Large-value Payment System Research Articles

Intraday liquidity risk: based on microprudential and macroprudential regulatory perspective

ABSTRACT This paper addresses the effectiveness of current regulations on intraday liquidity risk through a comprehensive analysis using network structure and payment flow characteristics of China’s large-value payment system. Utilizing simulation and econometric methods, it examines the factors influencing the upper limit of liquidity levels required by payment system participants to satisfy real-time gross settlement (RTGS) and its tail distribution. The results of the study show that: (1) in a network topology, the characteristics of a node significantly affect its demand for intraday liquidity and its exposure to intraday liquidity risk; (2) microprudential regulation based only on individuals not only fails to achieve macroprudential regulatory objectives, but also fails to realize individual microprudential regulatory objectives, and at the same time, it reduces the efficiency of liquidity use; (3) reallocating intraday liquidity among participants based on systemic effects not only saves liquidity overall, but also achieves the macroprudential regulatory objective of liquidity at a high probability level. This study provides valuable theoretical and practical insights for regulators and financial managers, emphasizing the importance of effective regulation to prevent the evolution of intraday liquidity risk into systemic risk.

A computational model of bilateral credit limits in payment systems and other financial market infrastructures

This paper provides the first steps towards a theoretical and structural modelling framework through which optimal decision making in financial market infrastructures such as payments clearing and settlement systems can be assessed from a market microstructure perspective. In particular, the paper focuses on the application of agent-based computational economics and stochastic games in modelling the bilateral credit limit establishing behaviour of Participants in loss sharing arrangements within financial market infrastructures such as the Canadian Large Value Payments System (LVTS). With specific focus on the LVTS, the paper presents a structural model where the payments system represents a market in which bilateral credit limits are the pricing mechanisms for intraday liquidity provisioning and the credit risk arising from the loss sharing arrangement. The data-driven stochastic game framework further illustrates how payments data, in conjunction with other financial market and credit data, can be used to assess emergent macroscopic outcomes in clearing and settlement systems from the underpinning interactions of autonomous decision making agents. The paper speaks to potential policy issues such as the effectiveness of policy levers such as the System-Wide Percentage, regulatory concerns around procyclicality and free-riding arising from the market microstructure behaviours, and design of the System.

Measuring the impact of a failing participant in payment systems

Large banks and critical financial market infrastructures (FMIs) that are not able to fulfill their payment obligations, for example following a bankruptcy or cyber-attack, can be a source of financial instability and contagion in the financial system. This paper develops a composite risk indicator to evaluate the criticality of participants in a large value payment system network, combining liquidity risk (i.e. size of incoming and outgoing payments) and systemic impact or interconnections between network participants in one approach. It is applied, as a proof of concept, to the TARGET2 payment system that links banks and FMIs in a tight network of interdependencies. We find that the most critical participants in TARGET2 are other payment systems (large value and retail) because of the underlying gross size of their payment flows. Some banks may be critical, but this is mainly due to their interconnectedness with other TARGET2 participants. Central counterparties and central securities depositories are less critical to the payment system. Our findings can be used by (1) financial stability experts to evaluate the impact of a failing critical participant in the financial system, and (2) central banks in their role as payment system operator and overseer. Besides, it feeds into policy discussions on payment system access, oversight, and crisis management.

Quantifying the economic benefits of payments modernization: the case of Canada’s large-value payment system

In this paper, we develop a discrete-choice framework to quantify the economic benefits of Canada’s large-value payment system modernization (ie, the replacement of Canada’s large-value transfer system (LVTS) with Lynx, the new large-value payments system in Canada). We first estimate participants’ preferences for liquidity cost, payment safety and the network effect by exploiting intraday variations in the relative choice probabilities of the two substitutable subsystems (tranches 1 and 2) in the LVTS. Then, with the estimated model, we calculate the changes in participants’ welfare when the LVTS is replaced by Lynx. First, compared with the LVTS, Lynx has higher liquidity costs but is more secure. Second, when over 90% of current LVTS payments migrate to Lynx, there is an overall welfare gain. Third, accounting for equilibrium adjustment after the replacement of the LVTS with Lynx, about a 75% improvement in service quality level is needed to generate overall net economic benefits to participants. Among other things, adopting a liquidity-saving mechanism and reducing risks in the new large-value payments system could help achieve this improvement. Finally, the welfare changes are fairly heterogeneous across participants, especially between large and small participants.

