Modelling and forecasting liquidity supply using semiparametric factor dynamics

Abstract

We model the dynamics of ask and bid curves in a limit order book market using a dynamic semiparametric factor model. The shape of the curves is captured by a factor structure which is estimated nonparametrically. Corresponding factor loadings are modelled jointly with best bid and best ask quotes using a vector error correction specification. Applying the framework to four stocks traded at the Australian Stock Exchange (ASX) in 2002, we show that the suggested model captures the spatial and temporal dependencies of the limit order book. We find spill-over effects between both sides of the market and provide evidence for short-term quote predictability. Relating the shape of the curves to variables reflecting the current state of the market, we show that the recent liquidity demand has the strongest impact. In an extensive forecasting analysis we show that the model is successful in forecasting the liquidity supply over various time horizons during a trading day. Moreover, it is shown that the model's forecasting power can be used to improve optimal order execution strategies. ► The Dynamic Semiparametric Factor Model successfully models liquidity supply. ► The spatial structure of the limit order book can be explained by two factors. ► Factor loadings and quote dynamics follow a vector error correction specification. ► Our method outperforms a naive benchmark approach. ► Intra-day order execution strategies can be improved.