Experian, TransUnion, and Equifax collaborated to create a fresh kind of scoring model, the VANTAGESCORE. Here’s how it VantageScore is calculated http://www.newhorizon.org/credit-info/how-your-vantagescore-is-calculated/
Looking for unsecured credit cards for bad credit http://www.newhorizon.org/Info/unsecured.htm
The document discusses the financial performance of Fountain from 2009-2012, including key metrics like turnover, shareholders, shares, and financial ratios. Turnover steadily increased from 2009 to 2012. The largest shareholders are Syntegra Capital at 30% and Quaeroc at 12%. While liquidity and solvency ratios are acceptable, the conclusion is that Fountain is not worthy of investment due to its stable turnover, lack of dividends, and history of fusions with smaller companies.
Entropy and systemic risk measures
M. Billio, R. Casarin, M. Costola, A. Pasqualini
Ca’ Foscari Venice University
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
This document discusses factors that influence the level of credit risk in Ethiopian commercial banks. It presents a conceptual framework that categorizes credit risk factors into three groups: quantity of credit risk, quality of credit risk management, and direction of credit risk.
The study uses a quantitative approach with descriptive and econometric techniques on data from 8 major Ethiopian banks from 1990-2012. Regression models are used to analyze the influence of factors in each group as well as a combined model.
The results show that quantity of risk and quality of risk management variables have more influence on credit risk levels, while direction variables have limited impact. Specifically, higher loan amounts, loan growth rates, and single borrower limits increased credit risk, while
Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Netwo...SYRTO Project
Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks - Monica Billio - June 25 2013 - First International Conference on Syrto Project
Prudential policies and systemic risk: the role of interconnectionsEesti Pank
- Prudential policies aim to reduce systemic risk by detecting and preventing risk build-up in the financial system. However, their impact is uncertain due to interconnections between financial systems.
- The study finds that prudential policies reduced vulnerability to systemic crises (SRISK) in banking systems by 87% through both direct and indirect network effects. However, their effectiveness varied between country groups.
- The network effect accounted for 80% of the total impact within the EU, but policies were ineffective for reducing risk in GIIPS countries. The results were robust to various model specifications and weighting schemes.
Experian, TransUnion, and Equifax collaborated to create a fresh kind of scoring model, the VANTAGESCORE. Here’s how it VantageScore is calculated http://www.newhorizon.org/credit-info/how-your-vantagescore-is-calculated/
Looking for unsecured credit cards for bad credit http://www.newhorizon.org/Info/unsecured.htm
The document discusses the financial performance of Fountain from 2009-2012, including key metrics like turnover, shareholders, shares, and financial ratios. Turnover steadily increased from 2009 to 2012. The largest shareholders are Syntegra Capital at 30% and Quaeroc at 12%. While liquidity and solvency ratios are acceptable, the conclusion is that Fountain is not worthy of investment due to its stable turnover, lack of dividends, and history of fusions with smaller companies.
Entropy and systemic risk measures
M. Billio, R. Casarin, M. Costola, A. Pasqualini
Ca’ Foscari Venice University
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
This document discusses factors that influence the level of credit risk in Ethiopian commercial banks. It presents a conceptual framework that categorizes credit risk factors into three groups: quantity of credit risk, quality of credit risk management, and direction of credit risk.
The study uses a quantitative approach with descriptive and econometric techniques on data from 8 major Ethiopian banks from 1990-2012. Regression models are used to analyze the influence of factors in each group as well as a combined model.
The results show that quantity of risk and quality of risk management variables have more influence on credit risk levels, while direction variables have limited impact. Specifically, higher loan amounts, loan growth rates, and single borrower limits increased credit risk, while
Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Netwo...SYRTO Project
Sovereign, Bank, and Insurance Credit Spreads: Connectedness and System Networks - Monica Billio - June 25 2013 - First International Conference on Syrto Project
Prudential policies and systemic risk: the role of interconnectionsEesti Pank
- Prudential policies aim to reduce systemic risk by detecting and preventing risk build-up in the financial system. However, their impact is uncertain due to interconnections between financial systems.
