Title: An Agent-Based Approach to Modelling the High-Frequency Foreign Exchange Market
Speaker: Dr. Monira Aloud, Assistant Professor, MIS Department, KSU
In this seminar, we present a project that used an agent-based modelling (ABM) approach to model the trading activity in the Foreign Exchange (FX) market which is the most liquid financial market in the world. We first establish the statistical properties (stylized facts) of the trading activity in the FX market using a unique high frequency dataset of anonymised individual traders’ historical transactions on an account level, spanning 2.25 years. To the best of our knowledge, this dataset is the biggest available high-frequency dataset of individual FX market traders’ historical transactions. We then construct an agent-based FX market (ABFXM) which features a number of distinguishing elements including zero-intelligence directionalchange event (ZI-DCT0) trading agents and asynchronous trading-time windows. The individual agents are characterised by different levels of wealth, trading time windows, different profit objectives and risk appetites and initial activation conditions. Using the identified stylized facts as a benchmark, we evaluate the trading activity reproduced from the ABFXM and we establish that this resembles to a satisfactory
level the trading activity of the real FX market.
In the course of this project, we studied in depth the constructed ABFXM. We focus on performing a systematic exploration of the constituent elements of the ABFXM and their impact on the dynamics of the FX market behaviour. In particular, our study explores and identifies the essential elements under which the stylized facts of the FX market trading activity are exhibited in the ABFXM. Our study suggests that the key elements are the ZI-DCT0 agents, heterogeneity which has been embedded in our model in different ways, asynchronous trading time windows, initial activation conditions and the generation of limit orders. We also show that the dynamics of the market trading activity depend on the number of agents one considers.
We explore the emergence of the stylized facts in the trading activity when the ABFXM is populated with agents with three different strategies: a variation of the zero-intelligence with a constraint (ZI-CV) strategy; the ZI-DCT0 strategy; and a genetic programming-based (GP) strategy. Our results show that the ZI-DCT0 agents best reproduce and explain the stylized facts observed in the FX market transactions data. Our study suggests that some the observed stylized facts could be the result of introducing a threshold which triggers the agents to respond to fixed periodic patterns in the price time series.
Location: Research Center's Auditorium in Building 2
Date: Nov 26, 2013