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Níl an t-ábhar seo ar fáil i nGaeilge.

Peter Sarlin

21 September 2011
WORKING PAPER SERIES - No. 1382
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Abstract
The paper uses the Self-Organizing Map for mapping the state of financial stability and visualizing the sources of systemic risks as well as for predicting systemic financial crises. The Self-Organizing Financial Stability Map (SOFSM) enables a two-dimensional representation of a multidimensional financial stability space that allows disentangling the individual sources impacting on systemic risks. The SOFSM can be used to monitor macro-financial vulnerabilities by locating a country in the financial stability cycle: being it either in the pre-crisis, crisis, post-crisis or tranquil state. In addition, the SOFSM performs better than or equally well as a logit model in classifying in-sample data and predicting out-of-sample the global financial crisis that started in 2007. Model robustness is tested by varying the thresholds of the models, the policymaker's preferences, and the forecasting horizons.
JEL Code
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
F01 : International Economics→General→Global Outlook
F37 : International Economics→International Finance→International Finance Forecasting and Simulation: Models and Applications
G01 : Financial Economics→General→Financial Crises
Network
Macroprudential Research Network
1 February 2013
WORKING PAPER SERIES - No. 1509
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Abstract
This paper introduces a new loss function and Usefulness measure for evaluating early warning systems (EWSs) that incorporate policymakers' preferences between issuing false alarms and missing crises, as well as individual observations. The novelty derives from three enhancements: i) accounting for unconditional probabilities of the classes, ii) computing the proportion of available Usefulness that the model captures, and iii) weighting observations by their importance for the policymaker. The proposed measures are model free such that they can be used to assess signals issued by any type of EWS, such as logit and probit analysis and the signaling approach, and flexible for any type of crisis EWSs, such as banking, debt and currency crises. Applications to two renowned EWSs, and comparisons to two commonly used evaluation measures, illustrate three key implications of the new measures: i) further highlights the importance of an objective criterion for choosing a final specification and threshold value, and for models to be useful ii) the need to be more concerned about the rare class and iii) the importance of correctly classifying observations of the most relevant entities. Beyond financial stability surveillance, this paper also opens the door for cost-sensitive evaluations of predictive models in other tasks.
JEL Code
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
F01 : International Economics→General→Global Outlook
F37 : International Economics→International Finance→International Finance Forecasting and Simulation: Models and Applications
G01 : Financial Economics→General→Financial Crises
Network
Macroprudential Research Network
15 October 2013
WORKING PAPER SERIES - No. 1597
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Abstract
The paper develops an early-warning model for predicting vulnerabilities leading to distress in European banks using both bank and country-level data. As outright bank failures have been rare in Europe, the paper introduces a novel dataset that complements bankruptcies and defaults with state interventions and mergers in distress. The signals of the early warning model are calibrated not only according to the policy-maker's preferences between type I and II errors, but also to take into account the potential systemic relevance of each individual financial institution. The key findings of the paper are that complementing bank specific vulnerabilities with indicators for macro-financial imbalances and banking sector vulnerabilities improves model performance and yields useful out-of-sample predictions of bank distress during the current financial crisis.
JEL Code
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
F01 : International Economics→General→Global Outlook
F37 : International Economics→International Finance→International Finance Forecasting and Simulation: Models and Applications
G01 : Financial Economics→General→Financial Crises
Network
Macroprudential Research Network
12 February 2015
WORKING PAPER SERIES - No. 1758
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Abstract
This paper investigates leading indicators of systemic banking crises in a panel of 11 EU countries, with a particular focus on Finland. We use quarterly data from 1980Q1 to 2013Q2, in order to create a large number of macro-financial indicators, as well as their various transformations. We make use of univariate signal extraction and multivariate logit analysis to assess what factors lead the occurrence of a crisis and with what horizon the indicators lead a crisis. We find that loans-to-deposits and house price growth are the best leading indicators. Growth rates and trend deviations of loan stock variables also yield useful signals of impending crises. While the optimal lead horizon is three years, indicators generally perform well with lead times ranging from one to four years. We also tap into unique long time-series of the Finnish economy to perform historical explorations into macro-financial vulnerabilities.
