nep-rmg New Economics Papers
on Risk Management
Issue of 2016‒07‒16
eleven papers chosen by

  1. Estimation and prediction of credit risk based on rating transition systems By Jinghai Shao; Siming Li; Yong Li
  2. Total assets versus risk weighted assets: does it matter for MREL? By Bennet Berger; Pia Hüttl; Silvia Merler
  3. Should AMA be Replaced with SMA for Operational Risk? By Gareth W. Peters; Pavel V. Shevchenko; Bertrand Hassani; Ariane Chapelle
  4. An Analysis of Simultaneous Company Defaults Using a Shot Noise Process By Masahiko Egami; Rusudan Kevkhishvili
  5. Global credit risk: world country and industry factors By Schwaab, Bernd; Koopman, Siem Jan; Lucas, André
  6. Pricing sovereign credit risk of an emerging market By Camba-Méndez, Gonzalo; Kostrzewa, Konrad; Marszal, Anna; Serwa, Dobromil
  7. Rating models: emerging market distinctions By Alexander Karminsky
  8. Deep Learning for Mortgage Risk By Justin Sirignano; Apaar Sadhwani; Kay Giesecke
  9. Strengthening insurance partnerships in the face of climate change – insights from an agent-based model of flood insurance in the UK By Florence Crick; Katie Jenkins; Swenja Surminski
  10. LTV policy as a macroprudential tool: The case of residential mortgage loans in Asia By Morgan, Peter; Regis, Paulo José; Salike, Nimesh
  11. Solving commitment problems in disaster risk finance By Clarke,Daniel Jonathan; Wren-Lewis,Liam

  1. By: Jinghai Shao; Siming Li; Yong Li
    Abstract: Risk management is an important practice in the banking industry. In this paper we develop a new methodology to estimate and predict the probability of default (PD) based on the rating transition matrices, which relates the rating transition matrices to the macroeconomic variables. Our method can overcome the shortcomings of the framework of Belkin et al. (1998), and is especially useful in predicting the PD and doing stress testing. Simulation is conducted at the end, which shows that our method can provide more accurate estimate than that obtained by the method of Belkin et al. (1998).
    Date: 2016–07
  2. By: Bennet Berger; Pia Hüttl; Silvia Merler
    Abstract: Please see the PDF version of the paper for footnotes, references, and appendices. Highlights The European Union’s Bank Recovery and Resolution Directive foresees a ‘minimum requirement for own funds and eligible liabilities’ (known as MREL) that banks need to comply with in order to ensure the effectiveness of the bail-in tool. The details of how MREL should be constructed in practice are under discussion. We look at alternative ways to compute MREL, showing how the choice of the benchmark metric (risk weighted assets, total assets or leverage exposure) can change the allocation of requirements across banks. We also review MREL in light of the global effort to ensure future resolvability of banks, highlighting some differences with, and inconsistencies in relation to, the Financial Stability Board’s total loss-absorption capacity (TLAC) measure. 1 Introduction The financial and euro-area crises showed how costly it can be for the public sector to take charge of banking sector problems. Between 2007 and 2013, European Union governments provided €836 billion to guarantee bank funding and €448 billion to recapitalise banks1. The Bank Recovery and Resolution Directive (BRRD) was introduced to establish a new framework for resolving banks with reduced involvement of taxpayers in bank rescues. The backbone of the new approach is the bail-in tool, which requires a greater share of the cost of recapitalisation or resolution to be shifted onto private creditors. For bail-in to be effective, the BRRD foresees a minimum requirement for eligible liabilities and own funds (MREL) that banks need to comply with. Effective resolution of banks is however a global priority, and the Financial Stability Board (FSB) set in 2011 a global standard for total loss absorption capacity (TLAC), applying to global systemically important banks (G-SIBs), which needs to be transposed into EU law. How can the design of MREL be made consistent with both TLAC and the requirements of the BRRD? The two concepts have significant conceptual and operational differences and there is a strong rationale for harmonisation, to avoid creating confusion and uncertainty. We briefly review the differences and comment specifically on the choice of the measure through which requirements are expressed - risk-weighted assets or total assets. 2 MREL and TLAC - the background Before embarking on the data analysis, it is useful to briefly review the regulatory background to MREL. Article 45 of the Bank Recovery and Resolution Directive (BRRD) requires that banks hold sufficient bail-in-able liabilities and meet at all times a minimum requirement for own funds and eligible liabilities (MREL). MREL is currently envisaged as a Pillar 2 measure2, ie not a minimum standard but one set individually for each bank. While the concept of MREL is defined in the BRRD, its operational definition is left to the European Banking Authority (EBA)3, which published Regulatory Technical Standards (RTS) on 3 July 2015. These set out an MREL measure that combines a loss-absorption amount and a recapitalisation amount (Figure 1). The first component needs to be sufficient to ensure that losses are absorbed. The EBA argues that the regulatory capital requirements reflect the judgement of the supervisor about the level of unexpected losses that an institution should be able to absorb, so as a baseline, losses equal to capital requirements should be absorbed. Combined buffer requirements foreseen in the Capital Requirements Directive (CRDIV) could be added as could any existing Pillar 2 requirements. The EBA RTS leave discretion to the resolution authority to change these requirements, subject to consultation with the supervisor. In particular, MREL can be adjusted based on the estimated contribution of the Deposit Guarantee Scheme, or to reflect specific features of the institutions, such as business model risk profile or governance. Figure 1 - MREL according to EBA RTS Source - Bruegel, based on EBA RTS. The second component is a recapitalisation amount, which should ensure the institution is able to re-enter the market. For those institutions that can be liquidated credibly and safely, the EBA argues that the recapitalisation amount should be zero. If this is not the case, then the recapitalisation amount should at least enable institutions to comply with the minimum criteria required to obtain the supervisor’s authorisation to operate, so an 8 percent total capital ratio. However, the resolution authority can increase this, if deemed necessary to “maintain sufficient market confidence after resolution” (EBA, 2016). For systemically important institutions – which are unlikely to be easily liquidated or resolved without the use of external funds – the draft RTS require the resolution authority to confirm, as part of its assessment of MREL, that the bank’s resolution plan is compatible with the ‘burden sharing’ clause of the BRRD (Article 44(5)), which prescribes a bail-in amount of 8 percent of total liabilities before any external funds can be accessed.
    Date: 2016–07
  3. By: Gareth W. Peters; Pavel V. Shevchenko; Bertrand Hassani; Ariane Chapelle
    Abstract: Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of SMA such as instability, risk insensitivity, super-additivity and the implicit relationship between SMA capital model and systemic risk in the banking sector. We also discuss the issues with closely related operational risk Capital-at-Risk (OpCar) Basel Committee proposed model which is the precursor to the SMA. In conclusion, we advocate to maintain the AMA internal model framework and suggest as an alternative a number of standardization recommendations that could be considered to unify internal modelling of operational risk. The findings and views presented in this paper have been discussed with and supported by many OpRisk practitioners and academics in Australia, Europe, UK and USA, and recently at OpRisk Europe 2016 conference in London.
    Date: 2016–07
  4. By: Masahiko Egami; Rusudan Kevkhishvili
    Abstract: During subprime mortgage crisis, it became apparent that incumbent models had underestimated company default correlations. Complex models that attempt to incorporate default dependency are difficult to implement in practice. On the contrary, practical models, such as One-Factor Gaussian Copula model, greatly underestimated simultaneous default probabilities. In this article, we develop a model for a company asset process and based on this model, we calculate simultaneous default probabilities using option-theoretic approach. Our model focuses on one industry and includes a shot noise process in the asset model directly. The risk factor driving the shot noise process is common to all companies in the industry but the shot noise parameters are assumed to be company-specific; therefore, every company responds to this common risk factor differently. Apart from the shot noise process, the asset model includes company specific Brownian motion. Compared to commonly used geometric Brownian motion asset model in option-theoretic approach, our model predicted higher simultaneous default probabilities for Citigroup Inc. in 2008, and for all company combinations for the years of 2009 and 2010. Our model is easy to implement and can be extended to analyze any finite number of companies without greatly increasing computational difficulty.
    Keywords: shot noise; option-theoretic approach; asset process; simultaneous default probabilities
    JEL: G01 G21 G32
    Date: 2016–04
  5. By: Schwaab, Bernd; Koopman, Siem Jan; Lucas, André
    Abstract: We investigate the dynamic properties of systematic default risk conditions for firms in different countries, industries and rating groups. We use a high-dimensional nonlinear non-Gaussian state space model to estimate common components in corporate defaults in a 41 country sample between 1980Q1-2014Q4, covering both the global financial crisis and euro area sovereign debt crisis. We find that macro and default-specific world factors are a primary source of default clustering across countries. Defaults cluster more than what shared exposures to macro factors imply, indicating that other factors also play a signicant role. For all firms, deviations of systematic default risk from macro fundamentals are correlated with net tightening bank lending standards, suggesting that bank credit supply and systematic default risk are inversely related. JEL Classification: G21, C33
    Keywords: credit portfolio models, frailty-correlated defaults, international default risk cycles, state-space methods, systematic default risk
    Date: 2016–06
  6. By: Camba-Méndez, Gonzalo; Kostrzewa, Konrad; Marszal, Anna; Serwa, Dobromil
    Abstract: We analyze the market assessment of sovereign credit risk in an emerging market using a reduced-form model to price the credit default swap (CDS) spreads thus enabling us to derive values for the probability of default (PD) and loss given default (LGD) from the quotes of sovereign CDS contracts. We compare different specifications of the models allowing for both fixed and time varying LGD, and we use these values to analyze the sovereign credit risk of Polish debt throughout the recent global financial crisis. Our results suggest the presence of a low LGD and a relatively high PD for Poland during the crisis. The highest PD is in the months following the collapse of Lehman Brothers. The derived measures of sovereign risk are strongly linked with the level of public debt and with another measure of PD from a structural model. Correlations between our PD values and the CDS spreads heavily depend on the maturity of the sovereign CDS. JEL Classification: C11, C32, G01, G12, G15
    Keywords: CDS spreads, loss given default, Poland, probability of default, sovereign credit risk
    Date: 2016–06
  7. By: Alexander Karminsky
    Abstract: The Basel II Accords have sparked increased interest in the development of approaches based on internal ratings systems and have initiated the elaboration of models for remote ratings forecasts based on external ones as part of Risk Management and Early Warning Systems. This article evaluates the peculiarities of current ratings systems and addresses specific issues of development of econometrical rating models for emerging market companies.
    Date: 2016–07
  8. By: Justin Sirignano; Apaar Sadhwani; Kay Giesecke
    Abstract: This paper analyzes multi-period mortgage risk at loan and pool levels using an unprecedented dataset of over 120 million prime and subprime mortgages originated across the United States between 1995 and 2014, which includes the individual characteristics of each loan, monthly updates on loan performance over the life of a loan, and a number of time-varying economic variables at the zip code level. We develop, estimate, and test dynamic machine learning models for mortgage prepayment, delinquency, and foreclosure which capture loan-to-loan correlation due to geographic proximity and exposure to common risk factors. The basic building block is a deep neural network which addresses the nonlinear relationship between the explanatory variables and loan performance. Our likelihood estimators, which are based on 3.5 billion borrower-month observations, indicate that mortgage risk is strongly influenced by local economic factors such as zip-code level foreclosure rates. The out-of-sample predictive performance of our deep learning model is a significant improvement over linear models such as logistic regression. Model parameters are estimated using GPU parallel computing due to the computational challenges associated with the large amount of data. The deep learning model's superior accuracy compared to linear models directly translates into improved performance for investors. Portfolios constructed with the deep learning model have lower prepayment and delinquency rates than portfolios chosen with a logistic regression.
    Date: 2016–07
  9. By: Florence Crick; Katie Jenkins; Swenja Surminski
    Abstract: Multisectoral partnerships are increasingly being mentioned as a mechanism to deliver and improve disaster risk management. Yet, partnerships are not a panacea and more research is required to understand the role that they can play in disaster risk management and particularly in disaster risk reduction. In this paper, we investigate how partnerships can incentivise flood risk reduction by focusing on the UK public-private partnership on flood insurance. Developing the right flood insurance arrangements to incentivise flood risk reduction and adaptation to climate change is a key challenge. While expectations of the insurance industry have traditionally been high when it comes to flood risk management, the insurance industry alone will not provide the solution to the management of rising flood risks due to climate change and socio-economic development. In addition, faced with these risks insurance partnerships can no longer afford to focus only on the risk transfer function. The case of flood insurance in the UK illustrates these challenges: even national government and industry together cannot fully address these risks and other actors need to be involved to create strong incentives for risk reduction. Our paper investigates this for the specific issue of surface water flood risk in London. Using an agent-based model we investigate how other agents could strengthen the insurance partnership by maintaining affordable insurance premiums and reducing flood risk and test this for the new Flood Re scheme. Our findings are relevant for wider discussions on the potential of insurance schemes to incentivise flood risk management and climate adaptation not just in the UK but also internationally.
    Date: 2016–06
  10. By: Morgan, Peter (Asian Development Bank Institute); Regis, Paulo José (Division of Economics, Xi'an Jiaotong-Liverpool University); Salike, Nimesh (Division of Economics, Xi'an Jiaotong-Liverpool University)
    Abstract: Credit creation in the housing market has been a key source of systemic financial risk, and therefore is at the center of the debate on macroprudential policies. The loan-to-value (LTV) ratio is a widely-used macroprudential tool aimed at moderating mortgage loan creation, and its effectiveness needs to be estimated empirically. This paper is unique in that it analyzes the effect of LTV on mortgage lending, the direct channel of influence, using a large sample of banks in ten Asian countries. It uses estimation techniques to deal with the large presence of outliers in the data. Robust to outlier estimations show that countries with LTV polices have expanded residential mortgage loans by 6.7% per year while non-LTV countries have expanded by 14.6%, which suggests LTV policies have been effective.
    Keywords: macroprudential policies, financial stability, robust to outliers regression, mortgage loan creation
    JEL: C23 E58 G21 G28
    Date: 2015–08–25
  11. By: Clarke,Daniel Jonathan; Wren-Lewis,Liam
    Abstract: Those at risk from natural disasters are typically under-protected, possibly because they expect benefactors such as governments and donors to come to their aid. Yet when relief comes, it is often insufficient, delayed or misallocated. Benefactors may wish to commit to provide an efficient amount of fast well-targeted relief, and leave the rest up to recipients, but such commitments are difficult. This article analyses how transferring risk to third-parties such as private insurers may help resolve these commitment problems. Using a simple model of disaster risk finance is used to identify three distinct commitment problems and then show how various properties of risk transfer schemes can help to resolve these problems. The paper illustrates how these commitment problems play out using examples from around the world, and demonstrates where risk transfer schemes seem to have helped in practice. Overall, the findings show that the benefits of such schemes depend on the relative severity of the different commitment problems.
    Keywords: Climate Change Economics,Insurance&Risk Mitigation,Hazard Risk Management,Insurance Law,Natural Disasters
    Date: 2016–06–21

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