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Grzegorz Hałaj
Horizontal Line Supervision
- Division
Stress Test Experts
- Current Position
-
Team Lead - Banking Supervision
- Fields of interest
-
Mathematical and Quantitative Methods,Financial Economics
- Education
- 2010
Postdoc, Fields Institute, University of Toronto
- 2009
PhD, Mathematical Finance, Warsaw School of Economics
- 2003
M.Sc., Mathematics, Warsaw University
- Professional experience
- 2018-2022
Principal Researcher, Director and Policy Advisor at the Bank of Canada
- 2012-2017
Financial Stability Expert at the ECB
- 2008-2011
Principal Specialist at ALM unit of Bank Pekao SA (Unicredit Group)
- 2004-2007
Economist at the National Bank of Poland
- Awards
- 2004
Marie Curie Fellowship, CORE at Université catholique de Louvain
- 6 August 2024
- WORKING PAPER SERIES - No. 2970Details
- Abstract
- We build a balance sheet-based model to capture run risk, i.e., a reduced potential to raise capital from liquidity buffers under stress, driven by depositor scrutiny and further fuelled by fire sales in response to withdrawals. The setup is inspired by the Silicon Valley Bank (SVB) meltdown in March 2023 and our model may serve as a supervisory analysis tool to monitor build-up of balance sheet vulnerabilities. Specifically, we analyze which characteristics of the balance sheet are critical in order for banking system regulators to adequately assess run risk and resilience. By bringing a time series of SVB’s balance sheet data to our model, we are able to demonstrate how changes in the funding and respective asset composition made SVB prone to run risk, as they were increasingly relying on heldto-maturity, aka hidden-to-maturity, accounting standards, masking revaluation losses in securities portfolios. Finally, we formulate a tractable optimisation problem to address the designation of heldto-maturity assets and quantify banks’ ability to hold these assets without resorting to remarking. By calibrating this to SVB’s balance sheet data, we shed light on the bank’s funding risk and impliedrisk tolerance in the years 2020–22 leading up to its collapse.
- JEL Code
- C62 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Existence and Stability Conditions of Equilibrium
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
- 22 April 2024
- WORKING PAPER SERIES - No. 2929Details
- Abstract
- We evaluate the effects of contagion and common exposure on banks’ capital through a regression design inspired by the structural VAR literature and derived from the balance sheet identity. Contagion can occur through direct exposures, fire sales, and market-based sentiment, while common exposures result from portfolio overlaps. We estimate the structural regression on granular balance sheet and interbank exposure data of the Canadian banking market. First, we document that contagion varies in time, with the highest levels around the Great Financial Crisis and lowest levels during the pandemic. Second, we find that after the introduction of Basel III the relative importance of risks has changed, hinting that sources of systemic risk have changed structurally. Our new framework complements traditional stress-tests focused on single institutions by providing a holistic view of systemic risk.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
L14 : Industrial Organization→Market Structure, Firm Strategy, and Market Performance→Transactional Relationships, Contracts and Reputation, Networks
- 13 April 2023
- WORKING PAPER SERIES - No. 2806Details
- Abstract
- Interconnectedness is an inherent feature of the modern financial system. While it con-tributes to efficiency of financial services, it also creates structural vulnerabilities: pernicious shock transmission and amplification impacting banks’ capitalization. This has recently been seen during the Global Financial Crisis. Post-crisis reforms addressed many of the causes of this event, but contagion effects may not be fully eliminated. One reason for this may be related to financial institutions’ incentives and strategic behaviours. We propose a model to study contagion effects in a banking system capturing network effects of direct exposures and indirect effects of market behaviour that may impact asset valuation. By doing so, we can embed a well-established fire-sale channel into our model. Unlike in related literature, we relax the assumption that there is an exogenous pecking order of how banks would sell their assets. Instead, banks act rationally in our model; they optimally construct a portfolio subject to budget constraints so as to raise cash to satisfy creditors (interbank and external). We assume that the guiding principle for banks is to maximize risk-adjusted returns gener-ated by their balance sheets. We parameterize the theoretical model with publicly available data for a representative sample of European banks; this allows us to run simulations of bank valuations and asset prices under a set of stress scenarios.
