Harry (Harald) Scheule is Professor of Finance at the University of Technology Sydney. His expertise is in the area of Banking, Credit and Liquidity Risk, Data Analytics, Housing Finance, Insurance, Portfolio Construction, Prudential Regulation, and Securities Valuation.
Harry is a strategic partner for banks and regulators in Asia-Paciifc, Europe and North America. He has had influence with financial institutions who have applied his work to improve their risk management practices. His award-winning research has been widely cited and published in leading journals. He currently serves on the editorial board of the Journal of Risk Model Validation.
Harry is a dedicated educator, who consistently receives excellent student feedback, and his PhD students have produced impactful industry research. His textbooks on credit risk analytics are used around the world in data analytics courses and include: "Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS" and "The R Companion".
Asset Pricing, Banking, Credit and Liquidity Risk, Home Equity Release, House Prices in Distress, Insurance, Mortgages, Prudential Regulation, Real Estate Finance, Securities Evaluation and Structured Finance
25751 Financial Institution Management
25574 Commercial Bank Management
25575 Investment Banking
Scheule, H., Rosch, D. & Baesens, B. 2017, Credit Risk Analytics: The R Companion, Amazon, USA.
This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.
Scheule, H. 2003, Prognose von Kreditausfallrisiken, Uhlenbruch Verlag, Germany.
Bui, C., Scheule, H. & Wu, E. 2017, 'The value of bank capital buffers in maintaining financial system resilience', Journal of Financial Stability, vol. 33, pp. 23-40.View/Download from: Publisher's site
Claussen, A., Löhr, S., Rösch, D. & Scheule, H. 2017, 'Valuation of systematic risk in the cross-section of credit default swap spreads', Quarterly Review of Economics and Finance, vol. 64, pp. 183-195.View/Download from: Publisher's site
© 2016 Board of Trustees of the University of Illinois.We analyze the pricing of systematic risk factors in credit default swap (CDS) contracts in a two-stage empirical framework. Firstly we estimate contract-specific sensitivities (betas) to several systematic risk factors by time-series regressions using quoted CDS spreads of 339 U.S. entities from January 2004 to December 2010. Secondly, we show that these contract-specific sensitivities are cross-sectionally priced in CDS spreads after controlling for individual risk factors. We find that the credit market climate, the Cross-market Correlation, and the market volatility explain CDS spread changes and that their corresponding sensitivities (betas) are particularly priced in the cross-section. Our basic risk factors explain about 83% (90%) of the CDS spreads prior to (during) the crisis.
© 2016 Elsevier B.V.This study examines the relationship between funding liquidity and bank risk taking. Using quarterly data for U.S. bank holding companies from 1986 to 2014, we find evidence that banks having lower funding liquidity risk as proxied by higher deposit ratios, take more risk. A reduction in banks' funding liquidity risk increases bank risk as evidenced by higher risk-weighted assets, greater liquidity creation and lower Z-scores. However, our results show that bank size and capital buffers usually limit banks from taking more risk when they have lower funding liquidity risk. Moreover, during the Global Financial Crisis banks with lower funding liquidity risk took less risk. The findings of this study have implications for bank regulators advocating greater liquidity and capital requirements for banks under Basel III.
Kellner, R., Roesch, D. & Scheule, H. 2016, 'Analyzing Model Risk of Methods from Extreme Value Theory - Implications for Solvency Capital Requirements', The Journal of Risk, vol. 18, no. 6, pp. 39-70.
Lee, Y., Roesch, D. & Scheule, H. 2016, 'Accuracy of mortgage portfolio risk forecasts during financial crises', European Journal of Operational Research, vol. 249, no. 2, pp. 440-456.View/Download from: UTS OPUS or Publisher's site
This paper explores whether factor based credit portfolio risk models are able to predict losses in severe economic
downturns such as the recent Global Financial Crisis (GFC) within standard confidence levels. The paper
analyzes (i) the accuracy of default rate forecasts, and (ii) whether forecast downturn percentiles (Valueat-Risk,
VaR) are sufficient to cover default rate outcomes over a quarterly and an annual forecast horizon.
Uninformative maximum likelihood and informative Bayesian techniques are compared as they imply different
degrees of uncertainty.
We find that quarterly VaR estimates are generally sufficient but annual VaR estimates may be insufficient
during economic downturns. In addition, the paper develops and analyzes models based on auto-regressive
adjustments of scores, which provide a higher forecast accuracy. The consideration of parameter uncertainty
and auto-regressive error terms mitigates the shortfall.
© 2015 Institutional Investor LLC. All Rights Reserved. This article analyzes the sensitivity to systematic credit risk and pricing in fixed income instruments and compares corporate bonds and asset securitizations. The article finds crosssectional variation of systematic credit risk given the same credit rating and a market premium for the systematic risk embedded in yield spreads. Therefore, credit ratings do not provide comprehensive information on the degree of systematic risk, and investors are compensated for such differences in systematic risk after controlling for credit ratings and other risk characteristics.
Rösch, D. & Scheule, H. 2016, 'The role of loan portfolio losses and bank capital for Asian financial system resilience', Pacific Basin Finance Journal, vol. 40, pp. 289-305.View/Download from: Publisher's site
This paper analyses the systemic risk in relation to bank lending for Asian economies. The methodology complements existing market-based systemic risk measures by providing measures based on accounting information that regulators typically collect. Loan loss provisions of banks are decomposed into (i) a prediction component that is based on observable bank characteristics, and (ii) two frailty components: a bank-specific systematic factor based on the assumption that a bank's asset portfolio is diversified and a systemic factor. Systemic risk is measured as the Value-at-Risk and Expected Shortfall of the financial system based on a simulation model that takes into account the current condition of banks in the financial system, the absolute size and the capitalisation of financial institutions, as well as the sensitivity to systematic and systemic frailty risk.
Scheule, H., Kellner, R. & Rösch, D. 2016, 'The role of model risk in extreme value theory for capital adequacy', Journal of Risk, vol. 18, no. 6, pp. 39-70.View/Download from: UTS OPUS or Publisher's site
Jobst, R., Roesch, D., Scheule, H. & Schmelzle, M. 2015, 'A Simple Econometric Approach for Modeling Stress Event Intensities', Journal of Futures Markets, vol. 35, no. 4, pp. 300-320.View/Download from: Publisher's site
Luetzenkirchen, K., Roesch, D. & Scheule, H. 2014, 'Asset portfolio securitizations and cyclicality of regulatory capital', European Journal Of Operational Research, vol. 237, no. 1, pp. 289-302.View/Download from: Publisher's site
Roesch, D. & Scheule, H. 2014, 'Forecasting mortgage securitization risk under systematic risk and parameter uncertainty', Journal of Risk and Insurance, vol. 81, no. 3, pp. 563-586.View/Download from: Publisher's site
The global financial crisis exposed financial institutions to severe unexpected losses in relation to mortgage securitizations and derivatives. This article finds that risk models such as ratings are exposed to a large degree of systematic risk and parameter uncertainty. An out-of-sample forecasting exercise of the financial crisis shows that a simple approach addressing both issues is able to produce ranges for risk measures consistent with realized losses. This explains how financial markets were taken by surprise in relation to realized losses.
Roesch, D. & Scheule, H. 2014, 'Forecasting probabilities of default and loss rates given default in the presence of selection', Journal of the Operational Research Society, vol. 65, no. 3, pp. 393-407.View/Download from: UTS OPUS or Publisher's site
Bodenstedt, M., Roesch, D. & Scheule, H. 2013, 'The path to impairment: Do credit-rating agencies anticipate default events of structured finance transactions?', European Journal of Finance, vol. 19, no. 9, pp. 841-860.View/Download from: UTS OPUS or Publisher's site
The global financial crisis (GFC) has led to a general discussion of the accuracy and declining standards of credit-rating agency ratings. Substantial criticism has been directed towards the securitisation market, which has been identified as one of the main sources of the crisis. This study focuses on the ability of rating agencies to adjust their ratings prior to impairments of structured finance transactions. We develop a new measure that quantifies a rating agency's performance in advance of defaults. By analysing a large number of impaired transactions rated by Moody's Investors Service, we find that rating quality deteriorated during the GFC. Furthermore, we identify tranche-specific and macroeconomic factors that explain differences in Moody's performance.
Loehr, S., Mursajew, O., Roesch, D. & Scheule, H. 2013, 'Dynamic implied correlation modeling and forecasting in structured finance', Journal of Futures Markets, vol. 33, no. 11, pp. 994-1023.View/Download from: UTS OPUS or Publisher's site
Correlations are the main drivers for credit portfolio risk and constitute a major element in pricing credit derivatives such as synthetic single-tranche collateralized debt obligation swaps. This study suggests a dynamic panel regression approach to model and forecast implied correlations. Random effects are introduced to account for unobservable time-specific effects on implied tranche correlations. The implied-correlation forecasts of tranche spreads are compared to forecasts using historical correlations from asset returns. The empirical findings support our proposed dynamic mixed-effects regression correlation model.
Luetzenkirchen, K., Roesch, D. & Scheule, H. 2013, 'Ratings based capital adequacy for securitizations', Journal of Banking and Finance, vol. 37, no. 12, pp. 5236-5247.View/Download from: UTS OPUS or Publisher's site
This paper develops a framework to measure the exposure to systematic risk for pools of asset securitizations and measures empirically whether current ratings-based rules for regulatory capital of securitizations under Basel II and Basel III reflect this exposure. The analysis is based on a comprehensive US dataset on asset securitizations for the time period between 2000 and 2008. We find that the shortfall of regulatory capital during the Global Financial Crisis is strongly related to ratings. In particular, we empirically show that insufficient capital is allocated to tranches with the highest rating. These tranches account for the greatest part of the total issuance volumes. Furthermore, this paper is the first to calibrate risk weights which account for systematic risk and provide sufficient capital buffers to cover the exposure during similar economic downturns. These policy-relevant findings suggest a re-calibration of RBA risk weights and may contribute to the current efforts by the Basel Committee on Banking Supervision and others to re-establish sustainable securitization markets and to improve the stability of the financial system.
Roesch, D. & Scheule, H. 2012, 'Forecasting Mortgage Securitization Risk under Systematic Risk and Parameter Uncertainty'.
The Global Financial Crisis exposed financial institutions to severe unexpected losses in relation to mortgage securitizations and derivatives. This paper analyzes a unique and extensive ratings and impairment events database for securitizations. The paper finds that risk models such as ratings are exposed to a large degree of systematic risk and parameter uncertainty. An out-of-sample forecasting exercise of the financial crisis shows that a simple approach addressing both issues is able to produce ranges for risk measures consistent with realized losses. This explains how financial markets were taken by surprise in relation to realized losses.
Lützenkirchen, K., Rösch, D. & Scheule, H. 2012, 'Angemessenheit von regulatorischen Kapitalanforderungen für Verbriefungstransaktionen', Frankfurter Institut für Risikomanagement und Regulierung, pp. 93-94.
This paper analyzes the capital incentives and adequacy of financial institutions for asset portfolio securitizations. The empirical analysis is based on US securitization rating and impairment data. The paper finds that regulatory capital rules for securitizations may be insufficient to cover implied losses during economic downturns such as the Global Financial Crisis. In addition, the rating process of securitizations provides capital arbitrage incentives for financial institutions and may further reduce regulatory capital requirements. These policy-relevant findings assume that the ratings assigned by rating agencies are correct and can be used to build a test for the ability of Basel capital regulations to cover downturn losses.
Bade, B., Roesch, D. & Scheule, H. 2011, 'Empirical Performance of LGD Prediction Models', Journal of Risk Model Validation, vol. 5, no. 2.
The Global Financial Crisis highlighted that default and recovery rates of multiple borrowers generally deteriorate jointly during economic downturns. The vast majority of the literature, as well as many industry credit portfolio risk models ignore this and analyze default probabilities and recoveries in the event of default separately. As a result, the models project losses which are too low in economic downturns such as the recent financial crisis. Nevertheless, alternatives of incorporating the dependence between probabilities of default and recovery rates have been proposed. This paper is the first of its kind to assess the performance of these structurally different approaches. Four banks using different estimation procedures are compared. We use RMSE and RAE to measure the predictive accuracy of each procedure. The results show, that indeed models accounting for the correlation of default and recovery perform better than models ignoring it.
