nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒07‒13
thirty-two papers chosen by

  1. Re-evaluating cryptocurrencies' contribution to portfolio diversification -- A portfolio analysis with special focus on German investors By Tim Schmitz; Ingo Hoffmann
  2. Implications of Stochastic Transmission Rates for Managing Pandemic Risks By Harrison Hong; Neng Wang; Jinqiang Yang
  3. Artificial Intelligence in Asset Management By Bartram, Söhnke M; Branke, Jürgen; Motahari, Mehrshad
  4. 'Too central to fail' firms in bi-layered financial networks: Evidence of linkages from the US corporate bond and stock markets By Mishra, Abinash; Srivastava, Pranjal; Chakrabarti, Anindya S.
  5. Public-Private Partnership in the Management of Natural Disasters: A Review By Selene Perazzini
  6. Risk attitudes over small and large stakes recalibrated By Eduardo Zambrano
  7. Risk-Taking, Competition and Uncertainty: Do CoCo Bonds Increase the Risk Appetite of Banks? By Fatouh, Mahmoud; Neamtu, Ioana; van Wijnbergen, Sweder
  8. Corona shutdown and bankruptcy risk By Holtemöller, Oliver; Muradoglu, Yaz Gulnur
  9. On the profitability of selfish blockchain mining under consideration of ruin By Hansjörg Albrecher; Pierre-Olivier Goffard
  10. Necessary Evidence For A Risk Factor’s Relevance By Alexander M. Chinco; Samuel M. Hartzmark; Abigail B. Sussman
  11. Resolution Funding; Who Pays When Financial Institutions Fail? By Oana M Croitoru; Marc C Dobler; Johan Molin
  12. A Public-Private Insurance Model for Natural Risk Management: an Application to Seismic and Flood Risks on Residential Buildings in Italy By Selene Perazzini; Giorgio Stefano Gnecco; Fabio Pammolli
  13. An Adaptive Recursive Volatility Prediction Method By Nicklas Werge; Olivier Wintenberger
  14. Higher-Order Income Risk over the Business Cycle By Busch, Christopher; Ludwig, Alexander
  15. Relative utility bounds for empirically optimal portfolios By Dmitry B. Rokhlin
  16. Building Better Retirement Systems in the Wake of the Global Pandemic By Olivia S. Mitchell
  17. In Search of Distress Risk in Emerging Markets By Gonzalo Asis; Anusha Chari; Adam Haas
  18. The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy By Nawaf Almaskati; Ron Bird; Yue Lu; Danny Leung
  19. Aggregate Risk or Aggregate Uncertainty? Evidence from UK Households By Michelacci, Claudio; Paciello, Luigi
  20. Tail events, emotions and risk taking By Brice Corgnet; Camille Cornand; Nobuyuki Hanaki
  21. The Covid-19 Pandemic and Corporate Dividend Policy By Cejnek, Georg; Randl, Otto; Zechner, Josef
  22. A New Approach to Quantifying, Reducing and Insuring Cyber Risk: Preliminary Analysis and Proposal for Further Research By Bublil, Shalom; Gandal, Neil; Riordan, Michael
  23. Long-term stock returns in Brazil: volatile equity returns for U.S.-like investors By Eurilton Araujo; Ricardo D. Brito, Antonio Z. Sanvicente
  24. Central Counterparty Default Waterfalls and Systemic Loss By Mark Paddrik; Simpson Zhang
  25. Economic Uncertainty Before and During the COVID-19 Pandemic By David Altig; Scott R. Baker; Jose Maria Barrero; Nicholas Bloom; Philip Bunn; Scarlet Chen; Steven J. Davis; Julia Leather; Brent H. Meyer; Emil Mihaylov; Paul Mizen; Nicholas B. Parker; Thomas Renault; Pawel Smietanka; Greg Thwaites
  26. Asset Pricing vs Asset Expected Returning in Factor-Portfolio Models By Favero, Carlo A.; Melone, Alessandro
  27. Risk Attitudes and Human Mobility during the COVID-19 Pandemic By Ho Fai Chan; Ahmed Skali; David Savage; David Stadelmann; Benno Torgler
  28. A Coronavirus Asset Pricing Model: The Role of Skewness By Delis, Manthos; Savva, Christos; Theodossiou, Panayiotis
  29. Growth-and-Risk Trade-off By Gadea Rivas, Maria Dolores; Laeven, Luc; Pérez-Quirós, Gabriel
  30. Model-free bounds for multi-asset options using option-implied information and their exact computation By Ariel Neufeld; Antonis Papapantoleon; Qikun Xiang
  31. The importance of compound risk in the nexus of COVID-19, climate change and finance By Irene Monasterolo; Monica Billio; Stefano Battiston
  32. Nonparametric Tests of Tail Behavior in Stochastic Frontier Models By William C. Horrace; Yulong Wang

  1. By: Tim Schmitz; Ingo Hoffmann
    Abstract: In this paper, we investigate whether mixing cryptocurrencies to a German investor portfolio improves portfolio diversification. We analyse this research question by applying a (mean variance) portfolio analysis using a toolbox consisting of (i) the comparison of descriptive statistics, (ii) graphical methods and (iii) econometric spanning tests. In contrast to most of the former studies we use a (broad) customized, Equally-Weighted Cryptocurrency Index (EWCI) to capture the average development of a whole ex ante defined cryptocurrency universe and to mitigate possible survivorship biases in the data. According to Glas/Poddig (2018), this bias could have led to misleading results in some already existing studies. We find that cryptocurrencies can improve portfolio diversification in a few of the analyzed windows from our dataset (consisting of weekly observations from 2014-01-01 to 2019-05-31). However, we cannot confirm this pattern as the normal case. By including cryptocurrencies in their portfolios, investors predominantly cannot reach a significantly higher efficient frontier. These results also hold, if the non-normality of cryptocurrency returns is considered. Moreover, we control for changes of the results, if transaction costs/illiquidities on the cryptocurrency market are additionally considered.
    Date: 2020–06
  2. By: Harrison Hong; Neng Wang; Jinqiang Yang
    Abstract: The reproduction number R 0 plays an outsized role in managing Covid-19 risks. We show that it is an insufficient statistic, particularly for financial risks, because transmissions are stochastic due to unpredictable environmental factors. We introduce aggregate transmission shocks into a widely-used epidemic model and link firm valuation to epidemic data using an asset-pricing framework. Pooling early Covid-19 data for 16 high-risk countries, we estimate both a large R 0 and transmission volatility. R 0 mismeasures the benefits of lockdowns since it misses the permanence of initial transmission shocks and gives a poor approximation of conditional infection forecasts. R 0 also understates Covid-19 risks to financial markets because transmission volatility is as important for firm-value damages. We then value a potential vaccine in our framework.
    JEL: G12 G32 Q5
    Date: 2020–05
  3. By: Bartram, Söhnke M; Branke, Jürgen; Motahari, Mehrshad
    Abstract: Artificial intelligence (AI) has a growing presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and returns forecasts and under more complex constraints. Trading algorithms utilize AI to devise novel trading signals and execute trades with lower transaction costs, and AI improves risk modelling and forecasting by generating insights from new sources of data. Finally, robo-advisors owe a large part of their success to AI techniques. At the same time, the use of AI can create new risks and challenges, for instance as a result of model opacity, complexity, and reliance on data integrity.
    Keywords: Algorithmic trading; decision trees; deep learning; evolutionary algorithms; Lasso; Machine Learning; neural networks; NLP; random forests; SVM
    JEL: G11 G17
    Date: 2020–03
  4. By: Mishra, Abinash; Srivastava, Pranjal; Chakrabarti, Anindya S.
    Abstract: Complex mutual dependencies of asset returns are recognized to contribute to systemic risk. A growing literature emphasizes that identification of vulnerable firms is a fundamental requirement for mitigating systemic risk in a given asset market. However, in reality, firms are generally active in multiple asset markets with potentially different degrees of vulnerabilities in different markets. Therefore, to assess combined risks of the firms, we need to know how systemic risk measures of firms are related across markets? In this paper, we answer this question by studying US firms that are active in both stock as well as corporate bond markets. The main results are twofold. One, firms that exhibit higher systemic risk in the stock market also tend to exhibit higher systemic risk in the bond market. Two, systemic risk within an asset category is related to firm size, indicating that `too-big-to-fail’ firms also tend to be `too-central-to fail'. Our results are robust with respect to choose of asset classes, maturity horizons, model selection, time length of the data as well as controlling for all major market level factors. These results have prominent policy implications for identification of vulnerabilities and targeted interventions in financial networks.