- The study finds that prudential policies reduced vulnerability to systemic crises (SRISK) in banking systems by 87% through both direct and indirect network effects. However, their effectiveness varied between country groups.
- The network effect accounted for 80% of the total impact within the EU, but policies were ineffective for reducing risk in GIIPS countries. The results were robust to various model specifications and weighting schemes.
Credit Risk and Monetary Pass-through. Evidence from ChileEesti Pank
The document discusses a study analyzing the relationship between monetary policy rates, credit risk measures, and commercial interest rates on business loans in Chile. It presents preliminary analysis showing no clear evidence of cointegrating relationships between the variables. The study then shifts to a univariate model allowing for asymmetric pass-through of policy rate changes and the role of monetary policy expectations. It notes some issues with autocorrelated residuals that are addressed by including MA terms in the residuals. The goal is to better quantify how credit risk changes impact the pass-through of policy rates to commercial lending rates.
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christia...SYRTO Project
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christian Brownlees, Christina Hans, Natalia Podlich.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Costs of sovereign default: restructuring strategies, bank distress and the c...ADEMU_Project
This document discusses a research project analyzing how the costs of sovereign debt default, including impacts on economic growth, are affected by the restructuring strategy employed and whether the default triggers a bank crisis. The researchers aim to identify transmission channels through which defaults impact GDP, investment, bank credit, and capital flows, and whether these channels are influenced by the default strategy. They use a dataset of debt default and banking crisis events in 69 countries from 1970-2013. Local projections and augmented inverse probability weighting methods are employed to estimate effects on macroeconomic variables while addressing endogeneity concerns. Preliminary results suggest defaults negatively impact growth, investment, and credit by weakening the financial sector, and these effects depend on the restructuring strategy and whether a bank
Presentation by U. Devrim Demirel, CBO's Fiscal Policy Studies Unit Chief, and James Otterson at the 28th International Conference of The Society for Computational Economics.
Case studies for European Government Bond Dynamics around Euro crisis, Brexit referendum and pre-French election.
Based on ESM working paper #8: https://www.esm.europa.eu/publications/european-government-bond-dynamics-and-stability-policies-taming-contagion-risks
Journal article in JNTF: https://www.risk.net/journal-network-theory-finance/2437982/european-government-bond-dynamics-and-stability-policies
Tracking Variation in Systemic Risk-2 8-3edward kane
This paper proposes a new measure of systemic risk for US banks from 1974-2013 based on Merton's model of credit risk. The measure treats deposit insurance as an implicit option where taxpayers cover bank losses. Each bank's systemic risk is its contribution to the value of this sector-wide option. The model estimates show systemic risk peaked in 2008-2009 during the financial crisis, and bank size, leverage, and risk-taking were key drivers of systemic risk over time.
This document summarizes Sheri Markose's presentation on using multi-agent financial network models (MAFNs) and global macro-net models for macroprudential policymaking. It discusses two key issues with traditional approaches: the paradox of volatility in market data and negative externalities. MAFNs can help visualize systemic risk through bilateral financial data to identify super spreaders and assess network stability. The presentation also covers applications in derivatives modeling, sectoral flows of funds, and insights from country network analyses. Eigenvalue analysis of network structures is discussed as a tool for measuring systemic risk and stabilizing financial systems.
More than half of senior retail, commercial and investment bankers say they lack sufficient data to support robust risk management. This report, sponsored by SAP, looks at how banks are using Big Data to improve risk management and compliance performance. Find out more and watch video: http://bit.ly/RComp1
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based ...SYRTO Project
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based indicators for systemic risk of Euro area sovereigns and for Euro area financial firms” by Lucas et al. - Carsten Detken.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
We have used a multiple regression model to identify the impacts of the variables of credit risk management on the Nigerian deposit money banks’ performance from 2000 to 2020. The estimation was completed using the ordinary least squares method with E-Views 12. The data was sourced from the Nigerian Stock Exchange for information and the Statistical Bulletin of the Central Bank of Nigeria. The outcome determined that return on equity (ROE) is negatively correlated with the nonperforming loan/loan and advances ratio. Last but not least, the ROE measurements of the deposit money banks in Nigeria show a substantial correlation between the ratios of advances and loans to nonperforming loans, loan loss provision to loans and advances, and capital adequacy. These ratios are positively correlated with each other and negatively correlated with the capital adequacy ratio. We advise effective surveillance of pre- and post-deposit financial institution loans for the early detection of problematic debts that won’t be repaid according to schedule and for the thorough analysis of prospective projects as indicated in the financial statement given by the intended client (cash budget, income statement). Accurate identification of realistic projects and repayment terms based on the customer’s past performance will be achieved.
