Demand deposits modeling

Abstract

Demand deposits modeling is of top importance for banking institutions and usually represents a large part of a bank portfolio. Even though these products seem rather simple at first glance, demand deposits are without a fixed maturity, generating uncertainties in the model. A significant amount of academic literature on this subject is available. However, we are experiencing negative interest rates for a few years now, that may have affected customer behavior and have led to an excess in deposits-taking for a large majority of bank. In addition to the low-rate environment, this small European bank has been growing rapidly in recent years and new customers have different characteristics from the old ones. Demand deposits modeling is therefore a major challenge for the bank. It can be divided in three steps, respectively the market rates, the deposit volumes and the deposit rates, however, only the market rates and the deposit volumes will be considered in this thesis. The dynamics of market rate follow a single factor Hull-White model. The mean-reverting parameter and the volatility are calibrated on historical data of the one-month Euribor rates, and then simulated using an exact Monte-Carlo approach. The deposit volume model is based on an Ornstein-Uhlenbeck process, where the constant drift term μ is replaced by a trend that depends on the rates' level. The trend has a different slope whether we are in a normal-rate environment or in a low-rate environment, and the probability of being in either state depends on the market rates. We create age and wealth categories for customer segmentation purpose. We then perform a cluster analysis, either using a k-means method or a hierarchical clustering algorithm, that will be included in the deposit volume model. The clustering forms similar groups of customers, reflecting better customers' diversity. Lastly, we compare two output variables, the average life and the optionality, for different simulations, one without the clustering, and two with the clustering. The best-case scenario for any bank being a high average life associated with a low optionality with regards to demand deposits modeling, the clustering integration in the model leads to more optimal results.