    Date: 2020–06–20
  5. By: Selene Perazzini
    Abstract: Natural hazards can considerably impact the overall society of a country. As some degree of public sector involvement is always necessary to deal with the consequences of natural disasters, central governments have increasingly invested in proactive risk management planning. In order to empower and involve the whole society, some countries have established public-private partnerships, mainly with the insurance industry, with satisfactorily outcomes. Although they have proven necessary and most often effective, the public-private initiatives have often incurred high debts or have failed to achieved the desired risk reduction objectives. We review the role of these partnerships in the management of natural risks, with particular attention to the insurance sector. Among other country-specific issues, poor risk knowledge and weak governance have widely challenged the initiatives during the recent years, while the future is threatened by the uncertainty of climate change and unsustainable development. In order to strengthen the country's resilience, a greater involvement of all segments of the community, especially the weakest layers, is needed and the management of natural risks should be included in a sustainable development plan.
    Date: 2020–06
  6. By: Eduardo Zambrano (Department of Economics, California Polytechnic State University)
    Abstract: In this paper I provide bounds on the marginal rate of substitution between losing $x and winning $y, starting from wealth level $w, for a risk averse individual that rejects a small stake gamble for a range of initial wealth levels. I then prove a theorem that can be used to identify the kinds of large stakes that would be rejected by any such individual. The theorems allow us to understand how much risk aversion is embedded in anyone's rejections of certain small stakes gambles and provide tighter connections between the results in Rabin (2000) and the received theory of decision making under risk.
    Keywords: Expected Utility Theory, Risk Aversion Calibration
    JEL: D81
    Date: 2019
  7. By: Fatouh, Mahmoud; Neamtu, Ioana; van Wijnbergen, Sweder
    Abstract: We assess the impact of contingent convertible (CoCo) bonds and the wealth transfers they imply conditional on conversion on risk-taking behaviour of the issuing bank. We also test for regulatory arbitrage: do banks by issuing CoCos try to maintain risk taking incentives when regulators reduce them by insisting on higher capitalization ratios? While we test for and reject sample selection bias, we show that CoCo bonds issuance has a strong positive effect on risk-taking behaviour, and so do conversion parameters that reduce dilution of existing shareholders upon conversion. Higher volatility amplifies the impact of CoCos on risk taking.
    Keywords: Bank Capital Structure; Contingent Convertible Bonds; Risk Taking
    JEL: G01 G11 G21 G32
    Date: 2020–03
  8. By: Holtemöller, Oliver; Muradoglu, Yaz Gulnur
    Abstract: This paper investigates the consequences of shutdowns during the Corona crisis on the risk of bankruptcy for firms in Germany and United Kingdom. We use financial statements from the period 2014 to 2018 to predict how pervasive risk of bankruptcy becomes for micro, small, medium, and large firms due to shutdown measures. We estimate distress for firms using their capacity to service their debt. Our results indicate that under three months of shutdown almost all firms in shutdown industries face high risk of bankruptcy. In Germany, about 99% of firms in shutdown industries and in the UK about 98% of firms in shutdown industries are predicted to be under distress. The furlough schemes reduce the risk of bankruptcy only marginally to 97% of firms in shutdown industries in Germany and 95% of firms in shutdown industries in the United Kingdom in case of a three-month shutdown. In sectors that are not shutdown under conservative estimates of contagion of sales losses, our results indicate considerable risk of widespread bankruptcies ranging from 76% of firms in Germany to 69% of firms in the United Kingdom. These early findings suggest that the impact of corona crisis on corporate sector via shutdowns can be severe and subsequent policy should be designed accordingly.
    Date: 2020
  9. By: Hansjörg Albrecher; Pierre-Olivier Goffard (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)
    Abstract: Mining blocks on a blockchain equipped with a proof of work consensus protocol is well-known to be resource-consuming. A miner bears the operational cost, mainly electricity consumption and IT gear, of mining, and is compensated by a capital gain when a block is discovered. This paper aims at quantifying the profitability of mining when the possible event of ruin is also considered. This is done by formulating a tractable stochastic model and using tools from applied probability and analysis, including the explicit solution of a certain type of advanced functional differential equation. The expected profit at a future time point is determined for the situation when the miner follows the protocol as well as when he/she withholds blocks. The obtained explicit expressions allow to analyze the sensitivity with respect to the different model ingredients and to identify conditions under which selfish mining is a strategic advantage.
