time series, long memory Poisson autoregression |
| JEL: | G14 C32 |
| Date: | 2011–07 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2011-044&r=mst |
| By: | Takashi Kato |
| Abstract: | We study the optimal execution problem in the presence of market impact and give a generalization of the main result of Kato(2009). Then we consider an example where the security price follows a geometric Ornstein-Uhlenbeck process which has the so-called mean-reverting property, and then show that an optimal strategy is a mixture of initial/terminal block liquidation and intermediate gradual liquidation. When the security price has no volatility, the form of our optimal strategy is the same as results of Obizhaeva and Wang(2005) and Alfonsi et al.(2010), who studied the optimal execution in a limit-order-book model. |
| Date: | 2011–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1107.1787&r=mst |
| By: | Patton, Andrew J; Ramadorai, Tarun |
| Abstract: | We propose a new method to model hedge fund risk exposures using relatively high frequency conditioning variables. In a large sample of funds, we find substantial evidence that hedge fund risk exposures vary across and within months, and that capturing within-month variation is more important for hedge funds than for mutual funds. We consider different within-month functional forms, and uncover patterns such as day-of-the-month variation in risk exposures. We also find that changes in portfolio allocations, rather than changes in the risk exposures of the underlying assets, are the main drivers of hedge funds' risk exposure variation. |
| Keywords: | beta; hedge funds; mutual funds; performance evaluation; time-varying risk; window-dressing |
| JEL: | C22 G11 G23 |
| Date: | 2011–07 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:8479&r=mst |