nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒06‒22
27 papers chosen by

  1. On Quadratic Forms in Multivariate Generalized Hyperbolic Random Vectors∗ By S. Broda; Juan Carlos Arismendi-Zambrano
  2. Cyclical systemic risk and downside risks to bank profitability By Lang, Jan Hannes; Forletta, Marco
  3. Range Value-at-Risk: Multivariate and Extreme Values By Roba Bairakdar; Lu Cao; Melina Mailhot
  4. Classical Decomposition of Markowitz Portfolio Selection By Christopher Demone; Olivia Di Matteo; Barbara Collignon
  5. From physical to financial contagion: the COVID-19 pandemic and increasing systemic risk among banks By Baumöhl, Eduard; Bouri, Elie; Hoang, Thi-Hong-Van; Shahzad, Syed Jawad Hussain; Výrost,Tomáš
  6. Bank Resolution Regimes and Systemic Risk By Beck, Thorsten; Radev, Deyan; Schnabel, Isabel
  7. Are bank capital requirements optimally set? Evidence from researchers’ views By Ambrocio, Gene; Hasan, Iftekhar; Jokivuolle, Esa; Ristolainen, Kim
  8. Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints. By Falco J. Bargagli-Dtoffi; Massimo Riccaboni; Armando Rungi
  9. Computation of Expected Shortfall by fast detection of worst scenarios By Bruno Bouchard; Adil Reghai; Benjamin Virrion
  10. Measuring Risk Information By Smith, Kevin; So, Eric C.
  11. Value of Life and Annuity Demand By Svetlana Pashchenko; Ponpoje Porapakkarm
  12. On-site inspecting zombie lending By Bonfim, Diana; Cerqueiro, Geraldo; Degryse, Hans; Ongena, Steven
  13. Fat Tails due to Variable Renewables and Insufficient Flexibility By Huisman, Ronald; Kyritsis, Evangelos; Stet, Cristian
  14. Mercado Cambiario Chileno, una comparación internacional: 1998 a 2019 By José Miguel Villena; Alexander Hynes
  15. Using Network Interbank Contagion in Bank Default Prediction By Riccardo Doyle
  16. Using Disasters to Estimate the Impact of Uncertainty By Scott R. Baker; Nicholas Bloom; Stephen J. Terry
  17. Extended Loan Terms and Auto Loan Default Risk By Xudong An; Lawrence R. Cordell; Sahron Tang
  18. The Term Structures of Loss and Gain Uncertainty By Bruno Feunou; Ricardo Lopez Aliouchkin; Roméo Tedongap; Lai Xu
  19. What Do Financial Conditions Tell Us about Risks to GDP Growth? By Patrick A. Adams; Tobias Adrian; Nina Boyarchenko; Domenico Giannone; J. Nellie Liang; Eric Qian
  20. Dynamic Horizon Specific Network Risk By Jozef Barunik; Michael Ellington
  21. When to sell an asset amid anxiety about drawdowns By Neofytos Rodosthenous; Hongzhong Zhang
  22. No arbitrage in insurance and the QP-rule By Philippe Artzner; Karl-Theodor Eisele; Thorsten Schmidt
  23. The Valuation of Financial Derivatives Subject to Counterparty Risk and Credit Value Adjustment By Xiao, Tim
  24. A Novel MIMIC-Style Model of European Bank Technical Efficiency and Productivity Growth By Marwan Izzeldin; Emmanuel Mamatzakis; Anthony Murphy; Mike G. Tsionas
  25. Deadly Debt Crises: COVID-19 in Emerging Markets By Cristina Arellano; Yan Bai; Gabriel Mihalache
  26. A constraint-satisfaction agent-based model for the macroeconomy By Dhruv Sharma; Jean-Philippe Bouchaud; Marco Tarzia; Francesco Zamponi
  27. Advertisement-Financed Credit Ratings By Hoppe-Wewetzer, Heidrun C.; Siemering, Christian

  1. By: S. Broda (Department of Economics and Econometrics, University of Amsterdam); Juan Carlos Arismendi-Zambrano (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 closed-form 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 Gil-Pelaez (1951). Two numerical applications are considered: first, the finite-sample 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 heavy-tailed 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 Inver-sion; Two Stage Least Squares.
    JEL: C10 C13 C14 C15 C18 C63 C65 G32
    Date: 2020
  2. 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 bank-level return on assets (ROA) with a lead time of 3-5 years. Based on quantile local projections we further show that the negative impact of cyclical systemic risk on the left tail of the future bank-level 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 capital-at-risk” for a given banking system, akin to the concept of “Growth-at-risk”. We illustrate how the method can inform the calibration of countercyclical macroprudential policy instruments. JEL Classification: G01, G17, C22, C54, G21
    Keywords: bank profitability, Growth-at-risk, local projections, quantile regressions, systemic risk
    Date: 2020–05
  3. By: Roba Bairakdar; Lu Cao; Melina Mailhot
    Abstract: The concept of univariate Range Value-at-Risk, presented by Cont et al. (2010), is extended in the multidimensional setting. Traditional risk measures are not well suited when dealing with heavy-tail 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. Closed-form 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
  4. 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 threshold-based 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 gradient-based solver.
    Keywords: Central bank research
    JEL: C02
    Date: 2020–06
  5. By: Baumöhl, Eduard; Bouri, Elie; Hoang, Thi-Hong-Van; 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 COVID-19 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’s-just-a-flu” narrative will no longer be an option.
    Keywords: systemic risk,banks,COVID-19,pandemic,cross-quantilogram,financial networks,interconnectedness
    JEL: G01 G15 G21 G28 C21
    Date: 2020
  6. 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 system-wide shocks, such as Lehman Brothers' default, while it decreases more after positive system-wide shocks, such as Mario Draghi's "whatever it takes'' speech. These results suggest that more comprehensive bank resolution may exacerbate the effect of system-wide shocks and should not be solely relied on in cases of systemic distress.
    Keywords: bail-in; Bank resolution regimes; systemic risk
    JEL: G01 G21 G28
    Date: 2020–05
  7. 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 non-risk-weighted equity-to-assets ratio, which is considerably higher than the current requirement. North Americans prefer a significantly higher equity-to-assets 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 bail-inable debt in capital regulation. 70% of experts would support an additional market-based 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
  8. By: Falco J. Bargagli-Dtoffi (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 (BART-MIA), 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 2008-2017, we show how it outperforms proxy models like the Z-scores and the Distance-to-Default, 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 evidence-based 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
  9. By: Bruno Bouchard (CEREMADE); Adil Reghai (CEREMADE); Benjamin Virrion (CEREMADE)
    Abstract: We consider a multi-step 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 p-error 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 sub-gamma 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 off-line. To our knowledge, these are the first non-asymptotic 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
  10. 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 option-pricing 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, risk-factor exposures, implied costs of capital, the timing of heightened volatility, and deterioration in fundamental performance, and outperforms textual-based 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
  11. 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 life-contingent assets, ii) they always prefer living to dying, iii) agents have non-expected 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 well-known 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, life-contingent assets, longevity insurance
    JEL: D91 G11 G22
    Date: 2020–06
  12. 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 large-scale on-site 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
  13. By: Huisman, Ronald; Kyritsis, Evangelos; Stet, Cristian
    Abstract: The large-scale 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
  14. By: José Miguel Villena; Alexander Hynes
    Abstract: This work presents the results of the triennial central bank survey of foreign exchange and over-thecounter (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 non-residents, 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 best-practice 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
  15. 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
  16. 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 cross-country 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 COVID-19 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
  17. By: Xudong An; Lawrence R. Cordell; Sahron Tang
    Abstract: A salient feature of the $1.2 trillion auto-loan 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 seven-year loans are multiple times that of shorter five-year term loans. Most of the default risk difference is due to borrower risks associated with longer-term loans, as those longer-term auto borrowers are more credit and liquidity constrained. We also find borrowers’ loan-term 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’ loan-term choices in the years when six- and seven-year 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
  18. By: Bruno Feunou; Ricardo Lopez Aliouchkin; Roméo Tedongap; Lai Xu
    Abstract: We document that the term structures of risk-neutral 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 time-series variation with large negative slopes during crisis periods. Through the lens of Andersen et al.’s (2015) framework, we evaluate the ability of existing reduced-form 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
  19. By: Patrick A. Adams; Tobias Adrian; Nina Boyarchenko; Domenico Giannone; J. Nellie Liang; Eric Qian
    Abstract: The economic fallout from the COVID-19 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 long-lasting 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: growth-at-risk; financial conditions; multimodality; COVID-19
    JEL: G17 G32
    Date: 2020–05–21
  20. By: Jozef Barunik; Michael Ellington
    Abstract: This paper examines the pricing of dynamic horizon specific network risk in the cross-section of stock returns. We suggest how to track such dynamic network connections on a daily basis using time-varying parameter vector auto-regressions. Empirically, we characterize the short-term and long-term risks from a large-scale 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 two-standard deviation increase in long-term (short-term) network risk loadings associate with a 14.73% (12.96%) drop in annualized expected returns.
    Date: 2020–06
  21. 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 take-profit sales, the real-life employed stop-loss 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
  22. By: Philippe Artzner; Karl-Theodor 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, equity-linked 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 insurance-finance 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 risk-neutral valuation. As a key result we obtain a simple valuation rule, called QP-rule, 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
  23. 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 risk-free 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
  24. 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 Indicator-Multiple 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 EU-15 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 Indicators-Multiple Causes (MIMIC); technical efficiency; productivity growth; EU banks
    JEL: C11 C51 D24 G21
    Date: 2020–05–19
  25. By: Cristina Arellano; Yan Bai; Gabriel Mihalache
    Abstract: The COVID-19 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; COVID-19
    JEL: E52 F34 F41
    Date: 2020–05–22
  26. By: Dhruv Sharma; Jean-Philippe Bouchaud; Marco Tarzia; Francesco Zamponi
    Abstract: We introduce a prototype agent-based 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 structure-less. In an intermediate regime, the economy is stable with very low bankruptcy rate and no aggregate-level crises. This third regime displays a rich phenomenology: the system spontaneously and dynamically self-organizes 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
  27. By: Hoppe-Wewetzer, Heidrun C.; Siemering, Christian
    Abstract: This paper investigates the incentives of a credit rating agency (CRA) to generate accurate ratings under an advertisement-based business model. We study a two-period endogenous reputation model in which the CRA can choose to provide private effort in evaluating financial products in each period. We show that the advertisement-based 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

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