Ronald Heijmans

How to Measure the Unsecured Money Market? The Eurosystem's Implementation and Validation Using TARGET2 Data

This paper develops a methodology, based on Furfine (1999), to identify unsecured interbank money-market loans from transaction data of the most important euro processing payment system, TARGET2, for maturity ranging from one day (overnight) up to one year. The implementation has been verified with (i) interbank money-market transactions executed on the Italian trading platform e-MID and (ii) individual reporting by the EONIA panel banks. The type 2 (false negative) error for the best performing algorithm setup is equal to 0.92 percent. The different stages of the global financial crisis and of the sovereign debt crises are clearly visible in the interbank money market, characterized by significant drops in the turnover. We find aggregated interest rates very close to EONIA but we observe high heterogeneity across countries and market participants.

Monitoring the Unsecured Interbank Money Market Using Target2 Data

We investigate the euro unsecured interbank money market during the current financial crisis. To identify the loans traded in this market and settled in TARGET2, we extend the algorithm developed by Furfine (1999) and adapt it to the European interbank loan market with maturity up to one year. This paper solves the problem of systematic errors which occur when you only look at overnight loans (as the Furfine algorithm does). These errors especially occur in times of (very) low interest rates. The algorithm allows us to track the actual interest rates rather than quoted interest rates on liquidity trading by participants of the Dutch part of the euro large value payment system (TARGET2-NL). The algorithm enables us to constitute the Dutch part of the EONIA, making it possible to compare the interest rates developments in the Dutch market to the European average ones. Based on the new algorithm, we develop a policy tool to monitor the interbank money market, both at macro level (whole market) and individual bank level (Money Market Monitoring Dashboard).

Disruptions in large value payment systems: An experimental approach

This experimental study investigates the behaviour of banks in a large value payment system. More specifically, we look at 1) the reactions of banks to disruptions in the payment system, 2) the way in which the history of disruptions affects the behaviour of banks (path dependency) and 3) the effect of more concentration in the payment system (heterogeneous market versus a homogeneous market). The game used in this experiment is a stylized version of a model of Bech and Garrett (2006) in which each bank can choose between paying in the morning (efficient) or in the afternoon (inefficient). The results show that there is significant path dependency in terms of disruption history. Also the level of disruption influences the behaviour of the participants. Once the system has moved to the inefficient equilibrium, it does not easily move back to the efficient equilibrium. Furthermore, there is a clear leadership effect in the heterogeneous market.

Dynamic visualization of large transaction networks: the daily Dutch overnight money market

This paper shows how large data sets can be visualized in a dynamic way to support exploratory research, highlight econometric results or provide early warning information. The case studies included in this paper case are based on the payments and unsecured money market transaction data of the Dutch part of the Eurosystems large value payment system, TARGET2. We show how animation facilitates analysis at three different levels. First, animation shows how the market macrostructure develops. Second, it enables individual banks that are of interest to be followed. Finally, it facilitates a comparison of the same market at different moments in time and of different markets (such as countries) at the same moment in time.

Determinants of the Rate of the Dutch Unsecured Overnight Money Market

This paper investigates how changes in the monetary policy framework have affected the overnight money market lending rate for the Dutch segment of the euro area during tranquil and crisis times. We present an EGARCH model on the volatility of the overnight lending rate. The results show that modifications of the monetary policy framework in 2004 decreased the volatility of the rate. Since the turmoil of the crisis started the volatility increased again. Our method makes it possible for central banks to monitor the volatility of the rate and the impact of changes in the policy for the whole euro area.

Anomaly Detection in Real-Time Gross Settlement Systems.

We discuss how an autoencoder can detect system-level anomalies in a real-time gross settlement system by reconstructing a set of liquidity vectors. A liquidity vector is an aggregated representation of the underlying payment network of a settlement system for a particular time interval. Furthermore, we evaluate the performance of two autoencoders on real-world payment data extracted from the TARGET2 settlement system. We do this by generating different types of artificial bank runs in the data and determining how the autoencoders respond. Our experimental results show that the autoencoders are able to detect unexpected changes in the liquidity flows between banks.

Analysis of Risk Factors in the Korean Repo Market: Based on the US and European Repo Market Experiences

Repo markets had been deemed more resilient against market instability compared to the unsecured inter-bank loan markets. In the US and Europe, however, prolonged investor runs on repos developed during the global financial crisis. Furthermore, in the course of the evolution of the crisis, the repo markets in the US and Europe showed differing movements. In contrast, no risks arising from the Korean repo market have yet emerged in practice thanks in part to its small market volume during the global financial crisis, but it could give rise to significant risks due to some fragility arising from factors such as the increase of trade concentration. For this reason, we wish to identify some weak points, and suggest some areas for improvement such as a ceiling on the amount of borrowing, and on the proportion of illiquid collateral held by investors. In addition, we discuss the need for the greater differentiation of margin, and for the extension of the intraday repo facility to the repo market.

Cyclical Patterns in Risk Indicators Based on Financial Market Infrastructure Transaction Data

This paper studies cyclical patterns in risk indicators based on TARGET2 transaction data. These indicators provide information on network properties, operational aspects and links to ancillary systems. We compare the performance of two different ARIMA dummy models to the TBATS state space model. The results show that the forecasts of the ARIMA dummy models perform better than the TBATS model. We also find that there is no clear difference between the performances of the two ARIMA dummy models. The model with the fewest explanatory variables is therefore preferred.

Detection and explanation of anomalous payment behavior in real-time gross settlement systems

In this paper, we discuss how to apply an autoencoder to detect anomalies in payment data derived from an Real-Time Gross Settlement system. Moreover, we introduce a drill-down procedure to measure the extent to which the inflow or outflow of a particular bank explains an anomaly. Experimental results on real-world payment data show that our method can detect the liquidity problems of a bank when it was subject to a bank run with reasonable accuracy.

Risk Indicators for Financial Market Infrastructure: From High Frequency Transaction Data to a Traffic Light Signal

This paper identifies quantitative risks in financial market infrastructures (FMIs), which are inspired by the Principles for Financial Market Infrastructures. We convert transaction level data into indicators that provide information on operational risk, changes in the network structure and interdependencies. As a proof of concept we use TARGET2 level data. The indicators are based on legislation, guidelines and their own history. Indicators that are based on their own history are corrected for cyclical patterns. We also define a method for setting the signaling threshold of relevant changes. For the signaling, we opt for a traffic light approach: a green, yellow or red light for a small, moderate or substantial change in the indicator, respectively. The indicators developed in this paper can be used by overseers and operators of FMIs and by financial stability experts.