The document discusses consumer credit risk modeling. It covers various statistical and machine learning methods used for credit scoring, including logistic regression, neural networks, and support vector machines (SVM). Logistic regression models the probability of default as a function of input variables and is commonly used. Neural networks can combine and transform input characteristics in non-linear ways but may take longer to train than other methods. The goal is to accurately predict consumer credit risk and default based on application information.
Global credit risk cycles, lending standards, and limits to cross border risk...SYRTO Project
Global credit risk cycles, lending standards, and limits to cross border risk diversification. Bernd Schwaab, Siem Jan Koopman, André Lucas.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
The recent financial market turbulence caused considerable divergence in the banking market interest rate determination process of the euro area member countries (e.g. Illes and Lombardi 2013, Paries et al., 2014). The purpose of this study is to investigate the factors determining the banking market interest rates in the euro area countries during the pre-crisis and the post-crisis periods, and to highlight possible regional asymmetries in the interest rate determination processes. To this end, we employ a set of country specific factors, such as variables capturing macroeconomic conditions, financial risk and loans market conditions, together with common monetary policy factors at euro-zone level. Instead of using specific bank market interest rates, we base our analysis on the ECB’s harmonized cost of bank borrowing indicators of euro area members, in order to avoid cross-country and cross-product data heterogeneity. With the use of principal component analysis, we obtain a number of latent factors that describe unobserved movements in the cost of borrowing, originating either in certain Euro-zone regions or outside the euro area, or constitute common factors for all euro area members. Such factors are identified as macroeconomic conditions, financial risk, loans market conditions and euro area monetary policy variables. These obtained factors, are then used in order to estimate country specific structural equations of the cost of bank borrowing determination. Employing cluster analysis on the parameter coefficients of these models, we then identify euro area regions with similar characteristics regarding the determination of the cost of borrowing. Next, the member states are pooled within the regions identified and structural models are estimated for these regions. By comparing the estimated distinct regional models and the different dynamic effects of the latent factor shocks across the regions, we highlight the differences in the determination of the cost of bank borrowing between the euro-zone core and periphery, and how it has been evolved through the period of the 2007-9 global financial crisis and the subsequent euro area debt crisis.
HLEG thematic workshop on measuring economic, social and environmental resili...StatsCommunications
HLEG thematic workshop on Measuring economic, social and environmental resilience, 25-26 November 2015, Rome, Italy, More information at: http://oe.cd/StrategicForum2015
Accommodative monetary policy breathing space or breeding risks for emergin...Benjamin Huston
The document discusses a workshop on monetary policy spillovers and independence. It aims to examine how accommodative monetary policy in advanced economies may impact financial stability risks in emerging markets through various transmission channels. Specifically, it seeks to analyze potential correlations between financial cycles in advanced and emerging economies, assess how emerging markets responded to supportive monetary policies, and map macroprudential policy tools to different financial stability risks. Key challenges include generating long-term financial cycle data for emerging market countries.
This document summarizes a study that investigates the relationship between loan sizes and credit risk in the microfinance industry of sub-Saharan Africa. Using data on over 2000 annual observations from 632 microfinance institutions across 37 countries between 1995 and 2013, the study finds that credit risk is positively related to loan sizes. This contrasts with evidence from traditional banking, which typically finds an inverse relationship between loan sizes and risk. The results have implications for microfinance portfolio managers, particularly as mobile money services expand in the region.
Dan Andrews - Breaking the shackles:Zombie Firms, Weak Banks and Depressed Re...Structuralpolicyanalysis
1) The document discusses evidence that zombie firms, which are firms that are financially distressed but remain in operation, are more likely to be connected to weak banks.
2) It finds that zombie firms are more likely to be clients of banks that are in poorer financial health, as measured by various indicators of bank balance sheet strength. This is consistent with the hypothesis that weak banks continue to support zombie firms through forbearance to avoid realizing losses.
3) It also discusses how insolvency regimes that make corporate restructuring more difficult can strengthen banks' incentives to engage in forbearance with zombie firms. The negative relationship between bank health and zombie firms is stronger in countries with less restructuring-friendly insolvency
Lessons Learned from Implementing the Cybersecurity Capacity Maturity Model f...Carolin Weisser
This presentation was given by Prof Michael Goldsmith and Dr Patricia Esteve-González, both from the Global Cyber Security Capacity Centre (GCSCC), University of Oxford, at the 2020 Global Cybersecurity Capacity Building Conference in Melbourne, 18 February 2020.
The presentation includes:
- Mission, purpose and impact of the GCSCC
- Lessons learned from implementing the Cybersecurity Capacity Maturity Model for Nations (CMM) around the world
- The shaping and impacts of cybersecurity capacity: What is the status of cybersecurity capacity building? What factors are shaping capacity building within nations? What are the implications of capacity building for nations?
The document discusses debt sustainability analysis (DSA) and its practice in the Turkish Treasury. DSA aims to estimate future debt levels and test debt sustainability under adverse scenarios. The Turkish Treasury uses several models for DSA, including the conventional accounting approach (CAA), debt indicators module (DIM), and Turkish debt simulation model (TDSM). The TDSM is a stochastic model that generates forward-looking scenarios and assesses tail risks, providing a more robust analysis than CAA. Results are reported regularly to management and published to promote transparency.
Credit Risk and Monetary Pass-through. Evidence from ChileEesti Pank
The document discusses a study analyzing the relationship between monetary policy rates, credit risk measures, and commercial interest rates on business loans in Chile. It presents preliminary analysis showing no clear evidence of cointegrating relationships between the variables. The study then shifts to a univariate model allowing for asymmetric pass-through of policy rate changes and the role of monetary policy expectations. It notes some issues with autocorrelated residuals that are addressed by including MA terms in the residuals. The goal is to better quantify how credit risk changes impact the pass-through of policy rates to commercial lending rates.
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christia...SYRTO Project
Bank Interconnectedness What determines the links? - Puriya Abbassi, Christian Brownlees, Christina Hans, Natalia Podlich.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
Costs of sovereign default: restructuring strategies, bank distress and the c...ADEMU_Project
This document discusses a research project analyzing how the costs of sovereign debt default, including impacts on economic growth, are affected by the restructuring strategy employed and whether the default triggers a bank crisis. The researchers aim to identify transmission channels through which defaults impact GDP, investment, bank credit, and capital flows, and whether these channels are influenced by the default strategy. They use a dataset of debt default and banking crisis events in 69 countries from 1970-2013. Local projections and augmented inverse probability weighting methods are employed to estimate effects on macroeconomic variables while addressing endogeneity concerns. Preliminary results suggest defaults negatively impact growth, investment, and credit by weakening the financial sector, and these effects depend on the restructuring strategy and whether a bank
Presentation by U. Devrim Demirel, CBO's Fiscal Policy Studies Unit Chief, and James Otterson at the 28th International Conference of The Society for Computational Economics.
Case studies for European Government Bond Dynamics around Euro crisis, Brexit referendum and pre-French election.
Based on ESM working paper #8: https://www.esm.europa.eu/publications/european-government-bond-dynamics-and-stability-policies-taming-contagion-risks
Journal article in JNTF: https://www.risk.net/journal-network-theory-finance/2437982/european-government-bond-dynamics-and-stability-policies
Tracking Variation in Systemic Risk-2 8-3edward kane
This paper proposes a new measure of systemic risk for US banks from 1974-2013 based on Merton's model of credit risk. The measure treats deposit insurance as an implicit option where taxpayers cover bank losses. Each bank's systemic risk is its contribution to the value of this sector-wide option. The model estimates show systemic risk peaked in 2008-2009 during the financial crisis, and bank size, leverage, and risk-taking were key drivers of systemic risk over time.
This document summarizes Sheri Markose's presentation on using multi-agent financial network models (MAFNs) and global macro-net models for macroprudential policymaking. It discusses two key issues with traditional approaches: the paradox of volatility in market data and negative externalities. MAFNs can help visualize systemic risk through bilateral financial data to identify super spreaders and assess network stability. The presentation also covers applications in derivatives modeling, sectoral flows of funds, and insights from country network analyses. Eigenvalue analysis of network structures is discussed as a tool for measuring systemic risk and stabilizing financial systems.
More than half of senior retail, commercial and investment bankers say they lack sufficient data to support robust risk management. This report, sponsored by SAP, looks at how banks are using Big Data to improve risk management and compliance performance. Find out more and watch video: http://bit.ly/RComp1
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based ...SYRTO Project
Discussion of “Systemic and Systematic risk” by Billio et al. and “CDS based indicators for systemic risk of Euro area sovereigns and for Euro area financial firms” by Lucas et al. - Carsten Detken.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
We have used a multiple regression model to identify the impacts of the variables of credit risk management on the Nigerian deposit money banks’ performance from 2000 to 2020. The estimation was completed using the ordinary least squares method with E-Views 12. The data was sourced from the Nigerian Stock Exchange for information and the Statistical Bulletin of the Central Bank of Nigeria. The outcome determined that return on equity (ROE) is negatively correlated with the nonperforming loan/loan and advances ratio. Last but not least, the ROE measurements of the deposit money banks in Nigeria show a substantial correlation between the ratios of advances and loans to nonperforming loans, loan loss provision to loans and advances, and capital adequacy. These ratios are positively correlated with each other and negatively correlated with the capital adequacy ratio. We advise effective surveillance of pre- and post-deposit financial institution loans for the early detection of problematic debts that won’t be repaid according to schedule and for the thorough analysis of prospective projects as indicated in the financial statement given by the intended client (cash budget, income statement). Accurate identification of realistic projects and repayment terms based on the customer’s past performance will be achieved.
The document discusses consumer credit risk modeling. It covers various statistical and machine learning methods used for credit scoring, including logistic regression, neural networks, and support vector machines (SVM). Logistic regression models the probability of default as a function of input variables and is commonly used. Neural networks can combine and transform input characteristics in non-linear ways but may take longer to train than other methods. The goal is to accurately predict consumer credit risk and default based on application information.
Global credit risk cycles, lending standards, and limits to cross border risk...SYRTO Project
Global credit risk cycles, lending standards, and limits to cross border risk diversification. Bernd Schwaab, Siem Jan Koopman, André Lucas.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
The recent financial market turbulence caused considerable divergence in the banking market interest rate determination process of the euro area member countries (e.g. Illes and Lombardi 2013, Paries et al., 2014). The purpose of this study is to investigate the factors determining the banking market interest rates in the euro area countries during the pre-crisis and the post-crisis periods, and to highlight possible regional asymmetries in the interest rate determination processes. To this end, we employ a set of country specific factors, such as variables capturing macroeconomic conditions, financial risk and loans market conditions, together with common monetary policy factors at euro-zone level. Instead of using specific bank market interest rates, we base our analysis on the ECB’s harmonized cost of bank borrowing indicators of euro area members, in order to avoid cross-country and cross-product data heterogeneity. With the use of principal component analysis, we obtain a number of latent factors that describe unobserved movements in the cost of borrowing, originating either in certain Euro-zone regions or outside the euro area, or constitute common factors for all euro area members. Such factors are identified as macroeconomic conditions, financial risk, loans market conditions and euro area monetary policy variables. These obtained factors, are then used in order to estimate country specific structural equations of the cost of bank borrowing determination. Employing cluster analysis on the parameter coefficients of these models, we then identify euro area regions with similar characteristics regarding the determination of the cost of borrowing. Next, the member states are pooled within the regions identified and structural models are estimated for these regions. By comparing the estimated distinct regional models and the different dynamic effects of the latent factor shocks across the regions, we highlight the differences in the determination of the cost of bank borrowing between the euro-zone core and periphery, and how it has been evolved through the period of the 2007-9 global financial crisis and the subsequent euro area debt crisis.
HLEG thematic workshop on measuring economic, social and environmental resili...StatsCommunications
HLEG thematic workshop on Measuring economic, social and environmental resilience, 25-26 November 2015, Rome, Italy, More information at: http://oe.cd/StrategicForum2015
Accommodative monetary policy breathing space or breeding risks for emergin...Benjamin Huston
The document discusses a workshop on monetary policy spillovers and independence. It aims to examine how accommodative monetary policy in advanced economies may impact financial stability risks in emerging markets through various transmission channels. Specifically, it seeks to analyze potential correlations between financial cycles in advanced and emerging economies, assess how emerging markets responded to supportive monetary policies, and map macroprudential policy tools to different financial stability risks. Key challenges include generating long-term financial cycle data for emerging market countries.
This document summarizes a study that investigates the relationship between loan sizes and credit risk in the microfinance industry of sub-Saharan Africa. Using data on over 2000 annual observations from 632 microfinance institutions across 37 countries between 1995 and 2013, the study finds that credit risk is positively related to loan sizes. This contrasts with evidence from traditional banking, which typically finds an inverse relationship between loan sizes and risk. The results have implications for microfinance portfolio managers, particularly as mobile money services expand in the region.
Dan Andrews - Breaking the shackles:Zombie Firms, Weak Banks and Depressed Re...Structuralpolicyanalysis
1) The document discusses evidence that zombie firms, which are firms that are financially distressed but remain in operation, are more likely to be connected to weak banks.
2) It finds that zombie firms are more likely to be clients of banks that are in poorer financial health, as measured by various indicators of bank balance sheet strength. This is consistent with the hypothesis that weak banks continue to support zombie firms through forbearance to avoid realizing losses.
3) It also discusses how insolvency regimes that make corporate restructuring more difficult can strengthen banks' incentives to engage in forbearance with zombie firms. The negative relationship between bank health and zombie firms is stronger in countries with less restructuring-friendly insolvency
Lessons Learned from Implementing the Cybersecurity Capacity Maturity Model f...Carolin Weisser
This presentation was given by Prof Michael Goldsmith and Dr Patricia Esteve-González, both from the Global Cyber Security Capacity Centre (GCSCC), University of Oxford, at the 2020 Global Cybersecurity Capacity Building Conference in Melbourne, 18 February 2020.
The presentation includes:
- Mission, purpose and impact of the GCSCC
- Lessons learned from implementing the Cybersecurity Capacity Maturity Model for Nations (CMM) around the world
- The shaping and impacts of cybersecurity capacity: What is the status of cybersecurity capacity building? What factors are shaping capacity building within nations? What are the implications of capacity building for nations?
The document discusses debt sustainability analysis (DSA) and its practice in the Turkish Treasury. DSA aims to estimate future debt levels and test debt sustainability under adverse scenarios. The Turkish Treasury uses several models for DSA, including the conventional accounting approach (CAA), debt indicators module (DIM), and Turkish debt simulation model (TDSM). The TDSM is a stochastic model that generates forward-looking scenarios and assesses tail risks, providing a more robust analysis than CAA. Results are reported regularly to management and published to promote transparency.
Similar to Regulation et risque systémique - Monica Billio. April, 7-11 2013 (20)
Predicting the economic public opinions in EuropeSYRTO Project
Predicting the economic public opinions in Europe
Maurizio Carpita, Enrico Ciavolino, Mariangela Nitti
University of Brescia & University of Salento
SYRTO Project Final Conference, Paris – February 19, 2016
Scalable inference for a full multivariate stochastic volatilitySYRTO Project
Scalable inference for a full multivariate stochastic volatility
P. Dellaportas, A. Plataniotis and M. Titsias UCL(London), AUEB(Athens), AUEB(Athens)
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Network and risk spillovers: a multivariate GARCH perspectiveSYRTO Project
