nep-rmg New Economics Papers
on Risk Management
Issue of 2017‒05‒21
thirteen papers chosen by



  1. Multilateral Development Bank Credit Rating Methodology: Overcoming the Challenges in Assessing Relative Credit Risk in Highly Rated Institutions Based on Public Data By David Xiao Chen; Philippe Muller; Hawa Wagué
  2. Bayesian Inference of the Multi-Period Optimal Portfolio for an Exponential Utility By David Bauder; Taras Bodnar; Nestor Parolya; Wolfgang Schmid
  3. Murphy Diagrams: Forecast Evaluation of Expected Shortfall By Ziegel, Johanna F.; Krueger, Fabian; Jordan, Alexander; Fasciati, Fernando
  4. Capital requirements, risk shifting and the mortgage market By Uluc, Arzu; Wieladek, Tomasz
  5. Computation of second order price sensitivities in depressed markets By Youssef El-Khatib; Abdulnasser Hatemi-J
  6. Variance Decomposition Networks; Potential Pitfalls and a Simple Solution By Jorge A. Chan-Lau
  7. Risk of Life Insurers: Recent Trends and Transmission Mechanisms By Ralph S.J. Koijen; Motohiro Yogo
  8. Kingdom of the Netherlands-Netherlands: Financial Sector Assessment Program:; Technical Note-Regulation,Supervision, and Oversight of Financial Market Infrastructures-Responsibilities and EUROCCP Financial and Operational Risk Management By International Monetary Fund.
  9. Kingdom of the Netherlands-Netherlands: Financial Sector Assessment Program:; Technical Note-Financial Stability and Stress Testing of the Banking, Household, and Corporate Sectors By International Monetary Fund.
  10. Assessing Corporate Vulnerabilities in Indonesia; A Bottom-Up Default Analysis By Jorge A Chan-Lau; Weimin Miao; Ken Miyajima; Jongsoon Shin
  11. Should bank capital requirements be less risk-sensitive because of credit constraints? By Ambrocio, Gene; Jokivuolle, Esa
  12. Trends in commercial real estate: remarks at the Risk Management for Commercial Real Estate Financial Markets Conference, New York University Stern School of Business, New York, New York, May 9, 2017 By Rosengren, Eric S.
  13. Loss functions for LGD models comparison By Jérémy Leymarie; Christophe Hurlin; Antoine Patin

  1. By: David Xiao Chen; Philippe Muller; Hawa Wagué
    Abstract: The investment of foreign exchange reserves or other asset portfolios requires an assessment of the credit quality of counterparties. Traditionally, foreign exchange reserve managers and other investors have relied on credit rating agencies (CRAs) as the main source for credit assessments. The Financial Stability Board issued a set of principles in support of financial stability to reduce reliance on CRA ratings in standards, laws and regulations. To support efforts to end mechanistic reliance on CRA ratings and instead establish stronger internal credit assessment practices, this paper provides a detailed technical description of a methodology developed to assign an internal credit rating to multilateral development banks (MDBs), using only publicly available data. The methodology relies on fundamental credit analysis that produces a forward-looking assessment of the investment entity’s capacity and willingness to pay its financial obligations, resulting in an opinion on the relative credit standing or likelihood of default. This methodology proposes four key innovations: (i) a simple way of estimating the capital adequacy ratio, (ii) new metrics to evaluate the liquidity and funding profile of an MDB, (iii) a straightforward approach to evaluating the exceptional support from shareholders, and (iv) a new criterion related to corporate governance, which provides a high level of objectivity in assessing some of the qualitative indicators. The methodology is a key component of the joint Bank of Canada and Department of Finance Canada initiative to develop internal credit assessment capabilities and is currently used to assess eligibility and inform investment decisions in the management of Canada’s foreign exchange reserves.
    Keywords: Credit risk management, Foreign reserves management
    JEL: G24 G28 G32 F31
    Date: 2017
    URL: http://d.repec.org/n?u=RePEc:bca:bocadp:17-6&r=rmg
  2. By: David Bauder; Taras Bodnar; Nestor Parolya; Wolfgang Schmid
    Abstract: We consider the estimation of the multi-period optimal portfolio obtained by maximizing an exponential utility. Employing Jeffreys' non-informative prior and the conjugate informative prior, we derive stochastic representations for the optimal portfolio weights at each time point of portfolio reallocation. This provides a direct access not only to the posterior distribution of the portfolio weights but also to their point estimates together with uncertainties and their asymptotic distributions. Furthermore, we present the posterior predictive distribution for the investor's wealth at each time point of the investment period in terms of a stochastic representation for the future wealth realization. This in turn makes it possible to use quantile-based risk measures or to calculate the probability of default. We apply the suggested Bayesian approach to assess the uncertainty in the multi-period optimal portfolio by considering assets from the FTSE 100 in the weeks after the British referendum to leave the European Union. The behaviour of the novel portfolio estimation method in a precarious market situation is illustrated by calculating the predictive wealth, the risk associated with the holding portfolio, and the default probability in each period.
