Intra-daily Volume Modeling and Prediction for Algorithmic Trading

Relatore:  Giampiero Gallo
  martedì 15 settembre 2009 alle ore 12.30 Biblioteca DSE, Palazzina 32 - Ex Caserma Passalacqua


Abstract: The explosion of algorithmic trading has been one of the most prominent recent trends in the financial industry. Algorithmic trading consists of automated trading strategies that attempt to minimize transaction costs by optimally placing orders. The key ingredient of many of these strategies are intra-daily volume proportions forecasts. This work proposes a dynamic model for intra-daily volumes that captures salient features of the series such as time series dependence, intra-daily periodicity and volume asymmetry. Moreover, we introduce a loss functions for the evaluation of proportions forecasts which retains both an operational and information theoretic interpretation. An empirical application on a set of widely traded index ETFs shows that the proposed methodology is able to significantly outperform common forecasting methods and delivers significantly more precise predictions for VWAP trading.

Angelo Zago

Data pubblicazione
8 settembre 2009