nep-rmg New Economics Papers
on Risk Management
Issue of 2018‒06‒18
fifteen papers chosen by

  1. Testing the systemic risk differences in banks By Jokivuolle, Esa; Tunaru, Radu; Vioto, Davide
  2. The time-varying impact of systematic risk factors on corporate bond spreads By Klein, Arne C.; Pliszka, Kamil
  3. Can we prove a bank guilty of creating systemic risk? A minority report By Danielsson, Jon; James, Kevin R.; Valenzuela, Marcela; Zer, Ilknur
  4. Risk excess measures induced by hemi-metrics By Faugeras, Olivier; Rüschendorf, Ludger
  5. Corporate Credit Risk Premia By Douglas, Rohan; Berndt, Antje; Duffie, Darrell; Ferguson, Mark
  6. The determinants of bank loan recovery rates in good times and bad -- new evidence By Hong Wang; Catherine S. Forbes; Jean-Pierre Fenech; John Vaz
  7. Low Risk as a Predictor of Financial Crises By Jon Danielsson; Marcela Valenzuela; Ilknur Zer
  8. The Insurance Is the Lemon: Failing to Index Contracts By Hartman-Glaser, Barney; Hebert, Benjamin
  9. The Lifetime Medical Spending of Retirees By John Bailey Jones; Mariacristina De Nardi; Eric French; Rory McGee; Justin Kirschner
  10. Operational risk and its determinants among five companies in manufacturing industry in Germany By Cipriano, Nur Alisha Arfiffy; Zulkeflee, Nur Nabila; Amran, Fasihah; Shahudin, Haziah Aishah
  11. Comparing Three Credit Scoring Models /Rachael Beer, Felicia Ionescu, and Geng Li. By Rachael Beer; Felicia Ionescu; Geng Li
  12. Deposit Inflows and Outflows in Failing Banks: The Role of Deposit Insurance By Christopher Martin; Manju Puri; Alexander Ufier
  13. Coordinating monetary and financial regulatory policies By Van der Ghote, Alejandro
  14. Multi-tiered Supply Chain Risk Management By Schorpp, Georg; Erhun, Feryal; Lee, Hau L.
  15. Herding Behaviour in the Cryptocurrency Market By Elie Bouri; Rangan Gupta; David Roubaud

  1. By: Jokivuolle, Esa; Tunaru, Radu; Vioto, Davide
    Abstract: This paper contains a testing framework for the reliability of systemic risk measurement of banks, using the three leading market-based measures of systemic risk. We test whether the difference within the same category and across dfferent categories of systemic risk of individual banks is signifcant. We find that in general the systemic risk categories defined by the Financial Stability Board are dfferent from those constructed in a full pairwise comparison approach based on the market measures. Moreover, these dfferences were more pronounced during episodes of high market turbulence.To account for model risk we introduce a more robust ranking method based on nonparametric confidence intervals. We show that there is a large number of banks with overlapping confidence intervals of their market-based systemic risk measures.Further, similarity measures indicate that the scoring based rankings are not perfectly aligned with rankings produced by market based systemic risk measures.
    JEL: G01 G32
    Date: 2018–06–01
  2. By: Klein, Arne C.; Pliszka, Kamil
    Abstract: During the global financial crisis, stressed market conditions led to skyrocketing corporate bond spreads that could not be explained by conventional modeling approaches. This paper builds on this observation and sheds light on time-variations in the relationship between systematic risk factors and corporate bond spreads. First, we apply Bayesian model averaging to a battery of candidate variables for determining meaningful systematic risk factors. Second, Markov switching techniques provide us with an endogenous separation of regimes accounting for times of stress, on the one hand, and for normal market conditions, on the other. Our evidence for market indices of euro-denominated bonds suggests that systematic risk factors play a much more prominent role during periods of market turmoil. Most important, expectations about default rates seem to be much more driven by systematic factors rather than idiosyncratic components during times of market stress.
    Keywords: asset pricing,banking regulation,Bayesian model averaging,credit spreads,European bond market,Markov switching
    JEL: G01 G10 G11 G12 G14 G15 G32
    Date: 2018
  3. By: Danielsson, Jon; James, Kevin R.; Valenzuela, Marcela; Zer, Ilknur
    Abstract: Because increasing a bank's capital requirement to improve the stability of the financial system imposes costs upon the bank, a regulator should ideally be able to prove beyond a reasonable doubt that banks classified as systemically risky really do create systemic risk before subjecting them to this capital punishment. Evaluating the performance of two leading systemic risk models, we show that estimation error alone prevents the reliable identification of the most systemically risky banks. We conclude that it will be a considerable challenge to develop a riskometer that is sound and reliable enough to provide an adequate foundation for macroprudential policy.