JEL Code
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
F30 : International Economics→International Finance→General
G01 : Financial Economics→General→Financial Crises
G15 : Financial Economics→General Financial Markets→International Financial Markets
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
Network
Macroprudential Research Network
23 March 2015
WORKING PAPER SERIES - No. 1768
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Abstract
This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in data availability and precision, the transition from firm-centric to system-wide supervision imposes vast data needs. Moreover, in addition to internal communication as in any organization, broad and effective external communication of timely information related to systemic risks is a key mandate of macroprudential supervisors. This further stresses the importance of simple representations of complex data. The present paper focuses on the background and theory of information visualization and visual analytics, as well as techniques within these fields, as potential means for risk communication. We define the task of visualization in risk communication, discuss the structure of macroprudential data, and review visualization techniques applied to systemic risk. We conclude that two essential, yet rare, features for supporting the analysis of big data and communication of risks are analytical visualizations and interactive interfaces. For visualizing the so-called macroprudential data cube, we provide the VisRisk platform with three modules: plots, maps and networks. While VisRisk is herein illustrated with five web-based interactive visualizations of systemic risk indicators and models, the platform enables and is open to the visualization of any data from the macroprudential data cube.
JEL Code
G01 : Financial Economics→General→Financial Crises
G15 : Financial Economics→General Financial Markets→International Financial Markets
F37 : International Economics→International Finance→International Finance Forecasting and Simulation: Models and Applications
F38 : International Economics→International Finance→International Financial Policy: Financial Transactions Tax; Capital Controls
F47 : International Economics→Macroeconomic Aspects of International Trade and Finance→Forecasting and Simulation: Models and Applications
Network
Macroprudential Research Network
24 March 2015
WORKING PAPER SERIES - No. 1769
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Abstract
This paper introduces the ratio of debt to cash flow (D/CF) of nations and their economic sectors to macroprudential analysis, particularly as an indicator of systemic risk and vulnerabilities. While leverage is oftentimes linked to the vulnerability of a nation, the stock of total debt and the flow of gross savings is a less explored measure. Cash flows certainly have a well-known connection to corporations' ability to service debt. This paper investigates whether the D/CF provides a means for understanding systemic risks. For a panel of 33 nations, we explore historic D/CF trends, and apply the same procedure to economic sectors. In terms of an early-warning indicator, we show that the D/CF ratio provides a useful additional measure of vulnerability to systemic banking and sovereign crises, relative to more conventional indicators. As a conceptual framework, the assessment of financial stability is arranged for presentation within four vulnerability zones, and exemplified with a number of illustrative case studies.
JEL Code
E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
F34 : International Economics→International Finance→International Lending and Debt Problems
G01 : Financial Economics→General→Financial Crises
H63 : Public Economics→National Budget, Deficit, and Debt→Debt, Debt Management, Sovereign Debt
Network
Macroprudential Research Network
13 July 2015
WORKING PAPER SERIES - No. 1828
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Abstract
Building on the literature on systemic risk and financial contagion, the paper introduces estimated network linkages into an early-warning model to predict bank distress among European banks. We use multivariate extreme value theory to estimate equity-based tail-dependence networks, whose links proxy for the markets' view of bank interconnectedness in case of elevated financial stress. The paper finds that early warning models including estimated tail dependencies consistently outperform bank-specific benchmark models with- out networks. The results are robust to variation in model specification and also hold in relation to simpler benchmarks of contagion. Generally, this paper gives direct support for measures of interconnectedness in early-warning models, and moves toward a unified representation of cyclical and cross-sectional dimensions of systemic risk.
