
on Risk Management 
Issue of 2020‒06‒22
27 papers chosen by 
By:  S. Broda (Department of Economics and Econometrics, University of Amsterdam); Juan Carlos ArismendiZambrano (Department of Economics, Finance and Accounting, Maynooth University & ICMA Centre, Henley Business School, University of Reading) 
Abstract:  Countless test statistics can be written as quadratic forms in certain random vectors, or ratios thereof. Consequently, their distribution has received considerable attention in the literature. Except for a few special cases, no closedform expression for the cdf exists, and one resorts to numerical methods. Traditionally the problem is analyzed under the assumption of joint Gaussianity; the algorithm that is usually employed is that of Imhof (1961). The present manuscript generalizes this result to the case of multivariate generalized hyperbolic random vectors. This ﬂexible distribution nests, among others, the multivariate t, Laplace, and variance gamma distributions. An expression for the ﬁrst partial moment is also obtained, which plays a vital role in ﬁnancial risk management. The proof involves a generalization of the classic inversion formula due to GilPelaez (1951). Two numerical applications are considered: ﬁrst, the finitesample distribution of the two stage least squares estimator of a structural parameter. Second, the Value at Risk and expected shortfall of a quadratic portfolio with heavytailed risk factors. An empirical application is examined, in which a portfolio of Dow Jones Industrial Index stock options is optimized with respect to its expected shortfall. The results demonstrate the beneﬁts of the analytical expression. 
Keywords:  Characteristic Function; Conditional Value at Risk; Expected Shortfall; Transform Inversion; Two Stage Least Squares. 
JEL:  C10 C13 C14 C15 C18 C63 C65 G32 
Date:  2020 
URL:  http://d.repec.org/n?u=RePEc:may:mayecw:n30220.pdf&r=all 
By:  Lang, Jan Hannes; Forletta, Marco 
Abstract:  This paper studies the impact of cyclical systemic risk on future bank profitability for a large representative panel of EU banks between 2005 and 2017. Using linear local projections we show that high current levels of cyclical systemic risk predict large drops in the average banklevel return on assets (ROA) with a lead time of 35 years. Based on quantile local projections we further show that the negative impact of cyclical systemic risk on the left tail of the future banklevel ROA distribution is an order of magnitude larger than on the median. Given the tight link between negative profits and reductions in bank capital, our method can be used to quantify the level of “Bank capitalatrisk” for a given banking system, akin to the concept of “Growthatrisk”. We illustrate how the method can inform the calibration of countercyclical macroprudential policy instruments. JEL Classification: G01, G17, C22, C54, G21 
Keywords:  bank profitability, Growthatrisk, local projections, quantile regressions, systemic risk 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:ecb:ecbwps:20202405&r=all 
By:  Roba Bairakdar; Lu Cao; Melina Mailhot 
Abstract:  The concept of univariate Range ValueatRisk, presented by Cont et al. (2010), is extended in the multidimensional setting. Traditional risk measures are not well suited when dealing with heavytail distributions and infinite tail expectations. The multivariate definitions of robust truncated tail expectations are provided to overcome this problem. Robustness and other properties as well as empirical estimators are derived. Closedform expressions and special cases in the extreme value framework are also discussed. Numerical and graphical examples are provided to examine the accuracy of the empirical estimators. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2005.12473&r=all 
By:  Christopher Demone; Olivia Di Matteo; Barbara Collignon 
Abstract:  In this study, we enhance Markowitz portfolio selection with graph theory for the analysis of two portfolios composed of either EU or US assets. Using a thresholdbased decomposition of their respective covariance matrices, we perturb the level of risk in each portfolio and build the corresponding sets of graphs. We show that the “superimposition” of all graphs in a set allows for the (re)construction of the efficient frontiers. We also identify a relationship between the Sharpe ratio (SR) of a given portfolio and the topology of the corresponding network of assets. More specifically, we suggest SR = f(topology) ≈ f(ECC/BC), where ECC is the eccentricity and BC is the betweenness centrality averaged over all nodes in the network. At each threshold, the structural analysis of the correlated networks provides unique insights into the relationships between assets, agencies, risks, returns and cash flows. We observe that the best threshold or best graph representation corresponds to the portfolio with the highest Sharpe ratio. We also show that simulated annealing performs better than a gradientbased solver. 