Bade, B., Roesch, D. & Scheule, H. 2011, 'Empirical performance of loss given default prediction models', Journal of Risk Model Validation, vol. 5, no. 2, pp. 25-44.View/Download from: UTS OPUS or Publisher's site
Bade, B., Roesch, D. & Scheule, H. 2011, 'Default and recovery risk dependencies in a simple credit risk model', European Financial Management, vol. 17, no. 1, pp. 120-144.View/Download from: UTS OPUS or Publisher's site
This paper provides evidence for the relationship between credit quality, recovery rate, and correlation. The paper finds that rating grade, rating shift, and macroeconomic factors provide a highly significant explanation for default risk and recovery risk of US bond issues. The empirical data suggest that default and recovery processes are highly correlated. Therefore, a joint approach is required for estimating time-varying default probabilities and recovery rates that are conditional on default. This paper develops and applies such a model
Chan, H., Faff, R.W., Hill, P. & Scheule, H. 2011, 'Are watch procedures a critical informational event in the credit ratings process? An empirical investigation', Journal of Financial Research, vol. 34, no. 4, pp. 617-640.View/Download from: UTS OPUS or Publisher's site
The Boot, Milbourn, and Schmeits (2006) model (Boot model) predicts certain credit rating events are likely to be more informative than others and that credit watch procedures are an important driver of such differences. We test the core empirical predictions of their model. Our sample comprises U.S. corporate issuer credit ratings provided by Moodys, 19902006. Our findings fail to uncover compelling evidence for the empirical predictions of the Boot model in relation to the role of watch procedures as coordinating mechanisms. Rather, our findings are more supportive of the view that rating agencies are always at an informational advantage relative to investors.
Claussen, A., Löhr, S., Lützenkirchen, K., Scheule, H. & Rösch, D. 2011, 'Credit Ratings und Kapital für Verbriefungstransaktionen', Risiko-manager, vol. 9, pp. 20-21.
Bade, B., Roesch, D. & Scheule, H. 2010, 'Default and Recovery Risk Dependencies in a Simple Credit Risk Model', European Financial Management, vol. 17, no. 1.
This paper provides evidence for the relationship between credit quality, recovery rate, and correlation. The paper finds that rating grade, rating shift, and macroeconomic factors provide a highly significant explanation for default risk and recovery risk of US bond issues. The empirical data suggest that default and recovery processes are highly correlated. Therefore, a joint approach is required for estimating time-varying default probabilities and recovery rates that are conditional on default. This paper develops and applies such a model.
Roesch, D. & Scheule, H. 2010, 'Downturn credit portfolio risk, regulatory capital and prudential incentives', International Review of Finance, vol. 10, no. 2, pp. 185-207.View/Download from: UTS OPUS or Publisher's site
This paper analyzes the level and cyclicality of bank capital requirement in relation to (i) the model methodologies through-the-cycle and point-in-time, (ii) four distinct downturn loss rate given default concepts, and (iii) US corporate and mortgage loans. The major finding is that less accurate models may lead to a lower bank capital requirement for real estate loans. In other words, the current capital regulations may not support the development of credit portfolio risk measurement models as these would lead to higher capital requirements and hence lower lending volumes. The finding explains why risk measurement techniques in real estate lending may be less developed than in other credit risk instruments. In addition, various policy recommendations for prudential regulators are made.
Roesch, D. & Scheule, H. 2009, 'Credit portfolio loss forecasts for economic downturns', Financial Markets, Institutions and Instruments, vol. 18, no. 1, pp. 1-26.View/Download from: UTS OPUS or Publisher's site
Recent studies find a positive correlation between default and loss given default rates of credit portfolios. In response, financial regulators require financial institutions to base their capital on `Downturn loss rates given default which are also known as Downturn LGDs. This article proposes a concept for the Downturn LGD which incorporates econometric properties of credit risk as well as the information content of default and loss given default models. The concept is compared to an alternative proposal by the Department of the Treasury, the Federal Reserve System and the Federal Insurance Corporation. An empirical analysis is provided for US American corporate bond portfolios of different credit quality, seniority and security.
One of the most significant developments in international credit markets in recent years has been the trade in Collateralized Debt Obligations (CDO), which has enabled financial institutions to repackage the credit risk of an asset portfolio into tranches to be transferred to investors. The present paper evaluates the credit risk of such a portfolio and the related tranches by applying two prominent prototypes for credit ratings, namely the point-in-time and through-the-cycle approach. The central parameters default probability and correlation are forecast for multiple years and related forecasting errors are included. The article's main findings are that banks which transfer debt tranches but retain an equity part and apply a through-the-cycle rating approach may be exposed to higher insolvency risk. Firstly, the credit risk retained may be underestimated resulting in an inadequate capital allocation. Secondly, the credit risk transferred may be overestimated resulting in additional risk-based transfer costs.
Recent studies find a positive correlation between default and loss given default (LGD) rates for credit portfolios. In response, financial regulators require financial institutions to base their capital on the downturn loss rate given default, which is also known as downturn LGD. This paper compares alternative concepts for the downturn LGD of Hong Kong mortgage loan portfolios.
Hamerle, A., Liebig, T. & Scheule, H. 2006, 'Forecasting credit event frequency - Empirical evidence for West German firms', The Journal of Risk, vol. 9, no. 1, pp. 75-98.
Rauhmeier, R. & Scheule, H. 2005, 'Eigenschaften von Ratings und ihre Auswirkung suf die Kapitalanforderung nach Basel II', Deutsches Risk, vol. 5, pp. 34-40.
Rauhmeier, R. & Scheule, H. 2005, 'Rating properties and their implications for Basel II capital', Risk, vol. 18, no. 3, pp. 78-81.
Scheule, H., Berthold, N. & Lingenfelder, M. 2005, 'Bewertung von Kreditportfoliorisiken', WiSt - Wirtschaftswissenschaftliches Studium, vol. 34, no. 9, pp. 538-544.View/Download from: Publisher's site
Scheule, H. 2003, 'Die Auswirkung der Ratingqualität auf das Basel II-Eigenkapital', Zeitschrift für das gesamte Kreditwesen, vol. 56, no. 15, pp. 837-839.
Boegelein, L., Hamerle, A., Rauhmeier, R. & Scheule, H. 2002, 'Parametrisierung von CreditRisk+ im Konjunkturzyklus: Dynamische Ausfallquoten und Sektorenanalyse', Deutsches Risk, vol. 2, no. 2, pp. 37-42.
Boegelein, L., Hamerle, A., Scheule, H. & Rauhmeier, R. 2002, 'Modelling Default Rate Dynamics in the CreditRisk+ Framework', Risk, vol. 15, no. 10, pp. S24-S28.
Scheule, H. 2002, 'Credit Risk and Taxes: A Shareholder Value Analysis', Journal of Risk Management, vol. 4, no. 1, pp. 77-89.
Scheule, H. 2001, 'Kreditbewertung im deutschen Steuersystem', Zeitschrift fur das gesamte Kreditwesen, vol. 54, no. 3, pp. 127-130.
Bodenstedt, M., Rösch, D. & Scheule, H. 2015, 'The path to impairment: Do credit-rating agencies anticipate default events of structured finance transactions?' in Contemporary Issues in Financial Institutions and Markets, Routledge, UK, pp. 31-50.View/Download from: UTS OPUS
© 2015 Taylor & Francis. All rights reserved.The global financial crisis (GFC) has led to a general discussion of ihe accuracy and declining standards of credit-rating agency ratings. Substantial criticism has been directed towards the securitisation market, which has been identified as one of the main sources of the crisis. This study focuses on the ability of rating agencies to adjust their ratings prior to impairments of structured finance transactions. We develop a new measure that quantifies a rating agency's performance in advance of defaults. By analysing a large number of impaired transactions rated by Moody's Investors Service, we find that rating quality deteriorated during the GFC. Furthermore, we identify tranche-specific and macroeconomic factors that explain differences in Moody's performance.
Luetzenkirchen, K., Roesch, D. & Scheule, H. 2013, 'Regulatory capital requirements for securitizations' in Roesch, D. & Scheule, H. (eds), Credit Securitisations and Derivatives: Challenges for the Global Markets, John Wiley & Sons, Australia, pp. 343-356.View/Download from: UTS OPUS or Publisher's site
Asset securitizations are one of the most significant developments in financial intermediation in recent years. Financial institutions use vehicles such as asset-backed securities (ABSs), collateralized debt obligations (CDOs) or mortgage-backed securities (MBSs) to restructure the asset risks of their portfolios and transfer these to investors. Under regulations which are currently implemented, banks may apply the following three approaches: at present two different ways for financial institutions that have received the approval to use the IRB Approach to determine regulatory capital for securitized assets are provided: the Ratings Based Approach (RBA) and Supervisory Formula Approach (SFA). Non-IRB banks (banks that use the Standardized Approach (SA) for their calculations of regulatory capital for their credit exposures) are required to apply the SA to calculate capital requirements for their securitization exposures. The SA is also based on external ratings but is less sophisticated than the RBA approach.
Roesch, D. & Scheule, H. 2013, 'Credit securitizations and derivatives' in Rösch, D. & Scheule, H. (eds), Credit Securitisations and Derivatives: Challenges for the Global Markets, Wiley, Australia, pp. 3-9.View/Download from: Publisher's site
This is the introductory chapter of Credit Securitisations and Derivatives, which provides regulators with an overview of the risk inherent in credit securitizations and derivatives. The book aims to help quantitative analysts improve risk models and managers of financial institutions evaluate the performance of existing risk models and future model needs. The book addresses challenges in relation to the evaluation of credit portfolio securitizations and derivatives. It covers the following areas: credit portfolio risk measurement, credit portfolio risk tranching, credit ratings, credit default swaps, indices and tranches, counterparty credit risk and clearing of derivatives contracts, liquidity risk, and regulation.
Roesch, D. & Scheule, H. 2010, 'Downturn model risk: Another view of the global financial crisis' in Scheule, H. & Roesch, D. (eds), Model risk - Identification, measurement and management, Risk Books, London, UK, pp. 3-18.View/Download from: UTS OPUS
Researchers and practitioners have spent ample resources modelling credit, explaining correlations between risk models as well as inputs and outputs. One popular example is asset correlation, which describes the co-movement between the asset value returns of corporate borrowers or issuers. Other examples are default correlations, correlations between default and recovery processes and correlations between risk categories such as credit, interest, liquidity or market risk. In statistical terms, correlations are often placeholders for relationships which cannot be explained and are also known as "seeming correlations". The 2008-9 global financial crisis caught us by surprise and showed that, starting with US subprime mortgage markets, other markets such as equity, credit and commodity markets have declined globally. These links have not been included into existing risk models, and this chapter identifies these links and shows . how to address these relationships in risk models.
Roesch, D. & Scheule, H. 2008, 'Integrating stress-testing frameworks' in Roesch, D. & Scheule, H. (eds), Stress testing for financial institutions: Applications, regulations and techniques, Risk Books, London, UK, pp. 3-15.View/Download from: UTS OPUS
Bank regulators (compare Basel Committee on Banking Supervision 2006) expect financial institutions to provide sufficient Tier I and Tier II capital to cover future worst-case credit portfolio losses. These worst-case losses are based on conservative assumptions for a set of parameters such as the probability of default (PD), asset correlation, loss given default (LGD) or exposure at default (EAD) Stress of PD: probability of default is based on a one factor, non-linear model where the factor equals the 99.9th percentile of a systematic standard normally distributed variable and the sensitivity is based on the so-called asset correlation . Stress of EAD and LGD: EAD and LGD are modelled based on economic downturn conditions.
Khan, M.S., Scheule, H. & Wu, E. 2015, 'The Impact of Bank Liquidity on Bank Risk Taking: Do High Capital Buffers and Big Banks Help or Hinder?'.