Measure twice before you cut: differences in Furfine-type algorithm implementations

Our study focuses on the practical implementation aspects of “Furfine-type” algorithms used to identify money market loans from payments data. We use two different versions of Furfine-type algorithms applied to data from TARGET2, the Eurosystem’s large-value payment system. We illustrate the magnitude and development over time of the differences between the implementations and study the explanations depending on the technical specifications of the algorithms. Paramount are the applied range of plausible rates and the identification of zero-rate loans. Consequently, the monetary policy environment and market conditions have an influence on the differences in the resulting data sets. These even affect aggregate indicators including interest rate dispersion, especially in times of stress, such as during the sovereign debt crisis and, to a lesser extent, the global pandemic. We find that an environment with methodological plurality can reduce overall uncertainty by using cross-checks between different data sources. The results strengthen the foundation for applying Furfine-type algorithms and are useful as guidance for their implementation and calibration in the euro area and beyond.

How much liquidity would a liquidity-saving mechanism save if a liquidity-saving mechanism could save liquidity? A simulation approach for Canada’s large-value payment system

Canada’s Large Value Transfer System (LVTS) is in the process of being replaced by a real-time gross settlement (RTGS) system. A pure RTGS system typically requires participants to hold substantial amounts of intraday liquidity in order to settle their payment obligations. Implementing one or more liquidity-saving mechanisms (LSMs) can reduce the amount of liquidity that participants need to hold. This paper investigates how much liquidity requirements can be reduced with the implementation of different LSMs in the Financial Network Analytics simulation engine using LVTS transaction data from 2018. These LSMs include (1) bilateral offsetting, (2) FIFO–bypass, (3) multilateral offsetting and (4) a combination of all LSMs. We simulate two different scenarios at varying levels of the upper bound of liquidity. In the first scenario, all payments from Tranche 1, which are considered time critical, are settled in a pure RTGS payment stream, while less time-critical Tranche 2 payments are settled in a payment stream with LSMs. In the second scenario, we settle all payments (Tranches 1 and 2) in the LSM stream. Our results show that when there is ample liquidity available in the system, there is minimal benefit from LSMs, as payments are settled without much delay: the effectiveness of LSMs increases as the amount of intraday liquidity decreases. A combination of LSMs shows a reduction in liquidity requirements that is larger than any one individual LSM.

Monitoring Liquidity Management of Banks With Recurrent Neural Networks

Monitoring liquidity management of banks is one of the prime tasks of central banks. A bank that manages its liquidity inadequately can severely harm its liquidity position and potentially threaten the stability of the entire financial system. Central banks try to anticipate these risks by carefully monitoring the liquidity management of banks in large-value payment systems (LVPSs). Typically, they do this based on statistical methods in which various risk indicators related to the liquidity usage of banks are calculated from the transaction log of an LVPS. These indicators need to be manually analyzed by payment experts to find irregularities that could signal potential risks. Although statistical methods provide much insight into the liquidity management of banks, they do not scale well to the large number of banks that are subject to risk monitoring and the high velocity by which payments are nowadays settled. In this paper, we investigate whether the liquidity management of banks can be monitored more efficiently by anomaly detection. We construct different probabilistic classifiers that classify delta sequences of banks by the corresponding bank. A delta sequence captures the change in the liquidity position of a bank in an LVPS throughout a given day. Accordingly, anomalies in the intraday liquidity usage of banks are detected by determining whether the classifiers misclassify recent delta sequences that were not used to train the classifiers. Our results show that recurrent neural networks are well suited to perform this classification task and detect many irregularities in payment behavior that are interesting for the supervisors and operators of an LVPS.

Measuring the Impact of a Failing Participant in Payment Systems

Banks and financial market infrastructures (FMIs) that are not able to fulfill their payment obligations can be a source of financial instability. This paper develops a composite risk indicator to evaluate the criticality of participants in a large value payment system network, combining liquidity risk and interconnections in one approach, and applying this to the TARGET2 payment system. Findings suggest that the most critical participants in TARGET2 are other payment systems, because of the size of underlying payment flows. Some banks may be critical, but this is mainly due to their interconnectedness with other TARGET2 participants. Central counterparties and central securities depositories are less critical. These findings can be used in financial stability analysis, and feed into central bank policies on payment system access, oversight, and crisis management.