Keywords:  Central bank research 
JEL:  C02 
Date:  2020–06 
URL:  http://d.repec.org/n?u=RePEc:bca:bocawp:2021&r=all 
By:  Baumöhl, Eduard; Bouri, Elie; Hoang, ThiHongVan; Shahzad, Syed Jawad Hussain; Výrost,Tomáš 
Abstract:  Over the last few decades, large banks worldwide have become more interconnected, and as a result, the failure of one can trigger the failure of many. In finance, this phenomenon is often known as financial contagion, which can occur as a domino effect. In this paper, we show an unprecedented increase in bank interconnectedness during the outburst of the COVID19 pandemic. We measure how extreme negative stock market returns for one bank spill over to all other banks within the network, and on this basis, we propose a new measure of systemic risk among banks. Our results indicate that the systemic risk and the density of the spillover network have never been as high as they have been during the pandemic, not even during the 2008 global financial crisis. Policy makers and regulatory authorities should be particularly cautious regarding this interconnected financial environment, as second waves of the pandemic could pose a significant danger to the worldwide economy, and the “it’sjustaflu” narrative will no longer be an option. 
Keywords:  systemic risk,banks,COVID19,pandemic,crossquantilogram,financial networks,interconnectedness 
JEL:  G01 G15 G21 G28 C21 
Date:  2020 
URL:  http://d.repec.org/n?u=RePEc:zbw:esprep:218944&r=all 
By:  Beck, Thorsten; Radev, Deyan; Schnabel, Isabel 
Abstract:  We assess the ability of bank resolution frameworks to deal with systemic banking fragility. Using a novel and detailed database on bank resolution regimes in 22 member countries of the Financial Stability Board, we show that systemic risk, as measured by â?³CoVaR, increases more for banks in countries with more comprehensive bank resolution frameworks after negative systemwide shocks, such as Lehman Brothers' default, while it decreases more after positive systemwide shocks, such as Mario Draghi's "whatever it takes'' speech. These results suggest that more comprehensive bank resolution may exacerbate the effect of systemwide shocks and should not be solely relied on in cases of systemic distress. 
Keywords:  bailin; Bank resolution regimes; systemic risk 
JEL:  G01 G21 G28 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:14724&r=all 
By:  Ambrocio, Gene; Hasan, Iftekhar; Jokivuolle, Esa; Ristolainen, Kim 
Abstract:  We survey 149 leading academic researchers on bank capital regulation. The median (average) respondent prefers a 10% (15%) minimum nonriskweighted equitytoassets ratio, which is considerably higher than the current requirement. North Americans prefer a significantly higher equitytoassets ratio than Europeans. We find substantial support for the new forms of regulation introduced in Basel III, such as liquidity requirements. Views are most dispersed regarding the use of hybrid assets and bailinable debt in capital regulation. 70% of experts would support an additional marketbased capital requirement. When investigating factors driving capital requirement preferences, we find that the typical expert believes a five percentage points increase in capital requirements would “probably decrease” both the likelihood and social cost of a crisis with “minimal to no change” to loan volumes and economic activity. The best predictor of capital requirement preference is how strongly an expert believes that higher capital requirements would increase the cost of bank lending. 