    Keywords: G10; G18; G20; G28; G38; Systemic risk; macro-prudential policy; financial stability; risk management
    JEL: N0 E6
    Date: 2016–06–01
  4. By: Faugeras, Olivier; Rüschendorf, Ludger
    Abstract: The main aim of this paper is to introduce the notion of risk excess measure, to analyze its properties and to describe some basic construction methods. To compare the risk excess of one distribution Q w.r.t. a given risk distribution P, we propose to apply the concept of hemi-metric on the space of probability measures. This view of risk comparison has a natural basis in the extension of orderings and hemi-metrics on the underlying space to the level of probability measures. Basic examples of these kind of extensions are induced by mass transportation and by function class induced orderings. Our view towards measuring risk excess adds to the usually considered method to compare risks of Q and P by the values rho(Q), rho(P) of a risk measure rho. We argue that the difference rho(Q)-rho(P) neglects relevant aspects of the risk excess which are adequately described by the new notion of risk excess measure. We derive various concrete classes of risk excess measures and discuss corresponding ordering and measure extension properties.
    Keywords: risk measure; mass transportation; hemi-metric; stochastic order
    Date: 2018–05
  5. By: Douglas, Rohan (Quantifi, Inc); Berndt, Antje (Australian National University); Duffie, Darrell (Stanford University); Ferguson, Mark (?)
    Abstract: We measure credit risk premia, meaning the price for bearing corporate default risk in excess of expected default losses, using Markit CDS and Moody's Analytics EDF data. We find dramatic variation over time in credit risk premia, with peaks in 2002, during the global financial crisis of 2008-09, and in the second half of 2011. These risk premia comove with economic indicators, even after controlling for variation in expected default losses, with higher premia per unit of expected loss during times of market-wide distress. Countercyclical variation of premia-to-expected-loss ratios is more pronounced for investment-grade issuers than for high-yield issuers.
    JEL: G12 G13 G22 G24
    Date: 2017–11
  6. By: Hong Wang; Catherine S. Forbes; Jean-Pierre Fenech; John Vaz
    Abstract: We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. Using US bank default loan data from Moody's Ultimate Recovery Database and covering the pre- and post-GFC period, this paper develops a dynamic predictive model for bank loan recovery rates, accommodating the distinctive empirical features of the recovery rate data while incorporating a large number of possible determinants. We find that the probability of default and certain loan-specific and other variables hold different explanatory power with respect to recovery rates over `good' and `bad' times in the credit cycle, meaning that the relationship between recovery rates and certain loan characteristics, firm characteristics and the probability of default differs depending on underlying credit market conditions. Our findings demonstrate the importance of accounting for countercyclical expected recovery rates when determining capital retention levels.
    Keywords: credit risk, Basel III, countercyclicality, Bayesian estimation, LASSO prior, Markov switching.
    JEL: G17 G21 G28
    Date: 2018
  7. By: Jon Danielsson; Marcela Valenzuela; Ilknur Zer
    Abstract: Reliable indicators of future financial crises are important for policymakers and practitioners. While most indicators consider an observation of high volatility as a warning signal, this column argues that such an alarm comes too late, arriving only once a crisis is already under way. A better warning is provided by low volatility, which is a reliable indication of an increased likelihood of a future crisis.
    Date: 2018–05–09
  8. By: Hartman-Glaser, Barney (University of California, Los Angeles); Hebert, Benjamin (Stanford University)
    Abstract: We model the widespread failure of contracts to share risk using available indices. A borrower and lender can share risk by conditioning repayments on an index. The lender has private information about the ability of this index to measure the true state the borrower would like to hedge. The lender is risk-averse, and thus requires a premium to insure the borrower. The borrower, however, might be paying something for nothing, if the index is a poor measure of the true state. We provide sufficient conditions for this effect to cause the borrower to choose a non-indexed contract instead.
    Date: 2017–12
  9. By: John Bailey Jones; Mariacristina De Nardi; Eric French; Rory McGee; Justin Kirschner
    Abstract: Using dynamic models of health, mortality, and out-of-pocket medical spending (both inclusive and net of Medicaid payments), we estimate the distribution of lifetime medical spending that retired U.S. households face over the remainder of their lives. We find that at age 70, households will on average incur $122,000 in medical spending, including Medicaid payments, over their remaining lives. At the top tail, 5 percent of households will incur more than $300,000, and 1 percent of households will incur over $600,000 in medical spending inclusive of Medicaid. The level and the dispersion of this spending diminish only slowly with age. Although permanent income, initial health, and initial marital status have large effects on this spending, much of the dispersion in lifetime spending is due to events realized later in life. Medicaid covers the majority of the lifetime costs of the poorest households and significantly reduces their risk.