- JEL Code
- C62 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Existence and Stability Conditions of Equilibrium
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
- 18 February 2020
- WORKING PAPER SERIES - No. 2373Details
- Abstract
- We develop an agent-based model of traditional banks and asset managers to investigate the contagion risk related to fire sales and balance sheet interactions. We take a structural approach to the price formation in fire sales as in Bluhm et al. (2014) and introduce a market clearing mechanism with endogenous formation of asset prices. We find that, first, banks which are active in both the interbank and securities markets may channel financial distress between the two markets. Second, while higher bank capital requirements decrease default risk and funding costs, they make it also more profitable to invest into less-liquid assets financed by interbank borrowing. Third, asset managers absorb small liquidity shocks, but they exacerbate contagion when their voluntary liquid buffers are fully utilised. Fourth, a system with larger and more interconnected agents is more prone to contagion risk stemming from funding shocks.
- JEL Code
- C6 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G23 : Financial Economics→Financial Institutions and Services→Non-bank Financial Institutions, Financial Instruments, Institutional Investors
- 20 November 2019
- WORKING PAPER SERIES - No. 2331Details
- Abstract
- We study the interplay between two channels of interconnectedness in the banking system. The first one is a direct interconnectedness, via a network of interbank loans, banks' loans to other corporate and retail clients, and securities holdings. The second channel is an indirect interconnectedness, via exposures to common asset classes. To this end, we analyze a unique supervisory data set collected by the European Central Bank that covers 26 large banks in the euro area. To assess the impact of contagion, we apply a structural valuation model NEVA (Barucca et al., 2016a), in which common shocks to banks' external assets are reflected in a consistent way in the market value of banks' mutual liabilities through the network of obligations. We identify a strongly non-linear relationship between diversification of exposures, shock size, and losses due to interbank contagion. Moreover, the most systemically important sectors tend to be the households and the financial sectors of larger countries because of their size and position in the financial network. Finally, we provide policy insights into the potential impact of more diversified versus more domestic portfolio allocation strategies on the propagation of contagion, which are relevant to the policy discussion on the European Capital Market Union.
- JEL Code
- C45 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Neural Networks and Related Topics
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
- 10 January 2018
- WORKING PAPER SERIES - No. 2121Details
- Abstract
- Liquidity has its systemic aspect that is frequently neglected in research and risk management applications. We build a model that focuses on systemic aspects of liquidity and its links with solvency conditions accounting for pertinent interactions between market participants in an agent-based modelling fashion. The model is confronted with data from the 2014 EU stress test covering all the major banking groups in the EU. The potential amplification role of asset managers is taken into account in a stylised fashion. In particular, we investigate the importance of the channels through which the funding shock to financial institutions can spread across the financial system.
- JEL Code
- G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C61 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Optimization Techniques, Programming Models, Dynamic Analysis
- 2 February 2017
- WORKING PAPER SERIES - No. 2010Details
- Abstract
- We present a tractable framework to assess the systemic implications of bail-in. To this end, we construct a multi-layered network model where each layer represents the securities cross holdings of a specific seniority among the largest euro area banking groups. On this basis, the bail-in of a bank can be simulated to identify the direct contagion risk to the other banks in the network. We find that there is no direct contagion to creditor banks. Spill-overs also tend to be small due to low levels of securities cross-holdings in the interbank network. We also quantify the impact of a bail-in on the different liability holders. In the baseline scenario, shareholders and subordinated creditors are always affected by the bail-in, senior unsecured creditors in 75% of the cases. Finally, we compute the effect of the bail-in on the network topology in each layer. We find that a bail-in significantly reshapes interbank linkages within specific seniority layers.