JEL:  G01 G28 
Date:  2020–06–02 
URL:  http://d.repec.org/n?u=RePEc:bof:bofrdp:2020_010&r=all 
By:  Falco J. BargagliDtoffi (IMT School for advanced studies); Massimo Riccaboni (IMT School for advanced studies); Armando Rungi (IMT School for advanced studies) 
Abstract:  In this contribution, we exploit machine learning techniques to predict the risk of failure of firms. Then, we propose an empirical definition of zombies as firms that persist in a status of high risk, beyond the highest decile, after which we observe that the chances to transit to lower risk are minimal. We implement a Bayesian Additive Regression Tree with Missing Incorporated in Attributes (BARTMIA), which is specifically useful in our setting as we provide evidence that patterns of undisclosed accounts correlate with firms failures. After training our algorithm on 304,906 firms active in Italy in the period 20082017, we show how it outperforms proxy models like the Zscores and the DistancetoDefault, traditional econometric methods, and other widely used machine learning techniques. We document that zombies are on average 21% less productive, 76% smaller, and they increased in times of financial crisis. In general, we argue that our application helps in the design of evidencebased policies in the presence of market failures, for example optimal bankruptcy laws. We believe our framework can help to inform the design of support programs for highly distressed firms after the recent pandemic crisis. 
Keywords:  machine learning; Bayesian statistical learning; financial constraints; bankruptcy;zombie firms 
JEL:  C53 C55 G32 G33 L21 L25 
Date:  2020–06 
URL:  http://d.repec.org/n?u=RePEc:ial:wpaper:1/2020&r=all 
By:  Bruno Bouchard (CEREMADE); Adil Reghai (CEREMADE); Benjamin Virrion (CEREMADE) 
Abstract:  We consider a multistep algorithm for the computation of the historical expected shortfall such as defined by the Basel Minimum Capital Requirements for Market Risk. At each step of the algorithm, we use Monte Carlo simulations to reduce the number of historical scenarios that potentially belong to the set of worst scenarios. The number of simulations increases as the number of candidate scenarios is reduced and the distance between them diminishes. For the most naive scheme, we show that the L perror of the estimator of the Expected Shortfall is bounded by a linear combination of the probabilities of inversion of favorable and unfavorable scenarios at each step, and of the last step Monte Carlo error associated to each scenario. By using concentration inequalities, we then show that, for subgamma pricing errors, the probabilities of inversion converge at an exponential rate in the number of simulated paths. We then propose an adaptative version in which the algorithm improves step by step its knowledge on the unknown parameters of interest: mean and variance of the Monte Carlo estimators of the different scenarios. Both schemes can be optimized by using dynamic programming algorithms that can be solved offline. To our knowledge, these are the first nonasymptotic bounds for such estimators. Our hypotheses are weak enough to allow for the use of estimators for the different scenarios and steps based on the same random variables, which, in practice, reduces considerably the computational effort. First numerical tests are performed. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2005.12593&r=all 
By:  Smith, Kevin (Stanford U); So, Eric C. (MIT) 
Abstract:  We develop a measure of how information events impact investors' perceptions of firms' riskiness. We derive this measure from an optionpricing model where investors anticipate an announcement containing information on the mean and variance of firms' future prices. We apply the measure to firms' earnings announcements and show it has many desirable properties: it predicts firms' return volatilities, riskfactor exposures, implied costs of capital, the timing of heightened volatility, and deterioration in fundamental performance, and outperforms textualbased proxies. Together, our study offers an approach for studying risk information conveyed by information events that is simple to implement and broadly applicable. 
JEL:  G10 G11 G12 G14 M40 M41 
Date:  2020–01 
URL:  http://d.repec.org/n?u=RePEc:ecl:stabus:3857&r=all 
By:  Svetlana Pashchenko (University of Georgia); Ponpoje Porapakkarm (National Graduate Institute for Policy Studies) 
Abstract:  How does the value of life affect annuity demand? To address this question, we construct a portfolio choice problem with three key features: i) agents have access to lifecontingent assets, ii) they always prefer living to dying, iii) agents have nonexpected utility preferences. We show that as utility from being alive increases, annuity demand decreases (increases) if agents are more (less) averse to risk rather than to intertemporal fluctuations. Put differently, if people prefer early resolution of uncertainty, they are less interested in annuities when the value of life is high. Our findings have two important implications. First, we get a better understanding of the wellknown annuity puzzle. Second, we argue that the observed low annuity demand provides evidence that people prefer early rather than late resolution of uncertainty. 
Keywords:  annuities, value of a statistical life, portfolio choice problem, lifecontingent assets, longevity insurance 
JEL:  D91 G11 G22 
Date:  2020–06 
URL:  http://d.repec.org/n?u=RePEc:hka:wpaper:2020042&r=all 
By:  Bonfim, Diana; Cerqueiro, Geraldo; Degryse, Hans; Ongena, Steven 
Abstract:  In spite of growing regulatory pressure in most developed economies, "zombie lending" remains a widespread practice by banks. In this paper we exploit a series of largescale onsite inspections made on the credit portfolios of several Portuguese banks to investigate how these inspections affect banks' future lending decisions. We find that an inspected bank becomes 20% less likely to refinance zombie firms, immediately spurring their default. However, banks change their lending decisions only in the inspected sectors. Overall, banks seemingly reduce zombie lending because the incentives to hold these loans disappear once they are forced to recognize losses. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:14754&r=all 
By:  Huisman, Ronald; Kyritsis, Evangelos; Stet, Cristian 
Abstract:  The largescale integration of renewable energy sources requires flexibility from power markets in the sense that the latter should quickly counterbalance the renewable supply variation driven by weather conditions. Most power markets cannot (yet) provide this flexibility effectively as they suffer from inelastic demand and insufficient flexible storage capacity. Research accordingly shows that the volume of renewable energy in the supply system affects the mean and volatility of power prices. We extend this view and show that the level of wind and solar energy supply affects the tails of the electricity price distributions as well, and that it does so asymmetrically. The higher the supply from wind and solar energy sources, the fatter the left tail of the price distribution and the thinner the right tail. This implies that one cannot rely on symmetric price distributions for risk management and for valuation of (flexible) power assets. The evidence in this paper suggests that we have to rethink the methods of subsidizing variable renewable supply such that they take also into consideration the flexibility needs of power markets. 
Keywords:  intermittent renewable supply, flexibility, power prices, fat tails, asymmetric probability distribution, Environment, energy and climate policy, C10, Q41, Q42, 
Date:  2020 
URL:  http://d.repec.org/n?u=RePEc:fer:wpaper:134&r=all 
By:  José Miguel Villena; Alexander Hynes 
Abstract:  This work presents the results of the triennial central bank survey of foreign exchange and overthecounter (OTC) derivatives markets, carried out in 2019 and is coordinated by the Bank for International Settlements with the participation of 53 jurisdictions. The objective of this survey is to provide transparency and contribute to the discussion related to the global OTC derivative markets reforms since 2008. Global results from the survey indicate that trading in foreign exchange (FX) markets reached US$6.6 trillion per day in April 2019 and that derivative contracts constituted 70% of total FX activity. The document also presents an international comparison of the Chilean FX market with respect to three different economic blocks, highlighting its deepness after normalising for GDP, which is superior to other emerging and Latin American economies. Stylized facts on the Chilean FX market reveal the growth this market has experienced over recent years. Active in the market are nonresidents, pension funds, insurance firms, brokers, fund managers and real sector companies, whose participation is related to their access to international capital markets and commerce. Furthermore, this study introduces new derivatives series by original maturity published by the Central Bank of Chile. These new series illustrate that around 50% of newly entered contracts have original maturities up to thirty days, whereas 50% of outstanding contracts extend out to one year. Final reference is made to work currently under way in the Central Bank of Chile regarding the development of a Trade Repository which will gradually begin operations towards the final quarter of 2020 and into 2021. The objectives of this new financial market infrastructure is to promote greater transparency and bestpractice financial risk management, improve the decision making capabilities of investors and other stakeholders, and contribute to the supervisory processes of the Comisión para el Mercado Financiero in Chile. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:chb:bcchee:132&r=all 
By:  Riccardo Doyle 