    JEL: D1 D14 E02 E2 H31
    Date: 2018–05
  10. By: Cipriano, Nur Alisha Arfiffy; Zulkeflee, Nur Nabila; Amran, Fasihah; Shahudin, Haziah Aishah
    Abstract: Operational risk management is an important aspect in an organization to manage operational risk efficiently. Hence, this study intended to investigate the effects of internal and external factor in manufacturing industry towards operational risk. This study employs time series regression analysis of manufacturing industry in Germany from 2012 to 2016. The analysis shows that firm specific factors (average current ratio and average collection period) and macroeconomic factors (the company’s beta) influence the operational risk of the company. This study suggests the company to manage their average collection period by managing their account receivable efficiently through establishing clear credit policies and incorporate more corporate governance elements such as accountability, fairness, independence and transparency.
    Keywords: Operational risk, Average collection period, corporate governance
    JEL: G3 G32
    Date: 2018–05–25
  11. By: Rachael Beer; Felicia Ionescu; Geng Li
    Abstract: Our analysis uses a different, unique proprietary dataset that features three frequently used credit scores for each individual. Compared with the dataset used in the CFPB report, this dataset includes more recent time periods and provides a longer historical perspective of credit score comparisons.
    Date: 2018–05–21
  12. By: Christopher Martin; Manju Puri; Alexander Ufier
    Abstract: Using unique, daily, account-level balances data we investigate deposit stability and the drivers of deposit outflows and inflows in a distressed bank. We observe an outflow of uninsured depositors from the bank following bad regulatory news. We find that government deposit guarantees, both regular deposit insurance and temporary deposit insurance measures, reduce the outflow of deposits. We also characterize which accounts are more stable (e.g., checking accounts and older accounts). We further provide important new evidence that, simultaneous with the run-off, gross funding inflows are large and of first-order impact — a result which is missed when looking at aggregated deposit data alone. Losses of uninsured deposits were largely offset with new insured deposits as the bank approached failure. We show our results hold more generally using a large sample of banks that faced regulatory action. Our results raise questions about depositor discipline, widely considered to be one of the key pillars of financial stability, raising the importance of other mechanisms of restricting bank risk taking, including prudent supervision.
    JEL: D12 G01 G21 G28
    Date: 2018–05
  13. By: Van der Ghote, Alejandro
    Abstract: How to conduct macro-prudential regulation? How to coordinate monetary policy and macro-prudential policy? To address these questions, I develop a continuous-time New Keynesian economy in which a financial intermediary sector is subject to a leverage constraint. Coordination between monetary and macro-prudential policies helps to reduce the risk of entering into a financial crisis and speeds up exit from the crisis. The downside of coordination is variability in inflation and in the employment gap. JEL Classification: E31, E32, E44, E52, E61, G01
    Keywords: macro-prudential policy, monetary policy, policy coordination
    Date: 2018–06
  14. By: Schorpp, Georg (End-to-End Analytics, Palo Alto); Erhun, Feryal (University of Cambridge); Lee, Hau L. (Stanford University)
    Abstract: We study contracting for a three-tier supply chain consisting of a buyer, a supplier, and a sub-supplier where disruptions of random length occur at the sub-supplier. As is common in supply chains, the buyer has a direct relationship with the supplier but not the sub-supplier; that is, the buyer has limited supply chain visibility. Both the supplier and the sub-supplier can reserve emergency capacity proactively to protect the supply chain from a disruption. We study how the buyer and the supplier can guarantee that the correct level of emergency capacity is reserved. Due to two types of inefficiencies--a special form of double marginalization and the substitution effect--the supply chain is misaligned in its decentralized form, leading to either under or over-reservation of emergency capacity by the sub-supplier depending on the cost structure of the supply chain. The lack of visibility prevents the buyer from directly contracting with the sub-supplier to eliminate these inefficiencies. Yet, he can coordinate the supply chain through cascading: i.e., contracting with the supplier (using a value-based carrot-and-stick contract), who in turn contracts with the sub-supplier (using a cost-based carrot-and-stick or two-level wholesale price contract, depending on the cost structure of the supply chain). Although the sub-supplier is the source of limited visibility in the supply chain and is the party with private information, the supplier is the one that benefits from this limited visibility and is the party that receives information rent from the buyer.
    Date: 2018–01
  15. By: Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); David Roubaud (Montpellier Business School, Montpellier, France)
    Abstract: This study examines the presence of herding in the cryptocurrency market. The latter is the outcome of mass collaboration and imitation. Results from the static model suggest no significant herding. However, the presence of structural breaks and nonlinearities in the data series suggests applying a static model is not appropriate. Accordingly, we conduct a rolling-window analysis, and those results point to significant herding behavior, which varies over time. Using a logistic regression, we find that herding tends to occur as uncertainty increases. Our findings induce useful insights related to portfolio and risk management, trading strategies, and market efficiency.
    Keywords: Bitcoin; cryptocurrency market, herding behavior, economic policy uncertainty
    JEL: C22 G13
    Date: 2018–06

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