    Date: 2017–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1705.06533&r=rmg
  3. By: Ziegel, Johanna F.; Krueger, Fabian; Jordan, Alexander; Fasciati, Fernando
    Abstract: Motivated by the Basel 3 regulations, recent studies have considered joint forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring functions can be used to evaluate forecast performance in this context. However, little intuitive or empirical guidance is currently available, which renders the choice of scoring function awkward in practice. We therefore develop graphical checks (Murphy diagrams) of whether one forecast method dominates another under a relevant class of scoring functions, and propose an associated hypothesis test. We illustrate these tools with simulation examples and an empirical analysis of S&P 500 and DAX returns.
    Date: 2017–05–12
    URL: http://d.repec.org/n?u=RePEc:awi:wpaper:0632&r=rmg
  4. By: Uluc, Arzu; Wieladek, Tomasz
    Abstract: We study the effect of changes to bank-specific capital requirements on mortgage loan supply with a new loan-level dataset containing all mortgages issued in the UK between 2005Q2 and 2007Q2. We find that a rise of a 100 basis points in capital requirements leads to a 5.4% decline in individual loan size by bank. Loans issued by competing banks rise by roughly the same amount, which is indicative of credit substitution. Borrowers with an impaired credit history (verified income) are not (most) affected. This is consistent with origination of riskier loans to grow capital by raising retained earnings. No evidence for credit substitution of non-bank finance companies is found. JEL Classification: G21, G28
    Keywords: capital requirements, credit substitution, loan-level data, mortgage market
    Date: 2017–05
    URL: http://d.repec.org/n?u=RePEc:ecb:ecbwps:20172061&r=rmg
  5. By: Youssef El-Khatib; Abdulnasser Hatemi-J
    Abstract: Risk management in financial derivative markets requires inevitably the calculation of the different price sensitivities. The literature contains an abundant amount of research works that have studied the computation of these important values. Most of these works consider the well-known Black and Scholes model where the volatility is assumed to be constant. Moreover, to our best knowledge, they compute only the first order price sensitivities. Some works that attempt to extend to markets affected by financial crisis appeared recently. However, none of these papers deal with the calculation of the price sensitivities of second order. Providing second derivatives for the underlying price sensitivities is an important issue in financial risk management because the investor can determine whether or not each source of risk is increasing at an increasing rate. In this paper, we work on the computation of second order prices sensitivities for a market under crisis. The underlying second order price sensitivities are derived explicitly. The obtained formulas are expected to improve on the accuracy of the hedging strategies during a financial crunch.
    Date: 2017–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1705.02473&r=rmg
  6. By: Jorge A. Chan-Lau
    Abstract: Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. The standard choice is the order invariant generalized forecast error variance decomposition of Pesaran and Shin (1998). The shares of the forecast error variation, however, do not add to unity, making difficult to compare risk ratings and risks contributions at two different points in time. As a solution, this paper suggests using the Lanne-Nyberg (2016) decomposition, which shares the order invariance property. To illustrate the differences between both decomposition methods, I analyzed the global financial system during 2001 – 2016. The analysis shows that different decomposition methods yield substantially different systemic risk and vulnerability rankings. This suggests caution is warranted when using rankings and risk contributions for guiding financial regulation and economic policy.
    Date: 2017–05–04
    URL: http://d.repec.org/n?u=RePEc:imf:imfwpa:17/107&r=rmg
  7. By: Ralph S.J. Koijen; Motohiro Yogo
    Abstract: We summarize recent trends in risk exposure for U.S. life insurers from variable annuities, shadow insurance, securities lending, and derivatives. We discuss how these sources of risk could be amplified and transmitted to the rest of the financial sector and the real economy. More complete and transparent financial statements are necessary to accurately assess the overall risk mismatch in the insurance industry. We suggest ways to disclose relevant information and discuss some implications for insurance regulation.
    JEL: G12 G21 G22
    Date: 2017–04
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:23365&r=rmg
  8. By: International Monetary Fund.
    Abstract: The supervision of financial market infrastructures (FMIs) in the Netherlands has been significantly strengthened in recent years. The European Market Infrastructure Regulation (EMIR) introduced legally binding requirements for central counterparties (CCPs) located in the Netherlands. The Dutch authorities have also adopted the Committee on Payments and Market Infrastructures (CPMI)-International Organization of Securities Commissions (IOSCO) Principles for Financial Market Infrastructures (PFMI) in their oversight and supervision of central securities depositories (CSDs)/securities settlement systems (SSSs) and systemically important payment systems.