M. Billio, M. Caporin, L. Frattarolo, L. Pelizzon: “Network and risk spillovers: a multivariate GARCH perspective”.
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Clustering in dynamic causal networks as a measure of systemic risk on the eu...SYRTO Project
Clustering in dynamic causal networks as a measure of systemic risk on the euro zone
M. Billio, H. Gatfaoui, L. Frattarolo, P. de Peretti
IESEG/ Universitè Paris1 Panthèon-Sorbonne/ University Ca' Foscari
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
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Roberto Savona - Primary Coordinator of the SYRTO Project
University of Brescia
Final SYRTO Conference - Université Paris1 Panthéon-Sorbonne
February 19, 2016
Comment on:Risk Dynamics in the Eurozone: A New Factor Model forSovereign C...SYRTO Project
Comment on:Risk Dynamics in the Eurozone: A New Factor Model forSovereign CDS and Equity Returnsby Dellaportas, Meligkotsidou, Savona, Vrontos. Andre Lucas. Amsterda, June, 25 2015. Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models. Andre Lucas. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time...SYRTO Project
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Discussion of “Network Connectivity and Systematic Risk” and “The Impact of N...SYRTO Project
Discussion of “Network Connectivity and Systematic Risk” and “The Impact of Network Connectivity on Factor Exposures, Asset pricing and Portfolio Diversification” by Billio, Caporin, Panzica and Pelizzon. Arjen Siegmann. Amsterdam - June, 25 2015. European Financial Management Association 2015 Annual Meetings.
A Dynamic Factor Model: Inference and Empirical Application. Ioannis Vrontos SYRTO Project
The document describes a dynamic factor model to analyze how financial risks are interconnected within the Eurozone. It uses the model to examine risk dynamics using sovereign CDS and equity returns from 2007-2009 covering the US financial crisis and pre-sovereign crisis in Europe. The model relates asset returns to latent sector factors, macro factors, and covariates. Bayesian inference is applied using MCMC to estimate the time-varying parameters and latent factors.
Spillover dynamics for sistemic risk measurement using spatial financial time...SYRTO Project
Spillover dynamics for sistemic risk measurement using spatial financial time series models. Julia Schaumburg, Andre Lucas, Siem Jan Koopman, and Francisco Blasques. ESEM - Toulouse, August 25-29, 2014
http://www.eea-esem.com/eea-esem/2014/prog/viewpaper.asp?pid=1044
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Measuring the behavioral component of financial fluctuation: an analysis bas...
Regulation et risque systémique - Monica Billio. April, 7-11 2013
1. Regulation
et
risque systémique
SYstemic Risk TOmography:
Signals, Measurements, Transmission Channels, and
Policy Interventions
M.Billio,Ca’Foscari University ofVenice (ITALY)
M.Getmansky, Isenberg School ofManagement, University of Massachusetts (USA)
D.Gray,International Monetary Fund (IMF)
A.W. Lo,MIT Sloan School of Management (USA)
R.C.Merton,MIT Sloan School ofManagement (USA)
L. Pelizzon,Ca'Foscari University ofVenice (Italy)andGoethe University Frankfurt (Germany)
University ofOrléans – Paris. November 5, 2013.
2. Sovereign, Bank, and Insurance Credit Spreads:
Connectedness and System Networks
M. Billio, M. Getmansky, D. Gray
A.W. Lo, R.C. Merton, L. Pelizzon
The research leading to these results has received funding from the European
Union, Inquire Europe, and Seventh Framework Programme FP7/2007-2013
under grant agreement SYRTO-SSH-2012-320270.