JEL Code
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G33 : Financial Economics→Corporate Finance and Governance→Bankruptcy, Liquidation
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory
16 November 2015
WORKING PAPER SERIES - No. 1866
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Abstract
This paper uses domestic and cross-border linkages to measure the interconnectedness of the banking sector, and relates it to banking crises in Europe. Beyond cross-border financial linkages of the banking sector, we also account for financial linkages to the other main financial and non-financial sectors within the economy. We enrich conventional early-warning models using macro-financial vulnerabilities, by including measures of banking sector centrality as potential determinants of banking crises. Our results show that a more central position of the banking sector in these so-called macro-networks significantly increases the probability of a banking crisis. By analyzing the different types of risk exposures, our evidence shows that credit as well as market risks are important sources of vulnerabilities. Finally, the results show that early-warning models augmented with interconnectedness measures outperform traditional models in terms of out-of-sample predictions of recent banking crises in Europe.
JEL Code
F36 : International Economics→International Finance→Financial Aspects of Economic Integration
G20 : Financial Economics→Financial Institutions and Services→General
14 January 2016
WORKING PAPER SERIES - No. 1876
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Abstract
In the wake of the still ongoing global financial crisis, bank interdependencies have come into focus in trying to assess linkages among banks and systemic risk. To date, such analysis has largely been based on numerical data. By contrast, this study attempts to gain further insight into bank interconnections by tapping into financial discourse. We present a text-to-network process, which has its basis in co-occurrences of bank names and can be analyzed quantitatively and visualized. To quantify bank importance, we propose an information centrality measure to rank and assess trends of bank centrality in discussion. For qualitative assessment of bank networks, we put forward a visual, interactive interface for better illustrating network structures. We illustrate the text-based approach on European Large and Complex Banking Groups (LCBGs) during the ongoing financial crisis by quantifying bank interrelations and centrality from discussion in 3M news articles, spanning 2007Q1 to 2014Q3.
JEL Code
E : Macroeconomics and Monetary Economics
2 May 2016
WORKING PAPER SERIES - No. 1900
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Abstract
This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high relevance for assessing the relative performance of a wide variety of methods. Further, we test various ensemble approaches to aggregating the information products of the built models, providing a more robust basis for measuring country-level vulnerabilities. Finally, we provide approaches to estimating model uncertainty in early-warning exercises, particularly model performance uncertainty and model output uncertainty. The approaches put forward in this paper are shown with Europe as a playground. Generally, our results show that the conventional statistical approaches are outperformed by more advanced machine learning methods, such as k-nearest neighbors and neural networks, and particularly by model aggregation approaches through ensemble learning.
JEL Code
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
F30 : International Economics→International Finance→General
G01 : Financial Economics→General→Financial Crises
G15 : Financial Economics→General Financial Markets→International Financial Markets
C43 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Index Numbers and Aggregation
23 February 2017
WORKING PAPER SERIES - No. 2025
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Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-
JEL Code
C35 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Discrete Regression and Qualitative Choice Models, Discrete Regressors, Proportions
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
G01 : Financial Economics→General→Financial Crises
11 October 2018
WORKING PAPER SERIES - No. 2182
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Abstract
This paper proposes a framework for deriving early-warning models with optimal out-of-sample forecasting properties and applies it to predicting distress in European banks. The main contributions of the paper are threefold. First, the paper introduces a conceptual framework to guide the process of building early-warning models, which highlights and structures the numerous complex choices that the modeler needs to make. Second, the paper proposes a flexible modeling solution to the conceptual framework that supports model selection in real-time. Specifically, our proposed solution is to combine the loss function approach to evaluate early-warning models with regularized logistic regression and cross-validation to find a model specification with optimal real-time out-of-sample forecasting properties. Third, the paper illustrates how the modeling framework can be used in analysis supporting both microand macro-prudential policy by applying it to a large dataset of EU banks and showing some examples of early-warning model visualizations.
JEL Code
G01 : Financial Economics→General→Financial Crises
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G33 : Financial Economics→Corporate Finance and Governance→Bankruptcy, Liquidation
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling