Mapping the State of Financial Stability

The paper uses the Self-Organizing Map for mapping the state of financial stability and visualizing the sources of systemic risks on a two-dimensional plane 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 and thus 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 horizon.systemic financial crisis; systemic risk; self-organizing maps; visualisation; prediction; macroprudential supervision

Mapping the state of financial stability

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 Classification: E44, E58, F01, F37, G01macroprudential supervision, prediction, Self-Organizing Map (SOM), Systemic financial crisis, systemic risk, visualization

Three essays on the use of neural networks for financial prediction

The number of studies trying to explain the causes and consequences of the economic and financial crises usually rises considerably after a banking crisis occurs. The dramatic effects of the most recent financial crisis on the real economy around the world call for a better comprehension of previous crises as a way to anticipate future crisis episodes. It is precisely this objective, preventing future crises, the main motivation of this PhD dissertation. We identify two important mechanisms that have failed during the latest years and that are closely related to the onset of the financial crisis: The assessment of the solvency of banks along with the systemic risk over the time, and the detection of the macroeconomic imbalances in some countries, especially in Europe, which made the financial crisis evolve through a sovereign crisis. Our dissertation is made up of three different essays, trying to go a step ahead in the knowledge of these mechanisms.Departamento de Economía Financiera y ContabilidadDoctorado en Economía de la Empres

A Macroprudential Framework for the Early Detection of Banking Problems in Emerging Economies

This paper develops an analytical framework that can be used to anticipate problems in the banking system and enable supervisors to take mitigating actions at an early stage. This paper has two components. First, it develops an early warning indicator that is intended to capture a number of the systemic risks that can affect the banking system as a whole. Second, it develops a methodology to detect problems at the individual bank level in an effort to identify those firms with financial vulnerabilities. For the systemic component of our methodology, the final output is a banking system vulnerability index to facilitate bank monitoring tasks, as well as some disaggregated subcomponents that are intended to display the relative importance of the different risks (e.g., liquidity, currency, and interest rate risks). Regarding the assessment of the soundness of individual institutions, the paper uses a methodology based on cluster analysis that incorporates the results of the previous framework. There is an empirical application of the systemic component that is based on the 2001 Argentine banking crisis. It shows that the proposed vulnerability indicator started to increase steadily beginning in 1999, following 2 years in which it had remained flat, and it finally peaked in mid-2001, which was just before the onset of the crisis.Banks; stress testing; banking crises; banking regulation; banking supervision; early warning systems

Fitting and forecasting sovereign defaults using multiple risk signals

2noopenopenSavona R.; Vezzoli M.Savona, Roberto; Vezzoli, Marik

Towards a new model for early warning signals for systemic financial fragility and near crises: an application to OECD countries

Using a signal extraction framework and looking at OECD countries over a 30 year period this paper attempts to identify a number of variables significant in predicting near-crises as a pre-cursor to full-fledged crises. These include growth in pension assets as an indicator for the development of liquidity bubbles, equity market dividend yields as a proxy for corporate balance sheet health, banking sector assets growth and relative size to GDP. We also study the development of asset price bubbles through an equity markets indicator and a house price indicator. Finally we also look at a banking sector funding stability indicator and liquidity indicator on a micro-level. Simultaneously, a dynamic research design improves on previous static set-ups and enhances the model predictive power and applicability to different time periods. This paper shows that as early as 2004, clear signals were being given for a number of countries that vulnerabilities were building up with out-of-sample performance better than in-sample in terms of overall noise to signal ratios, showing a significant improvement compared to earlier work. EWS design has significant implications for financial stability and financial regulation.financial crises, financial fragility, liquidity bubbles, early warning signals, financial stability, financial regulation

Bank heterogeneity and interest rate setting: what lessons have we learned since Lehman Brothers?

A substantial literature has investigated the role of relationship lending in shielding borrowers from idiosyncratic shocks. Much less is known about how lending relationships and bank-specific characteristics affect the functioning of the credit market in an economy-wide crisis, when banks may find it difficult to perform the role of shock absorbers. We investigate how bank-specific characteristics (size, liquidity, capitalization, funding structure) and the bank-firm relationship have influenced interest rate setting since the collapse of Lehman Brothers. Unlike the existing literature, which has focused chiefly on the amount of credit granted during the crisis, we look at its cost. The data on a large sample of loans from Italian banks to non-financial firms suggest that close lending relationships kept firms more insulated from the financial crisis. Further, spreads increased by less for the customers of well-capitalized, liquid banks and those engaged mainly in traditional lending business.bank interest rate setting, lending relationship, bank lending channel, financial crisis.

A corporate failure prediction model for non-financial South African corporates incorporating best practices used by the credit industry

In the context of the current macroeconomic environment there is an expectation of an increase in South African non-financial corporate failure, where advance prediction thereof will become even more important. A number of South African non-financial corporate failures have occurred following the financial crisis. In addition, South Africa experienced a watershed moment with the first default on a non-financial corporate bond in 2013. At the same time, with the adoption of the International Financial Reporting Standards (IFRS) framework there have been significant advances in the quality of financial information which should improve its usage in predicting corporate failure. This study used the latest sample to date of listed South African non-financial corporates that met the definition of failure but limited the universe of financial information to that which was prepared under IFRS. At the same time, adjustments were made to the financial data based upon pre-selection of independent credit statistic variables most commonly used in ranking relative credit risk for non-financial corporates. Additionally, equity market price data was introduced into the model to add a forward-looking information consideration. This resulted in an eleven variable model where differentiation of corporate failure was facilitated through the use of multiple discriminant analysis