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G18 : Financial Economics→General Financial Markets→Government Policy and Regulation
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
- 7 June 2016
- WORKING PAPER SERIES - No. 1916Details
- Abstract
- With the aim of reigniting inflation in the euro area, in early 2015 the ECB embarked on a large-scale asset purchase programme. We analyse the macroeconomic effects of the Asset Purchase Programme via the banking system, exploiting the cross-section of individual bank portfolio decisions. For this purpose, an augmented version of the DSGE model of Gertler and Karadi (2013), featuring a segmented banking sector, is estimated for the euro area and combined with a bank portfolio optimisation approach using granular bank level data. An important feature of our modelling approach is that it captures the heterogeneity of banks
- JEL Code
- C61 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Optimization Techniques, Programming Models, Dynamic Analysis
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
- 24 May 2016
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 1, 2016Details
- Abstract
- The new bail-in tool in the EU bank resolution toolkit is an important step forward to safeguard financial stability in Europe, notably in relation to mitigating moral hazard and other problems inherent in a strong reliance on bailouts. At the same time, it is important to understand the potential contagion channels in the financial system following a bail-in and prior to resolution in order to assess potential systemic implications of the use of the bail-in tool. This special feature outlines salient features of the new requirements and then presents a multi-layered network model of banks’ bail-inable securities that could help in gauging potential contagion risk and, prior to a resolution, identifying mitigating measures to avoid systemic implications.
- JEL Code
- G00 : Financial Economics→General→General
- 26 April 2016
- WORKING PAPER SERIES - No. 1896Details
- Abstract
- Theoretically optimal responses of banks to various liquidity and solvency shocks are modelled. The proposed framework is based on a risk-adjusted return portfolio choice in multiple periods subject to the default risk related either to liquidity or solvency problems. Performance of the model and sensitivity of optimal balance sheet structures to some key parameters of the model are illustrated in a specific calibrated setup. The results of the simulations shed light on the effectiveness of the liquidity and solvency regulation. The flexible implementation of the model and its semi-analytical solvability allows for various easy applications of the framework for the macro-prudential policy analysis.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C61 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Optimization Techniques, Programming Models, Dynamic Analysis
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
- 7 March 2014
- WORKING PAPER SERIES - No. 1646Details
- Abstract
- Interbank contagion has become a buzzword in the aftermath of the financial crisis that led to a series of shocks to the interbank market and to periods of pronounced market disruptions. However, little is known about how interbank networks are formed and about their sensitivity to changes in key bank parameters (for example, induced by common exogenous shocks or by regulatory initiatives). This paper aims to shed light on these issues by modelling endogenously the formation of interbank networks, which in turn allows for checking the sensitivity of interbank network structures and hence their underlying contagion risk to changes in market-driven parameters as well as to changes in regulatory measures such as large exposures limits. The sequential network formation mechanism presented in the paper is based on a portfolio optimisation model whereby banks allocate their interbank exposures while balancing the return and risk of counterparty default risk and the placements are accepted taking into account funding diversification benefits. The model offers some interesting insights into how key parameters may affect interbank network structures and can be a valuable tool for analysing the impact of various regulatory policy measures relating to banks' incentives to operate in the interbank market.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
C78 : Mathematical and Quantitative Methods→Game Theory and Bargaining Theory→Bargaining Theory, Matching Theory - Network
- Macroprudential Research Network
- 27 November 2013
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 2, 2013Details
- Abstract
- This special feature examines various macro-prudential tools through the lens of recent advances in the study of interbank contagion. The specific set of tools analysed are those designed to contain the “cross-sectional” dimension of systemic risk – that is, those designed to limit the systemic risk stemming from factors such as correlations and common exposures across financial institutions. These include tools such as large exposure limits and other regulatory requirements designed to limit the spread of systemic risk between banks. The analysis rests on the basic notion that interbank network structures, and hence the risk of contagion across the banking system in response to shocks, are influenced by banks’ optimising behaviour subject to regulatory (and other) constraints. Changes in macro-prudential policy parameters, such as large exposure limits, capital charges on counterparty exposures and capital and liquidity requirements more generally, will affect the contagion risk because of their impact on banks’ asset allocation and interbank funding decisions. This in turn implies that well-tailored macro-prudential policy can help reduce interbank contagion risk by making network structures more resilient. The analysis shows that to capture the full extent of potential interbank contagion, all of the different layers of bank interaction should be taken into account. Hence, if the regulator only focuses on one segment of interbank relationships (e.g. direct bilateral exposures), the true contagion risks are likely to be grossly underestimated. This finding has clear policy implications and flags the importance of micro- and macro-prudential regulators having access to sufficiently detailed data so as to be able to map the many interactions between banks.