This study examines the impact of bank liquidity on bank risk taking. Using quarterly data for U.S. bank holding companies from 1986 to 2014 we find evidence to support that more liquid banks take more risk. This key result is robust for alternative bank risk and liquidity proxies, including some new liquidity measures advocated under the Basel III regulatory framework. An increase in banks' short-term liquidity increases banks' non-performing assets, risk-weighted assets and stock return volatility. The relation is stronger for banks with high capital buffers and in the high liquidity post-GFC era. However, our results show that bank size usually limits banks from taking more risk when they are flushed with liquidity but this was not the case during the more recent post-GFC high liquidity sub-period. The findings of this study have implications for bank regulators advocating greater liquidity and capital requirements for banks under Basel III.
Roesch, D. & Scheule, H. 2012, 'Systematic risk and credit ratings', The Seventh Annual Conference on AsiaâPacific Financial Markets (CAFM) of the Korean Securities Association (KSA), Seoul, South Korea.
Scheule, H. 2012, 'Systematic risk and credit ratings', Methods in International Finance Network (MIFN) Conference, Sydney, Australia.
Scheule, H. 2011, 'Systematic risk and credit ratings: How bonds and mortgage securitizations are different', Quantitative Methods in Finance 2011 Conference, Sydney Australia.
Roesch, D. & Scheule, H.H. 2010, 'Securitization Rating Performance and Agency Incentives'.
Roesch, D. & Scheule, H.H. 2009, 'Rating Performance and Agency Incentives of Structured Finance Transactions'.
Lee, Y., Roesch, D. & Scheule, H. 2014, 'Decomposing the Smile: Systematic Credit Risk in Mortgage Portfolios'.
This study analyzes systematic and non-systematic credit risk in mortgage portfolios given US loan-level information by controlling for time-varying observable information in relation to the borrower, the collateral and the macro economy. The total risk in relation to rating class default rates is decomposed into systematic and class-specific non-systematic risk by a state space model. The paper finds that the total risk relates to credit quality in a smile-shaped pattern: systematic risk is negatively related and non systematic risk is positively related to average default rate levels. In addition, total risk increases during and after the Global Financial Crisis. The impact of the crisis on systematic risk is persistent whereas the impact on non-systematic risk appears to be temporary. The analysis of regulatory capital suggests that mortgage risk models in conjunction with periodic updating warrant a sufficient level of regulatory capital given the current regime. These findings are relevant to prudential regulators who are currently discussing the implementation of a monotone relationship between default probabilities and asset correlations under Basel III.
Roesch, D. & Scheule, H. 2014, 'Systemic Risk in Commercial Bank Lending'.
This paper develops a bank model for financial systemic risk in bank lending. The model analyzes the impact of a financial institution failure on the distribution of losses in the financial system. The fundamental idea is that bank loss rates may be decomposed into a level, momentum, systematic and systemic component. Financial institutions fail when unexpected losses exceed the capital buffer and the release of capital allocated to credits. Failed financial institutions pass these loss exceedances on to creditors, deposit insurance schemes or the general public. The benefits of the presented model framework are (i) the identification of systemically relevant financial institutions, and (ii) the measurement of the size of safety nets in terms of attachment likelihood and expected losses given attachment. The model is generally applicable as it does not rely on financial market data. The empirical evidence presented is based on information collected by US prudential regulators from 1997 to 2012. The parameter estimation is based on a novel maximum likelihood technique to derive the parameters in a non-linear mixed model with multiple random effects.
Roesch, D., Scheule, H. & Silvapulle, P. 2013, 'Modelling and Predicting of Australian Mortgage Delinquency Risk: A Preliminary Data Analysis'.