    Keywords: Blockchain,miner,cryptocurrency,ruin theory,dual risk model
    Date: 2020–05–29
  10. By: Alexander M. Chinco; Samuel M. Hartzmark; Abigail B. Sussman
    Abstract: Textbook finance theory assumes that investors strategically try to insure themselves against bad future states of the world when forming portfolios. This is a testable assumption, surveys are ideally suited to test it, and we develop a framework for doing so. Our framework combines survey experiments with field data to test this assumption as it pertains to any candidate risk factor. We study consumption growth to demonstrate the approach. While participants strategically respond to changes in the mean and volatility of stock returns when forming their portfolios, there is no evidence that investors view this canonical risk factor as relevant.
    JEL: D81 D91 E21 G11 G12
    Date: 2020–05
  11. By: Oana M Croitoru; Marc C Dobler; Johan Molin
    Abstract: A key element of the international reform agenda since the Global Financial Crisis has been to strengthen resolution regimes and make government bailouts the last, not first, resort. A new international standard prescribes a range of tools, powers, and funding arrangements needed to resolve “any financial institution that could be systemically significant or critical if it fails.” It recommends having resolution funding arrangements set up in advance, “so that authorities are not constrained to rely on public ownership or bail-out funds as a means of resolving firms.” It leaves open significant flexibility with respect to the arrangements that would provide the resources authorities will need to carry out effective resolution. This paper offers a framework for weighing the relative advantages of different resolution funding options that could meet the standard. It presents the main developments to date and discusses the advantages and disadvantages of different options.
    Keywords: Bank resolution;Central banks and their policies;Deposit insurance;Bank bailouts;Financial crises;Systemically important financial institutions;Crisis resolution;resolution, resolution funds, systemic financial institutions, deposit insurance funds, financial crisis
    Date: 2018–08–16
  12. By: Selene Perazzini; Giorgio Stefano Gnecco; Fabio Pammolli
    Abstract: This paper proposes a public-private insurance scheme for earthquakes and floods in Italy in which property-owners, the insurer and the government co-operate in risk financing. Our model departs from the existing literature by describing a public-private insurance intended to relieve the financial burden that natural events place on governments, while at the same time assisting individuals and protecting the insurance business. Hence, the business is aiming at maximizing social welfare rather than profits. Given the limited amount of data available on natural risks, expected losses per individual have been estimated through risk-modeling. In order to evaluate the insurer's loss profile, spatial correlation among insured assets has been evaluated by means of the Hoeffding bound for r-dependent random variables. Though earthquakes generate expected losses that are almost six times greater than floods, we found that the amount of public funds needed to manage the two perils is almost the same. We argue that this result is determined by a combination of the risk aversion of individuals and the shape of the loss distribution. Lastly, since earthquakes and floods are uncorrelated, we tested whether jointly managing the two perils can counteract the negative impact of spatial correlation. Some benefit from risk diversification emerged, though the probability of the government having to inject further capital might be considerable. Our findings suggest that, when not supported by the government, private insurance might either financially over-expose the insurer or set premiums so high that individuals would fail to purchase policies.
    Date: 2020–06
  13. By: Nicklas Werge (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - Université de Paris); Olivier Wintenberger (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - Université de Paris)
    Abstract: The Quasi-Maximum Likelihood (QML) procedure is widely used for statistical inference due to its robustness against overdispersion. However, while there are extensive references on non-recursive QML estimation, recursive QML estimation has attracted little attention until recently. In this paper, we investigate the convergence properties of the QML procedure in a general conditionally heteroscedastic time series model, extending the classical offline optimization routines to recursive approximation. We propose an adaptive recursive estimation routine for GARCH models using the technique of Variance Targeting Estimation (VTE) to alleviate the convergence difficulties encountered in the usual QML estimation. Finally, empirical results demonstrate a favorable trade-off between the ability to adapt to time-varying estimates and stability of the estimation routine.
    Keywords: recursive algorithm,quasi-likelihood,volatility models,GARCH,prediction method,stock index
    Date: 2020–06–02
  14. By: Busch, Christopher; Ludwig, Alexander
    Abstract: We extend the canonical income process with persistent and transitory risk to shock distributions with left-skewness and excess kurtosis, to which we refer as higher- order risk. We estimate our extended income process by GMM for household data from the United States. We find countercyclical variance and procyclical skewness of persistent shocks. All shock distributions are highly leptokurtic. The existing tax and transfer system reduces dispersion and left-skewness of shocks. We then show that in a standard incomplete-markets life-cycle model, first, higher-order risk has sizable welfare implications, which depend crucially on risk attitudes of households; second, higher-order risk matters quantitatively for the welfare costs of cyclical idiosyncratic risk; third, higher-order risk has non-trivial implications for the degree of self-insurance against both transitory and persistent shocks.