- JEL Code
- G00 : Financial Economics→General→General
- 11 October 2013
- OCCASIONAL PAPER SERIES - No. 152Details
- Abstract
- The use of macro stress tests to assess bank solvency has developed rapidly over the past few years. This development was reinforced by the financial crisis, which resulted in substantial losses for banks and created general uncertainty about the banking sector's loss-bearing capacity. Macro stress testing has proved a useful instrument to help identify potential vulnerabilities within the banking sector and to gauge its resilience to adverse developments. To support its contribution to safeguarding financial stability and its financial sector-related work in the context of EU/IMF Financial Assistance Programmes, and looking ahead to the establishment of the Single Supervisory Mechanism (SSM), the ECB has developed a top-down macro stress testing framework that is used regularly for forward-looking bank solvency assessments. This paper comprehensively presents the main features of this framework and illustrates how it can be employed for various policy analysis purposes.
- JEL Code
- C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→Forecasting and Simulation: Models and Applications
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G01 : Financial Economics→General→Financial Crises
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
- 15 April 2013
- WORKING PAPER SERIES - No. 1533Details
- Abstract
- The aim of the paper is to propose a model of banks' asset portfolios to account for the strategic and optimising behavior of banks under adverse economic conditions. In the proposed modelling framework, banks are assumed to respond in an optimising manner to changes in their economic environment (e.g. interest rate and credit risk shocks, funding disruptions, etc.). The modelling approach is based on the risk-return optimal program in which banks aim at a particular composition of their assets to maximise risk-adjusted returns while taking into account regulatory capital and liquidity constraints. The approach is designed for applications in banks' stress testing context, as an alternative to the typical static balance sheet assumption. The stress testing applications are illustrated for a large sample of European banks.
- JEL Code
- E6 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
- Network
- Macroprudential Research Network
- 24 January 2013
- WORKING PAPER SERIES - No. 1506Details
- Abstract
- This paper presents a new approach to randomly generate interbank networks while overcoming shortcomings in the availability of bank-by-bank bilateral exposures. Our model can be used to simulate and assess interbank contagion effects on banking sector soundness and resilience. We find a strongly non-linear pattern across the distribution of simulated networks, whereby only for a small percentage of networks the impact of interbank contagion will substantially reduce average solvency of the system. In the vast majority of the simulated networks the system-wide contagion effects are largely negligible. The approach furthermore enables to form a view about the most systemic banks in the system in terms of the banks whose failure would have the most detrimental contagion effects on the system as a whole. Finally, as the simulation of the network structures is computationally very costly, we also propose a simplified measure - a so-called Systemic Probability Index (SPI) - that also captures the likelihood of contagion from the failure of a given bank to honour its interbank payment obligations but at the same time is less costly to compute. We find that the SPI is broadly consistent with the results from the simulated network structures.
- JEL Code
- E6 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
- Network
- Macroprudential Research Network
- 2024
- Journal of Financial Stability
- 2023
- Journal of Economic Dynamics and Control
- 2022
- Journal of Banking and Finance
- 2022
- Bank of Canada Staff Discussion Papers
- 2021
- Journal of Economic Dynamics and Control
- 2021
- Bank of Canada Staff Working Papers
- 2020
- Bank of Canada Staff Working Papers
- 2020
- Latin American Journal of Central Banking
- 2018
- Journal of Financial Stability
- 2018
- Journal of Financial Stability
- 2018
- Journal of Financial Stability
- 2018
- Physica A: Statistical Mechanics and its Applications
- 2016
- International Journal of Theoretical and Applied Finance
- 2015
- Quantitative Finance
- 2015
- Journal of Network Theory in Finance
- 2013
- Computational Management Science
- 2008
- Applied Mathematical Finance