This paper employs the parametric probit regression model, estimates the probability of default (PD) of Australian mortgages, and examines the nature of the relationships between the PD and some loan level variables such as loan-to-value ratio (LVR), loan documentation, loan type, loan purpose, and state. The data covers a cross-section of 25,537 mortgage loans, which were originated in the years 2004 to 2010. The data set has 694 default events defined by the delinquency of the mortgage borrower. In this preliminary analysis, we find that the parametric model specification does not capture the underlying relationships between the dependent variable PD and the other variables included in the model. In addition, we find that the PD and the LVR, which is known to be a key determinant of mortgage default, have a nonlinear relationship that is not fully captured by the probit model. Despite many forms of parametric nonlinear models being available in the literature, the process of finding a suitable parametric nonlinear model may not lead to a model that would capture the true nonlinear relationship between the PD and LVR. To overcome this problem, in our future research, we will assume an unknown functional form for this relationship, and then propose an estimation method for this semi parametric probit model. Based on the overall findings of our preliminary analysis, we provide a roadmap for the future research directions on robust modelling and predicting the PD of Australian mortgages, and for the need to expand the size of the data and the variables sets.
Roesch, D. & Scheule, H.H. 2012, 'Forecasting Probabilities of Default and Loss Rates Given Default in the Presence of Selection'.
Roesch, D. & Scheule, H. 2011, 'Downturn Risk: Another View on the Current Financial Crisis'.
The current financial crisis had its origins in the US subprime mortgage market and led to downturns in global equity, credit and commodity markets. This paper identifies the lack of economic information in risk valuation models as one reason why the financial industry was unable to predict, mitigate and cover the current losses. This is at first sight rather surprising as credit and credit derivative products have existed for centuries. However, the markets have experienced an exponential growth in size as well as variety. In particular, the associated transparency may have not matched this development in relation to the underlying risks, risk models and model risks.
Roesch, D. & Scheule, H. 2011, 'Securitization Rating Performance and Agency Incentives'.
This paper provides an empirical study, which assesses the historical performance of credit rating agency (CRA) ratings for securitizations before and during the financial crisis. The paper finds that CRAs do not sufficiently address the systematic risk of the underlying collateral pools as well as characteristics of the deal and tranche structure in their ratings. The paper also finds that impairment risk is understated during origination years and years with high securitization volumes when CRA fee revenue is high. The mismatch between credit ratings of securitizations and their underlying risks has been suggested as one source of the Global Financial Crisis, which resulted in the criticism of models and techniques applied by CRAs and misaligned incentives due to the fees paid by originators.
Roesch, D. & Scheule, H. 2010, 'Rating Performance and Agency Incentives of Structured Finance Transactions'.
The mismatch between credit ratings o fstructured finance transactions and their true risks has been a source of the Global Financial Crisis which manifested in criticism of models and techniques applied by credit rating agencies (CRA). This paper provides an empirical study which assesses the historical performance of credit ratings for structured finance transactions and finds that CRAs do not include all factors explaining securitization impairment risk. In addition, CRA ratings for selected asset categories underestimate risk in origination years when the fee revenue is high.
Roesch, D. & Scheule, H. 2009, 'The Empirical Relation between Credit Quality, Recovery, and Correlation'.
The majority of industry credit portfolio risk models, as well as recent scientific results, are based on isolated modules for default probabilities and recoveries in the event of default. This paper shows that these common methods lead to various econometric drawbacks when the parameters are interpreted and aggregated for risk capital allocation and pricing purposes. This paper provides a top down approach in which individual credit risk parameters are derived analytically from a single model. This model allows for a i) dynamic, ii) consistent, and iii) unbiased modeling of credit portfolio risks. An empirical analysis provides evidence for the inferred relationship between credit quality, recovery and correlation.
Scheule, H. 2008, 'Credit Losses in Economic Downturns - Empirical Evidence for Hong Kong Mortgage Loans'.
Recent studies find a positive correlation between default and loss given default rates of credit portfolios. In response, financial regulators require financial institutions to base their capital on the 'Downturn' loss rate given default which is also known as Downturn LGD. This article proposes a concept for the Downturn LGD which incorporates econometric properties of credit risk as well as the information content of default and loss given default models. The concept is compared to an alternative proposal by the Department of the Treasury, the Federal Reserve System and the Federal Insurance Corporation. An empirical analysis is provided for Hong Kong mortgage loan portfolios.
FINSIA Retail and Business Banking Council
Global Association of Risk Professionals
Hong Kong Institute for Monetary Research