Funded by the European Union
7th Framework Programme (FP7)
SYRTO
1
3. Objectives
• The risks of the banking and insurance
systems have become increasingly
interconnected with sovereign risk
• Highlight interconnections:
• Among countries and financial
institutions
• Consider both explicit and implicit
connections
2
4. Methodology
• We propose to measure and analyze
interactions between banks, insurers,
and sovereigns using:
– Contingent claims analysis (CCA)
– Network approach
3
5. Background
• Existing methods of measuring financial stability
have been heavily criticized by Cihak (2007) and
Segoviano and Goodhart (2009):
• A good measure of systemic stability has to
incorporate two fundamental components:
– The probability of individual financial
institution or country defaults
– The probability and speed of possible shocks
spreading throughout the financial industry
and countries
4
6. Background
• Most policy efforts have not focused in a
comprehensive way on:
– Assessing network externalities
– Interconnectedness between financial institutions,
financial markets, and sovereign countries
– Effect of network and interconnectedness on
systemic risk
5
7. Background: Feedback Loops of Risk
from Explicit and Implicit Guarantees
Source: IMF GFSR 2010, October Dale Gray
6
8. Background
• The size, interconnectedness, and complexity of
individual financial institutions and their inter-
relationships with sovereign risk create
vulnerabilities to systemic risk
• We use Expected Loss Ratios (based on CCA) and
network measures to analyze financial system
interactions and systemic risk
7
9. Core Concept of CCA:
Merton Model
• Expected Loss Ratio (ELR)
= Cost of Guar/RF Debt = PUT/B exp[-rT]
• Fair Value CDS Spread = -log (1 – ELR)/ T
8
10. Moody’s KMV CreditEdge for
Banks and Insurers
• MKMV uses equity and equity volatility and default barrier (from
accounting information) to get “distance-to- distress” which it maps
to a default probability (EDF) using a pool of 30 years of default
information
• It then converts the EDF to a risk neutral default probability (RNDP)
using the market price of risk, then using the sector loss given default
(LGD) it calculates the Expected Loss Ratio (ELR) for banks and
Insurers:
EL Ratio = RNDP*LGD=PUT/B exp[-rT]
9
11. Sovereign Expected Loss Ratio
• For this study the formula for estimating sovereign EL is
simply derived from sovereign CDS
EL Ratio Sovereign = 1-exp(-(Sovereign CDS/10000)*T)
• EL ratios for both banks and sovereigns have a horizon of 5
years (5-year CDS most liquid)
12. Linear Granger Causality Tests
ELRk (t) = ak + bk ELRk(t-1) + bjk ELRj(t-1) + Ɛt
ELRj(t) = aj + bj ELRj(t-1) + bkj ELRk(t-1) + ζt
• If bjk is significantly > 0, then j influences k
• If bkj is significantly > 0, then k influences j
• If both are significantly > 0, then there is
feedback, mutual influence, between j and
k.
11
13. Data
• Sample: Jan 01-Mar12
• Monthly frequency
• Entities:
– 17 Sovereigns (10 EMU, 4 EU, CH, US, JA)
– 59 Banks (31EMU, 11EU, 2CH, 12US, 4JA)
– 42 Insurers (12EMU, 6EU, 16US, 2CH, 5CA)
• CCA - Moody’s KMV CreditEdge:
– Expected Loss Ratios (ELR)
16. Network Measures
• Degrees
• Connectivity
• Centrality
•Indegree (IN): number of incoming connections
•Outdegree (FROM): number of outgoing
connections
•Totdegree: Indegree + Outdegree
•Number of node connected: Number
of nodes reachable following the
directed path
•Average Shortest Path: The average
number of steps required to reach the
connected nodes
•Eigenvector Centrality (EC): The more the
node is connected to central nodes (nodes
with high EC) the more is central (higher
EC)
31. t=March 2008; t+1=March 2009; t = Jul 2011; t+1= Feb 2012
Cumulated Exp. Loss Ratio ≡ Expected Loss Ratio of institution i +
Expected Loss Ratios of institutions caused by i
Early Warning Signals
Cumulative Expected Loss
Ratios
March 09 February 12
Coeff t-stat Coeff t-stat
# of out lines 0.42 2.92
Closeness Centrality -0.63 -2.51 -0.96 -6.40
R-Square 0.17 0.24
30
32. Conclusion
• The system of banks, insurance companies,
and countries in our sample is highly
dynamically connected
• We show how one sovereign/financial
institution is spreading risk to another
sovereign/financial institution
• Network measures allow for early warnings
and assessment of the system complexity
31
33. Implications
• The decision to bail out a bank or sovereign
affects not only the sovereign and its own
banks but also other sovereigns and foreign
banks in a significant way
• Stress tests are not adequate. Need to
account for interconnectedness and non-
linearity in exposures
32
35. This project has received funding from the European Union’s
Seventh Framework Programme for research, technological
development and demonstration under grant agreement n° 320270
www.syrtoproject.eu