How Much Liquidity Would a Liquidity-saving Mechanism Save if a Liquidity-saving Mechanism could Save Liquidity? A Simulation Approach for Canada’s Large-value Payment System

Canada’s Large Value Transfer System (LVTS) is in the process of being replaced by a real-time gross settlement (RTGS) system. A pure RTGS system typically requires participants to hold substantial amounts of intraday liquidity in order to settle their payment obligations. Implementing one or more liquidity-saving mechanisms (LSMs) can reduce the amount of liquidity that participants need to hold. This paper investigates how much liquidity requirements can be reduced with the implementation of different LSMs in the Financial Network Analytics simulation engine using LVTS transaction data from 2018. These LSMs include (1) bilateral offsetting, (2) FIFO–bypass, (3) multilateral offsetting and (4) a combination of all LSMs. We simulate two different scenarios at varying levels of the upper bound of liquidity. In the first scenario, all payments from Tranche 1, which are considered time critical, are settled in a pure RTGS payment stream, while less time-critical Tranche 2 payments are settled in a payment stream with LSMs. In the second scenario, we settle all payments (Tranches 1 and 2) in the LSM stream. Our results show that when there is ample liquidity available in the system, there is minimal benefit from LSMs, as payments are settled without much delay: the effectiveness of LSMs increases as the amount of intraday liquidity decreases. A combination of LSMs shows a reduction in liquidity requirements that is larger than any one individual LSM.

Detecting anomalous payments networks: A dimensionality-reduction approach

Abstract Anomaly-detection methods are aimed at identifying observations that deviate manifestly from what is expected. Such methods are usually run on low-dimensional data, such as time series data. However, the increasing importance of high-dimensional payments and exposure data for financial oversight requires methods for detecting anomalous networks. To detect an anomalous network, dimensionality reduction allows measuring of the extent to which the network's main connective features (i.e. the structure) deviate from those regarded as typical. The key to dimensionality-reduction methods is the ability to reconstruct data with an error; this reconstruction error serves as a yardstick for deviation from what is typical. Principal component analysis (PCA) is used as a dimensionality-reduction method, and a clustering algorithm is used to classify reconstruction errors as normal or anomalous. Based on data from Colombia's large-value payments system and a set of synthetic anomalous networks created through simulations of intraday payments, detecting anomalous payments networks is feasible and promising for financial-oversight purposes.

Measuring the Impact of a Failing Participant in Payment Systems

Banks and financial market infrastructures (FMIs) that are not able to fulfill their paymentobligations can be a source of financial instability. This paper develops a composite risk indicator toevaluate the criticality of participants in a large value payment system network, combiningliquidity risk and interconnections in one approach, and applying this to the TARGET2payment system. Findings suggest that the most critical participants in TARGET2 are otherpayment systems, because of the size of underlying payment flows. Some banks may becritical, but this is mainly due to their interconnectedness with other TARGET2 participants.Central counterparties and central securities depositories are less critical. These findings can beused in financial stability analysis, and feed into central bank policies on payment system access,oversight, and crisis management.

Pattern recognition of financial institutions’ payment behavior

Abstract We present a general supervised machine-learning methodology to represent the payment behavior of financial institutions starting from a database of transactions in the Colombian large-value payment system. The methodology learns a feedforward artificial neural network parameterization to represent the payment patterns through 113 features corresponding to financial institutions’ contribution to payments, funding habits, payment timing, payment concentration, centrality in the payment network, and systemic effects due to failure to pay. We then use the representation to compare the coherence of out-of-sample payment patterns of the same institution to its characteristic patterns. The performance is remarkable, with an out-of-sample classification error around three percent. The performance is robust to reductions in the number of features by unsupervised feature selection. In addition, we confirm that network centrality and systemic effect features definitively contribute to enhancing the performance of the methodology. For financial authorities, this is a major step towards the automated detection of individual financial institutions’ anomalous behaviors in payment systems.

Intraday liquidity facilities, late settlement fee and coordination

Abstract This paper analyses the intraday liquidity management game played in large-value payment systems accounting for a variable and a fixed cost of liquidity. While the liquidity cost is a decisive factor for settlement behaviour, the availability of intraday liquidity until end-of-day matters too, as it mutes late settlement incentives originating from settlement risk. Whether liquidity is provided via overdraft or intraday credit hardly matters. A late settlement fee can both disincentivise settlement coordination or implement early settlement. Its calibration is usually non-trivial with the major exception of a fixed cost and end-of-day availability of intraday liquidity.

Outlier Detection in TARGET2 Risk Indicators

This paper studies the detection of outliers in risk indicators based on large value payment system transaction data. The ten risk indicators are daily time series measuring various risks in the large value payment system, such as operational risk, concentration risk and liquidity flows related to other financial market infrastructures. We use extreme value theory and local outlier factor methods to identify anomalous data points (outliers). In a univariate setup, the extreme value analysis quantifies the unusualness of each outlier. In a multivariate setup, the local outlier factor method identifies outliers by measuring the local deviation of a given data point with respect to its neighbours. We find that most detected outliers are at the beginning and near end of the calendar month when turnover is significantly larger than at other days. Our method can be used e.g. by overseers and financial stability experts who wish to look at many (risk) indicators in relation to each other.