Abstract:  Interbank contagion can theoretically exacerbate losses in a financial system and lead to additional cascade defaults during downturn. In this paper we produce default analysis using both regression and neural network models to verify whether interbank contagion offers any predictive explanatory power on default events. We predict defaults of U.S. domiciled commercial banks in the first quarter of 2010 using data from the preceding four quarters. A number of established predictors (such as Tier 1 Capital Ratio and Return on Equity) are included alongside contagion to gauge if the latter adds significance. Based on this methodology, we conclude that interbank contagion is extremely explanatory in default prediction, often outperforming more established metrics, in both regression and neural network models. These findings have sizeable implications for the future use of interbank contagion as a variable of interest for stress testing, bank issued bond valuation and wider bank default prediction. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2005.12619&r=all 
By:  Scott R. Baker; Nicholas Bloom; Stephen J. Terry 
Abstract:  Uncertainty rises in recessions and falls in booms. But what is the causal relationship? We construct crosscountry panel data on stock market levels and volatility and use natural disasters, terrorist attacks, and political shocks as instruments in regressions and VAR estimations. We find that increased volatility robustly lowers growth. We also structurally estimate a heterogeneous firms business cycle model with uncertainty and disasters and use this to analyze our empirical results. Finally, using our VAR results we estimate COVID19 will reduce US GDP by 9% in 2020 based on the initial stock market returns and volatility response. 
JEL:  C23 D8 D92 E22 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:27167&r=all 
By:  Xudong An; Lawrence R. Cordell; Sahron Tang 
Abstract:  A salient feature of the $1.2 trillion autoloan market is the extension of loan maturity terms in recent years. Using a large, national sample of auto loans from the entire auto market, we find that the default rates on six and sevenyear loans are multiple times that of shorter fiveyear term loans. Most of the default risk difference is due to borrower risks associated with longerterm loans, as those longerterm auto borrowers are more credit and liquidity constrained. We also find borrowers’ loanterm choice to be endogenous and that the endogeneity bias is substantial in conventional default model estimates. To mitigate this risk, we separately estimate instrumental variable regression and simultaneous equation models. Finally, we find evidence of adverse selection in borrowers’ loanterm choices in the years when six and sevenyear loans first became widely used, which dissipates over time as lenders adjust to risks in the market. 
Keywords:  credit risk; adverse selection; auto loans 
JEL:  D14 D81 D82 G32 
Date:  2020–05–20 
URL:  http://d.repec.org/n?u=RePEc:fip:fedpwp:88024&r=all 
By:  Bruno Feunou; Ricardo Lopez Aliouchkin; Roméo Tedongap; Lai Xu 
Abstract:  We document that the term structures of riskneutral expected loss and gain uncertainty on S&P 500 returns are upward sloping on average. These shapes mainly reflect the higher premium required by investors to hedge downside risk and the belief that potential gains will increase in the long run. The term structures exhibit substantial timeseries variation with large negative slopes during crisis periods. Through the lens of Andersen et al.’s (2015) framework, we evaluate the ability of existing reducedform option pricing models to replicate these term structures. We stress that three ingredients are particularly important: (i) the inclusion of jumps, (ii) disentangling the price of negative jump risk from its positive analog in the stochastic discount factor specification, and (iii) specifying three latent factors. 
Keywords:  Asset pricing; Econometric and statistical methods 
JEL:  G12 
Date:  2020–06 
URL:  http://d.repec.org/n?u=RePEc:bca:bocawp:2019&r=all 
By:  Patrick A. Adams; Tobias Adrian; Nina Boyarchenko; Domenico Giannone; J. Nellie Liang; Eric Qian 
Abstract:  The economic fallout from the COVID19 pandemic has been sharp. Real U.S. GDP growth in the first quarter of 2020 (advance estimate) was 4.8 percent at an annual rate, the worst since the global financial crisis in 2008. Most forecasters predict much weaker growth in the second quarter, ranging widely from an annual rate of 15 percent to 50 percent as the economy pauses to allow for social distancing. Although growth is expected to begin its rebound in the third quarter absent a second wave of the pandemic, the speed of the recovery is highly uncertain. In this post, we estimate the risks around the modal forecast of GDP growth as a function of financial conditions. Tighter financial conditions led to a widening in the left tail of the distribution of 2020 growth before weekly economic indicators showed any deterioration. The Federal Reserve and the U.S. Department of the Treasury took aggressive actions to reduce financial stresses and support credit flows—moves aimed at stemming longlasting impacts from steep economic losses. While GDP growth will depend primarily on the speed with which many activities can be resumed safely, the improved financial conditions in April have reduced the likelihood that financial conditions and real growth will jointly deteriorate in the next few quarters. 