    Keywords: Business cycle; GMM estimation; labor income risk; Life-Cycle Model; Persistent and Transitory Income Shocks; Risk attitudes; Skewness
    JEL: D31 E24 E32 H31 J31
    Date: 2020–03
  15. By: Dmitry B. Rokhlin
    Abstract: We consider a single-period portfolio selection problem for an investor, maximizing the expected ratio of the portfolio utility and the utility of a best asset taken in hindsight. The decision rules are based on the history of stock returns with unknown distribution. Assuming that the utility function is Lipschitz or H\"{o}lder continuous (the concavity is not required), we obtain high probability utility bounds under the sole assumption that the returns are independent and identically distributed. These bounds depend only on the utility function, the number of assets and the number of observations. For concave utilities similar bounds are obtained for the portfolios produced by the exponentiated gradient method. Also we use statistical experiments to study risk and generalization properties of empirically optimal portfolios. Herein we consider a model with one risky asset and a dataset, containing the stock prices from NYSE.
    Date: 2020–06
  16. By: Olivia S. Mitchell
    Abstract: In the wake of the global pandemic known as COVID-19, retirees, along with those hoping to retire someday, have been shocked into a new awareness of the need for better risk management tools to handle longevity and aging. This paper offers an assessment of the status quo prior to the spread of the coronavirus, evaluates how retirement systems are faring in the wake of the shock. Next we examine insurance and financial market products that may render retirement systems more resilient for the world’s aging population. Finally, potential roles for policymakers are evaluated.
    JEL: G23 H55 J26 J32
    Date: 2020–05
  17. By: Gonzalo Asis; Anusha Chari; Adam Haas
    Abstract: This paper employs a novel multi-country dataset of corporate defaults to develop a model of distress risk specific to emerging markets. The data suggest that global financial variables such as US interest rates and shifts in global liquidity and risk aversion have significant predictive power for forecasting corporate distress risk in emerging markets. We document a positive distress risk premium in emerging market equities and show that the impact of a global "risk-off" environment on default risk is greater for firms whose returns are more sensitive to a composite global factor.
    JEL: F3 G12 G15 G33
    Date: 2020–05
  18. By: Nawaf Almaskati (University of Waikato); Ron Bird (University of Waikato); Yue Lu (University of Waikato); Danny Leung (University of Technology Sydney)
    Abstract: In a sample covering bankruptcies in public US firms in the period 2000 to 2015, we find that the addition of governance variables significantly improves the classification power and prediction accuracy of various bankruptcy prediction models. We also find that while adding governance variables improves the performance of bankruptcy prediction models, the additional explanatory power provided by adding the governance measures improves the further we are from bankruptcy, which implies that governance variables tend to provide earlier and more accurate warnings of the firm’s bankruptcy potential. Our analysis of five commonly used statistical methods in the literature showed that regardless of the bankruptcy model used, hazard analysis provides the best classification and out-of-sample forecast accuracy among the parametric methods. Nevertheless, non-parametric methods such as neural networks or data envelopment analysis appear to provide better classification accuracy regardless of the model selected.
    Keywords: corporate governance; bankruptcy studies; default prediction; non-parametric methods
    JEL: D81 G10 G14 G30 G32
    Date: 2019–07–31
  19. By: Michelacci, Claudio; Paciello, Luigi
    Abstract: Using the Bank of England Inflation Attitudes Survey we find that households with preferences for higher inflation and higher interest rates have lower expected inflation. The wedge is mildly correlated with existing measures of uncertainty and increases after major economic events such as the failure of Lehman Brothers or the Brexit referendum. We interpret the wedge as due to Knightian uncertainty about future monetary policy and the underlying economic environment. If households had treated uncertainty as measurable risk, consumption and output would have been around 1 percent higher both during the Great Recession and in recent years.
