Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

SFB649DP2014 035

Adaptive Order Flow Forecasting with Multiplicative Error Models

Wolfgang K. Härdle
Andrija Mihoci
Christopher Hian-Ann Ting

A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e., the buyer and the seller-initiated trading volumes and the order flow dynamics. Analysing order flow series and its information content of mini Nikkei 225 index futures traded at the Osaka Securities Exchange in 2012 and 2013, a data-driven optimal length of local windows up to approximately 1-2 hours is reasonable to capture parameter variations and is suitable for short-term prediction. Our proposed trading strategies achieve statistical arbitrage opportunities and are therefore beneficial for quantitative finance practice.

multiplicative error models, trading volume, order flow, forecasting

JEL Classification:
C41, C51, C53, G12, G17