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Measuring Immediate Price Impact Using Nonparametric Models

As a consequence of recent technological advances and the proliferation of high-frequency trading and other forms of algorithmic trading, the cost of trading in financial markets has irrevocably changed. One important change relates to how trading affects prices; this is known as price impact. Understanding price impact is vital as it helps in evaluating different trading strategies and hence leads to optimal execution strategies that minimize trading costs. Besides evaluating established parametric price impact models in the literature, this paper proposes a novel nonparametric approach, known as Generalized Additive Models, to estimate price impact. This paper provides the first empirical analysis of the performance of different immediate price impact models for individual trades using out-of-sample predictions. The study finds that the nonparametric model outperforms all other models both in- and out-of-sample. The outperformance comes from (1) the appropriate price-impact normalization, (2) the greater data-fitting flexibility inherent in nonparametric frameworks, and (3) the incorporation of new explanatory variables which cannot easily be accommodated analytically.