    Keywords: Europe;Netherlands;
    Date: 2017–04–13
    URL: http://d.repec.org/n?u=RePEc:imf:imfscr:17/92&r=rmg
  9. By: International Monetary Fund.
    Abstract: This analysis is conducted against a backdrop of a gradual domestic recovery, but still uncertain international context and remaining domestic vulnerabilities. Household debt is high and negative equity among young borrowers is prevalent. A substantial portion of households have a loan-to-value ratio above 100 percent. While bank capitalization has improved since the crisis, balance sheets have contracted, profitability is low, and banks remain significantly reliant on wholesale funding. Financial institutions also face challenges from the continuing low interest rate environment and slow credit growth.
    Keywords: Europe;Netherlands;
    Date: 2017–04–13
    URL: http://d.repec.org/n?u=RePEc:imf:imfscr:17/95&r=rmg
  10. By: Jorge A Chan-Lau; Weimin Miao; Ken Miyajima; Jongsoon Shin
    Abstract: Under adverse macroeconomic conditions, the potential realization of corporate sector vulnerabilities could pose major risks to the economy. This paper assesses corporate vulnerabilities in Indonesia by using a Bottom-Up Default Analysis (BuDA) approach, which allows projecting corporate probabilities of default (PDs) under different macroeconomic scenarios. In particular, a protracted recession and the ensuing currency depreciation could erode buffers on corporate balance sheets, pushing up the probabilities of default (PDs) in the corporate sector to the high levels observed during the Global Financial Crisis. While this is a low-probability scenario, the results suggest the need to closely monitor vulnerabilities and strengthen contingency plans.
    Keywords: Indonesia;Corporate sector;Asia and Pacific;bottom-up default analysis, default risk, scenario analysis, simulation, hazard rate models, Model Evaluation and Testing, General
    Date: 2017–04–26
    URL: http://d.repec.org/n?u=RePEc:imf:imfwpa:17/97&r=rmg
  11. By: Ambrocio, Gene; Jokivuolle, Esa
    Abstract: We consider optimal capital requirements for banks' lending activities when the potential trade-off between financial stability and economic (productivity) growth is taken into account. Both sides of the trade-off are affected by banks' credit allocation, which in turn is affected by the risk weights used to set capital requirements on bank loans. We find that when firms are credit constrained, the optimal risk weights are flatter than those that are only set to safeguard against bank failures and their social costs. This provides an additional rationale for capital requirements to be less 'risk-sensitive'. Differences in company productivity have a further effect on the profile of optimal risk weights, and may amplify the ‘flattening’ effect.
    JEL: E44 G21 G28
    Date: 2017–05–15
    URL: http://d.repec.org/n?u=RePEc:bof:bofrdp:2017_010&r=rmg
  12. By: Rosengren, Eric S. (Federal Reserve Bank of Boston)
    Abstract: Speaking in New York, Boston Fed President Eric Rosengren discussed commercial real estate valuations. While a variety of favorable conditions account for some of the elevated valuations, he noted that at such times it is worth asking what could go wrong and cause a reversal.
    Date: 2017–05–09
    URL: http://d.repec.org/n?u=RePEc:fip:fedbsp:118&r=rmg
  13. By: Jérémy Leymarie (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique); Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique); Antoine Patin (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We propose a new approach for comparing loss given default (LGD) models which is based on loss functions de…ned in terms of regulatory capital charge. These loss functions penalize more the LGD forecasts errors made on credits associated to high exposure and long maturity than the other ones. We also introduce asymmetric loss functions that only penalize the LGD forecasts errors that lead to underestimate the regulatory capital. We show theoretically that the LGD models ranking determined by our approach may di¤er from the ranking obtained according to the traditional approach that consists in comparing the models according to their LGD forecasts errors. Using an original sample of credits and leasing provided by an international bank, we apply this new approach to compare the LGD forecasts issued from 6 competing models. The empirical results con…rm that the ranking based on a naive LGD loss function is generally di¤erent from the models ranking obtained with the capital charge symmetric (or asymmetric) loss.
    Keywords: loss given default (LGD), credit risk capital requirement, loss function, fore-,casts comparison,JEL classi…cation: G21, G28
    Date: 2017–04–28
    URL: http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-01516147&r=rmg

General information on the NEP project can be found at https://nep.repec.org. For comments please write to the director of NEP, Marco Novarese at <director@nep.repec.org>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.