COMPONENTS OF THE BID–ASK SPREAD AND VARIANCE:AUNIFIED APPROACH BJ € ORN HAGSTR € OMER, RICHARD HENRICSSON and LARS L. NORD  EN* We develop a structural model for the price formation and liquidity supply of an asset. Our model facilitates decompositions of both the bid–ask spread and the return variance into components related to adverse selection, inventory, and order processing costs. Furthermore, the model shows how the fragmentation of trading volume across trading venues influences inventory pressure and price discovery. We use the model to analyze intraday price formation for gold futures traded at the Shanghai Futures Exchange. We find that order processing costs explain about 50% of the futures bid–ask spread, whereas the remaining 50% is equally due to asymmetric information and to inventory costs. About a third of the variance in futures returns is attributable to microstructure noise. Trading at the spot market has a significant influence on futures price discovery, but only a limited impact on the futures bid–ask spread. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 36:545–563, 2016 1. INTRODUCTION Understanding of market microstructure and its influence on prices and market quality is more important than ever. The last two decades have seen securities exchanges worldwide develop from being national monopolies with quote-driven trading systems to global competitive businesses with order-driven pricing mechanisms. Technological development, competition, and legislation have led to trading and quoting at frequencies unimaginable before, opening for business models in the millisecond domain spanning multiple trading platforms. When the human eye is no longer able to monitor the markets in real time, the need to understand the principles for how market microstructure influences the strategic behavior of traders (both humans and algorithms) becomes increasingly important to investors and regulators alike. In a seminal paper, Madhavan, Richardson, and Roomans (henceforth MRR; 1997) develop a theoretical model that provides such understanding. 1 Bj€ orn Hagstr€ omer and Lars L. Nord en are at Stockholm Business School, Stockholm University, Sweden. Richard Henricsson is at Swedbank, Stockholm, Sweden. We thank Angelo Aspris, J er^ ome Dugast, Bob Webb (the editor), and participants at the Arne Ryde Workshop in Financial Economics (Lund, 2012), the Frontiers of Finance Conference (Warwick, 2012), the Derivative Markets Conference (Auckland, 2015), and seminar participants at Stockholm University (2012) for their comments and help. All authors are grateful to the Jan Wallander and Tom Hedelius foundation and the Tore Browaldh foundation for research support. JEL Classification: G10, G12, G13, G14 *Correspondence author, Stockholm Business School, Stockholm University, S-106 91 Stockholm, Sweden. Tel: þ46-8-6747139, Fax: þ46-8-6747440, e-mail: ln@sbs.su.se Received November 2015; Accepted November 2015 1 The model in MRR (1997) is similar to the one developed by Huang and Stoll (1997), where the latter is slightly more general than the former. Related alternatives are models by Glosten and Harris (1988) and Sadka (2006). See Kim and Murphy (2013) for a comparative analysis of these four model alternatives. The Journal of Futures Markets, Vol. 36, No. 6, 545–563 (2016) © 2016 Wiley Periodicals, Inc. Published online 15 February 2016 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/fut.21776