Keywords:  growthatrisk; financial conditions; multimodality; COVID19 
JEL:  G17 G32 
Date:  2020–05–21 
URL:  http://d.repec.org/n?u=RePEc:fip:fednls:88027&r=all 
By:  Jozef Barunik; Michael Ellington 
Abstract:  This paper examines the pricing of dynamic horizon specific network risk in the crosssection of stock returns. We suggest how to track such dynamic network connections on a daily basis using timevarying parameter vector autoregressions. Empirically, we characterize the shortterm and longterm risks from a largescale dynamic network on all S&P500 constituents' return volatilities. Consistent with theory, we show that stocks with high sensitivities to dynamic network risk earn lower returns. A twostandard deviation increase in longterm (shortterm) network risk loadings associate with a 14.73% (12.96%) drop in annualized expected returns. 
Date:  2020–06 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2006.04639&r=all 
By:  Neofytos Rodosthenous; Hongzhong Zhang 
Abstract:  We consider risk averse investors with different levels of anxiety about asset price drawdowns. The latter is defined as the distance of the current price away from its best performance since inception. These drawdowns can increase either continuously or by jumps, and will contribute towards the investor's overall impatience when breaching the investor's private tolerance level. We investigate the unusual reactions of investors when aiming to sell an asset under such adverse market conditions. Mathematically, we study the optimal stopping of the utility of an asset sale with a random discounting that captures the investor's overall impatience. The random discounting is given by the cumulative amount of time spent by the drawdowns in an undesirable high region, fine tuned by the investor's personal tolerance and anxiety about drawdowns. We prove that in addition to the traditional takeprofit sales, the reallife employed stoploss orders and trailing stops may become part of the optimal selling strategy, depending on different personal characteristics. This paper thus provides insights on the effect of anxiety and its distinction with traditional risk aversion on decision making. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2006.00282&r=all 
By:  Philippe Artzner; KarlTheodor Eisele; Thorsten Schmidt 
Abstract:  This paper is an attempt to study fundamentally the valuation of insurance contracts. We start from the observation that insurance contracts are inherently linked to financial markets, be it via interest rates, or  as in hybrid products, equitylinked life insurance and variable annuities  directly to stocks or indices. By defining portfolio strategies on an insurance portfolio and combining them with financial trading strategies we arrive at the notion of insurancefinance arbitrage (IFA). A fundamental theorem provides two sufficient conditions for presence or absence of IFA, respectively. For the first one it utilizes the conditional law of large numbers and riskneutral valuation. As a key result we obtain a simple valuation rule, called QPrule, which is market consistent and excludes IFA. Utilizing the theory of enlargements of filtrations we construct a tractable framework for general valuation results, working under weak assumptions. The generality of the approach allows to incorporate many important aspects, like mortality risk or dependence of mortality and stock markets which is of utmost importance in the recent corona crisis. For practical applications, we provide an affine formulation which leads to explicit valuation formulas for a large class of hybrid products. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2005.11022&r=all 
By:  Xiao, Tim 
Abstract:  This article presents a generic model for pricing financial derivatives subject to counterparty credit risk. Both unilateral and bilateral types of credit risks are considered. Our study shows that credit risk should be modeled as American style options in most cases, which require a backward induction valuation. To correct a common mistake in the literature, we emphasize that the market value of a defaultable derivative is actually a risky value rather than a riskfree value. Credit value adjustment (CVA) is also elaborated. A practical framework is developed for pricing defaultable derivatives and calculating their CVAs at a portfolio level. 
