Market Microstructure And Automatic Market Making Models : An Empirical Study of Eurodollar futures on the GLOBEX exchange

The core objective of this paper is to quantitatively model the market making function, develop an autonomous application which applies those models to a real financial market, and test this application on a electronic market for Eurodollars. The Eurodollar Futures market is one of the most liquid financial instruments traded in the world today. The contract trades both electronically, on GLOBEX, and through manual trading in the Chicago Mercanti le Exchange Eurodollar pit. During exchange hours, the volume and liquidity on the GLOBEX change is small and poor. After the CME exchange closes, the GLOBEX market becomes quite active and liquid. The initial target of the automated market making application is to make a market in Eurodollar futures on the GLOBEX exchange during CME hours. This initial target has the advantages of access to the CME pit for both current market prices and liquidity and the ability to accurately simulate a market at low risk. This paper presents trading performance from four automatic market making models we construct and test results and analysis from 3 testbeds: Eurodollar Futures market, S&P market, and a simulated ex-ante market. HIGH-FREQUENCY DATA RESEARCH recently becomes a center topic for practitioners and researchers, who are interested in trading strategies, trading systems, performance measures, data mining, pricing analytics, risk-hedging engines, and other related fields. Research composes of three major steps: 1. Exploring on high-frequency data for fundamental statistical properties, such as “stylized facts” . 2. Formulating models by util izing empirical facts. 3. Veri fying whether those models satisfactorily reproduce the stylized facts found in the data. Along with trading frequencies and demands for traditional financial instruments and new structured products worldwide increase, investment banks are nowadays facing challenges in improving trading performance and trading systems. Therefore, an empirical study and test on automatic/electronic market making models can be fairly valuable and able to answer questions such as “Does automatic market making bring liquidity and fair quoted prices to the markets? ” “Does automatic market making accurately simulate the market at low cost? ” “Does automatic market making well manage the inventory level, shortterm and long-term profit/loss (P&L), and mark-to-market? ” In our paper, we build two simple “toy” models and two real market making models to answer questions above by studying and comparing their performance measures. If spread as 1 jl995@cims.nyu. edu 2 sy352@cims.nyu. edu 3 johan_liljefors@aigtrading.com 4 igor.desyatnikov@citigroup.com 5 john.Stevenson@jpmorgan.com an initial parameter is required in a model, we use spread= 2 cov − , where “cov” is the first-order serial covariance of mark-to-market. Roll (1984) proved this relationship between spread and auto-covariance of the first order serial covariance of price changes. Then we test our models with Eurodollar Futures and S&P market data provided by JP Morgan Chase and study performance measures of each model. In addition, we analyze the feasibility of testing a simulated exponential price generating process. Based on test results, we provide two potential techniques to improve performances of Market Making Models. The quality of market data, which can be critical in a high-frequency data research, is assumed to be clean, i.e. no missing data, no needs for filtering data process and changing in time scale for performance measures. The paper is organized as follows. Section I outlines the evolution of the market making models and introduced several influential models, including the most recent Adaptive model. It also provides a summary of our extensive journal reviews on Market Making Models, trading strategies, and high-frequency data. This initial research review step is indeed very important for carrying out testable models. We search for reliable mathematical equations on market making models and then modify those mathematical models, if necessary. It also initializes several creative ideas about market making algorithms we would like to implement on our models. Section II introduces main concepts on Reinforcement Learning, which is the fundamental block of the Adaptive Model. Section III presents our analysis on Eurodollar Futures market data, S&P market data, and simulated exponential price generating test-bed. Section IV and V show the detailed description and algorithm of our models. Section VI represents the market structure and test-beds studied in our paper. Section VII details the definition of performance measures we use in our paper and how they indicate the performance of our models. Section VIII presents the performance measure results and analysis in comparing one model to another. Section IX suggests two inventory control techniques for Market Making Models. Section X documents our codes. Section XI is our conclusion and several potential studies in the future, and other techniques to improve our models. Section XII lists references we quote in our paper.