Payment instruments and collateral in the interbank payment system

This paper presents a three-period model to analyze why banks need bank reserves despite the presence of other liquid assets, such as Treasury securities. The model shows that if a pair of banks settle bank transfers between them without the central bank, a hold-up problem occurs when they bargain over the terms of settlement. This result stems from the confidentiality of bank-transfer requests, which makes it necessary for a depositor to retain an outside option to withdraw cash to enforce a bank-transfer request in a deposit contract. In light of this result, the large value payment system operated by the central bank can be regarded as an implicit interbank settlement contract to prevent a hold-up problem. In this contract, the central bank is characterized as the custodian of collateral. Bank reserves correspond to the balances of liquid collateral that banks submit to the central bank. This result can explain the rate-of-return dominance puzzle as well as why the central bank must replace liquid assets with bank reserves. The optimal implicit contract features a type of deferred net settlement in which the value of bank reserves transferred between banks is smaller than the net value of bank transfers to be settled.

Freeriding on liquidity in the Colombian large-value payment system

Freeriding on liquidity in the Colombian large-value payment system

The Impact of De-Tiering in the United Kingdom's Large-Value Payment System

Large-value payment systems (LVPS) often have a tiered structure where only a limited number of banks have direct access to these systems and every other institution accesses the system through agency arrangements with the direct participants. As such, a high degree of tiering is often perceived as being associated with credit and operational risks. In this paper we use data around five recent de-tiering events in the United Kingdom’s LVPS (CHAPS), to assess the impact of de-tiering on these risks as well as on liquidity usage. We find that the impact of de-tiering is largest on credit risk, where average intraday exposures between first and second-tier banks drop by anywhere between £0.3 billion and £1.5 billion per bank, while the cost of insuring against losses arising from these exposures drops by about £4 million to £19 million per bank, per year. On the other hand, the impact of these de-tiering events on operational risk and liquidity usage appears to be economically small.

Nonmonotonic trade-offs of tiering in a large-value payment system

Even though international authorities encourage open and wide access to large-value payment systems, the optimal level of access, or tiering, is still an open problem. For real-time gross settlement systems, it may be limited by the trade-off between the potentially higher liquidity needs of a larger pool of direct participants settling in real time and the lower counterparty credit risks resulting from a smaller number of second-tier participants entering uncovered bilateral credit positions with correspondent banks. The literature has evaluated this trade-off through simulations, finding monotonically increasing liquidity savings and increasing credit risk exposures as the level of tiering in the system rises. In contrast, we find that, in the Colombian real-time gross settlement systems, liquidity savings increase but then decrease with higher tiering, showing a humped shape. Our results provide insights into the effects of tiering when participants are too big or too connected to tier.

Developments in the Payment System Architecture of India

The payment and settlement systems are at the core of financial infrastructure in a country. A well-functioning payment and settlement system is crucial for the successful implementation of monetary policy and maintaining the financial stability. This paper examines the various types of Payment systems in India along with the developments aimed at improving the efficiency of Payments system. We analyse both the large value payment systems and retail payment systems. The large value payment infrastructure comprises RTGS, high value clearing, and CCIL. The retail payment systems include MICR/non-MICR cheque clearing, national electronic funds transfer (NEFT) system, electronic clearing service (ECS), cheque truncation system (CTS) and payment channels like credit/debit cards, internet and mobile phone-based products. The paper also looks at the role of RBI in Payment and Settlement System. In addition, there is a description of various recent measures to promote electronic retail payments such as the Digital Wallet, Aadhaar-enabled Payment System (AEPS) and Unified Payment Interface (UPI) of India.

The impact of de-tiering in the United Kingdom’s large-value payment system

Large-value payment systems (LVPSs) often have a tiered structure, whereby only a limited number of banks have direct access to these systems, while every other institution accesses it through agency arrangements with direct participants. As such, a high degree of tiering is often perceived as being associated with credit and operational risks. In this paper, we use data around five recent de-tiering events in the United Kingdom’s LVPS (Clearing House Automated Payment System) to assess the impact of de-tiering on these risks as well as on liquidity usage. We find that the impact of de-tiering is largest on credit risk, where average intraday exposures between first- and second-tier banks drop by anywhere between £0.3 billion and £1.5 billion per bank, while the cost of insuring against the losses arising from these exposures drops by £4 million to £19 million per bank, per year. Nevertheless, the impact of these de-tiering events on operational risk and liquidity usage appears to be economically small.