Clustering in foreign exchange markets : price, trades and traders.

Summary Using unpublished high-frequency data, this thesis studies three types of clustering present in the foreign exchange market: the concentration of orders on certain prices, the concentration of transactions over time and the existence of groups of investors making the same decisions. We start by studying the statistical properties of the EBS order book for the EUR/USD and USD/JPY currency pairs and the impact of a reduction in tick size on its dynamics. A large proportion of limit orders are still placed on the old authorized prices, leading to the appearance of barrier prices, where the best limits appear most of the time. This congestion effect can be seen in the average shape of the book where peaks are present at full distances. We show that this concentration of prices is caused by manual traders who refuse to use the new price resolution. We then raise the question of the ability of Hawkes processes to capture market dynamics. We analyze the accuracy of such processes as the calibration interval is increased. Different kernels constructed from sums of exponentials are systematically compared. The FX market that never closes is particularly suitable for our purpose, as it avoids the complications due to the nightly closing of equity markets. We find that the modeling is valid according to the three statistical tests, if a two-exponential kernel is used to fit one hour, and two or three for a full day. Over longer periods the model is systematically rejected by the tests because of the non-stationarity of the endogenous process. The estimated self-excitation time scales are relatively short and the endogeneity factor is high but subcritical around 0.8. Most agent-based models implicitly assume that agents interact through asset prices and trading volumes. Some explicitly use a network of interaction between traders, on which rumors are propagated, while others use a network that represents groups making common decisions. Unlike other types of data, such networks, if they exist at all, are necessarily implicit, which makes their detection complicated. We study the transactions of customers of two liquidity providers over several years. Assuming that the links between agents are determined by the timing of their activity or inactivity, we show that interaction networks exist. Moreover, we find that the activity of some agents systematically leads to the activity of other agents, thus defining lead-lag relationships between agents. This implies that the flow of customers is predictable, which we verify using a sophisticated statistical learning method.