Date:  2020–06–05 
URL:  http://d.repec.org/n?u=RePEc:osf:socarx:jc43a&r=all 
By:  Marwan Izzeldin; Emmanuel Mamatzakis; Anthony Murphy; Mike G. Tsionas 
Abstract:  Using Bayesian Monte Carlo methods, we augment a stochastic distance function measure of bank efficiency and productivity growth with indicators of capitalization, return and risk. Our novel Multiple IndicatorMultiple Cause (MIMIC) style model generates more precise estimates of policy relevant parameters such as returns to scale, technical inefficiency and productivity growth. We find considerable variation in the performance of EU15 banks over the period 2008 to 2015. For the vast majority of banks, productivity growth – the sum of efficiency and technical changes – is negative, implying that the industry would benefit from innovation. We show that greater technical efficiency is associated with higher profitability, higher capital, a lower probability of default and lower return volatility. 
Keywords:  Multiple IndicatorsMultiple Causes (MIMIC); technical efficiency; productivity growth; EU banks 
JEL:  C11 C51 D24 G21 
Date:  2020–05–19 
URL:  http://d.repec.org/n?u=RePEc:fip:feddwp:88038&r=all 
By:  Cristina Arellano; Yan Bai; Gabriel Mihalache 
Abstract:  The COVID19 epidemic in emerging markets risks a combined health, economic, and debt crisis. We integrate a standard epidemiology model into a sovereign default model and study how default risk impacts the ability of these countries to respond to the epidemic. Lockdown policies are useful for alleviating the health crisis but they carry large economic costs and can generate costly and prolonged debt crises. The possibility of lockdown induced debt crises in turn results in less aggressive lockdowns and a more severe health crisis. We find that the social value of debt relief can be substantial because it can prevent the debt crisis and can save lives. 
Keywords:  Default risk; Pandemic mitigation; Sovereign debt; Partial default; Debt relief; COVID19 
JEL:  E52 F34 F41 
Date:  2020–05–22 
URL:  http://d.repec.org/n?u=RePEc:fip:fedmsr:88044&r=all 
By:  Dhruv Sharma; JeanPhilippe Bouchaud; Marco Tarzia; Francesco Zamponi 
Abstract:  We introduce a prototype agentbased model of the macroeconomy, with a budgetary constraint at its core. The model is related to a class of constraint satisfaction problems, which has been thoroughly investigated in computer science. We identify three different regimes of our toy economy upon varying the amount of debt that each agent can accumulate before defaulting. In presence of a very loose constraint on debt, endogenous crises leading to waves of synchronized bankruptcies are present. In the opposite regime of very tight debt constraining, the bankruptcy rate is extremely high and the economy remains structureless. In an intermediate regime, the economy is stable with very low bankruptcy rate and no aggregatelevel crises. This third regime displays a rich phenomenology: the system spontaneously and dynamically selforganizes in a set of cheap and expensive goods (i.e. some kind of "speciation"), with switches triggered by random fluctuations and feedback loops. Our analysis confirms the central role that debt levels play in the stability of the economy. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2005.11748&r=all 
By:  HoppeWewetzer, Heidrun C.; Siemering, Christian 
Abstract:  This paper investigates the incentives of a credit rating agency (CRA) to generate accurate ratings under an advertisementbased business model. We study a twoperiod endogenous reputation model in which the CRA can choose to provide private effort in evaluating financial products in each period. We show that the advertisementbased business model may provide sufficient incentives to improve the precision of signals when the CRA has an intermediate reputation. Furthermore, we identify conditions under which truthful reporting is incentive compatible. 
Keywords:  advertisement; Credit rating agencies; Information Acquisition; rating precision; reputation 
JEL:  D82 G24 L15 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:14735&r=all 