Trading large volumes of a nancial asset in order driven markets requires the use of algorithmic execution dividing the volume into many transactions in order to minimize costs due to market impact. A proper design of an optimal execution strategy strongly depends on a careful modeling of market impact, i.e. how the price reacts to trades. In this paper we consider a recently introduced market impact model (Bouchaud et al 2004 Quant. Finance, 4 176{90), which has the property of describing both the volume and the temporal dependence of price change due to trading. We show how this model can be used to describe price impact also in aggregated trade time or in real time. We then solve analytically and calibrate with real data the optimal execution problem both for risk neutral and for risk averse investors and we derive an e cient frontier of optimal execution. When we include spread costs the problem must be solved numerically and we show that the introduction of such costs regularizes the solution.
Busseti, E., Lillo, F. (2012). Calibration of optimal execution of financial transactions in the presence of transient market impact. JOURNAL OF STATISTICAL MECHANICS: THEORY AND EXPERIMENT, 2012, P09010 [10.1088/1742-5468/2012/09/P09010].
Calibration of optimal execution of financial transactions in the presence of transient market impact
LILLO, Fabrizio
2012-01-01
Abstract
Trading large volumes of a nancial asset in order driven markets requires the use of algorithmic execution dividing the volume into many transactions in order to minimize costs due to market impact. A proper design of an optimal execution strategy strongly depends on a careful modeling of market impact, i.e. how the price reacts to trades. In this paper we consider a recently introduced market impact model (Bouchaud et al 2004 Quant. Finance, 4 176{90), which has the property of describing both the volume and the temporal dependence of price change due to trading. We show how this model can be used to describe price impact also in aggregated trade time or in real time. We then solve analytically and calibrate with real data the optimal execution problem both for risk neutral and for risk averse investors and we derive an e cient frontier of optimal execution. When we include spread costs the problem must be solved numerically and we show that the introduction of such costs regularizes the solution.File | Dimensione | Formato | |
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