    Date: 2020–04
  20. By: Brice Corgnet (Univ Lyon, emlyon business school, GATE UMR 5824, F-69130 Ecully, France); Camille Cornand (Univ Lyon, CNRS, GATE UMR 5824, F-69130 Ecully, France); Nobuyuki Hanaki (Institute for Social and Economic Research, Osaka University)
    Abstract: Recent works have shown how tail events could account for ?nancial anomalies such as the equity premiumpuzzle. These models do not explore, however, why investors would discount tail risk so heavily. We take on this challenge by designing a novel tail-event experiment to assess both investors’ behavioral and physiological reactions. We show that investors who observe the tail event without su?ering losses tend to decrease their pricing of the asset subsequently. By contrast, loss-averse investors who su?er tail losses tend to increase their bids. This response is especially pronounced for those who exhibit a strong emotional response to tail losses. This demonstrates the key role played by emotions in in?uencing investors’ response to tail events. Finally, investors who exhibit high anticipatory arousal, as measured with electrodermal activity, posted lower bids and were less likely to su?er tail losses and go bankrupt. They also achieved higher earnings when tail events occurred frequently. This ?nding contrasts with the common view that investors should silence their emotions.
    Keywords: tail events, emotions and risk
    JEL: C91 D87 D91
    Date: 2020
  21. By: Cejnek, Georg; Randl, Otto; Zechner, Josef
    Abstract: Important dimensions of dividend behavior are not well understood. How do dividends behave in extreme states of the world? Why is the risk premium on dividend claims so high? Would dividend bans in crisis states have plausible effects on firms' cost of capital? In this paper we use evidence from the ongoing Corona pandemic to shed light on these questions. We find that, contrary to the folklore that near-future dividends are smoother than earnings or share prices, the opposite is true in disaster states. It appears that firms do not fulfill their role as liquidity intermediaries for their shareholders in precisely those states, in which predictable cash payments would be valued most highly. This is consistent with the apparent puzzle that near-maturity dividend futures have provided investors with "anomalously" high returns in the past years. In light of the recent Corona disaster, these risk premia are consistent with compensation for negative co-skewness and exposure to disaster risk. Our findings imply that policy setters who consider banning dividends in a crisis should take into account the potential effect this is likely to have on firms' future cost of capital.
    Date: 2020–04
  22. By: Bublil, Shalom; Gandal, Neil; Riordan, Michael
    Abstract: Few would dispute that cyber risk is a very serious problem for the global economy and for society. But there is a "disconnect" between acknowledgement of the problem and action to address the problem. What is the relationship between vulnerabilities, preventive measures, and security incidents, like the leaking of sensitive data (say credit card information) to the web? To the best of our knowledge, little if anything is known about the relationship among these variables and no one has examined this issue empirically at the micro level, that is, at the level of the firm. In this paper, we put together a remarkable and unique cross-sectional data set at the firm level that includes information on vulnerabilities, attempted email attacks, incidents (breaches), precautions (security measures.) and firm characteristics. The data set contains slightly under 1000 small and medium firms in the U.K. We empirically examine the data and show that there are meaningful correlations among incidents and the other variables. Finally, we estimate a reduced form model with incidents as the dependent variable to illustrate the potential from employing such data.
    Keywords: cyber insurance; cyber risk; cyber security; empirical
    Date: 2020–03
  23. By: Eurilton Araujo; Ricardo D. Brito, Antonio Z. Sanvicente
    Abstract: This paper tells the history of Brazilian stock market returns since the creation of the Ibovespa (the main Brazilian stock market index). From 1968 to 2019, the arithmetic mean return of the Brazilian stock market is 21.3% per year. The equity premium is 20.1% per year, with a huge standard deviation of 67% . Surprisingly, such numbers are compatible with investors’ risk aversions that accommodate the very different U.S. market evidence, reinforcing the belief that national investors are similar in nature. The equity premium has been higher in Brazil than in the U.S., but the much higher Brazilian volatility discourages heavier investments in stocks.
    Keywords: Equity returns; Equity risk premium; Emerging market; Lifetime portfolio selection
    JEL: E21 G10 G12
    Date: 2020–06–19
  24. By: Mark Paddrik (Office of Financial Research); Simpson Zhang (Office of the Comptroller of the Currency)
    Abstract: Central counterparty default waterfalls act as last lines of defense in over-the-counter markets by managing and allocating resources to cover defaults of clearing members and clients. However, central counterparties face competing objectives in setting up their default waterfalls. In this paper we evaluate the trade-offs between default waterfall resiliency and central clearing, using a unique and comprehensive dataset containing all U.S. cleared and bilateral credit default swap positions. We evaluate the resiliency of different default waterfall designs, accounting for the interconnectedness of payments in the system, the presence of client clearing obligations for members, and the distribution of losses among market participants.
    Keywords: central counterparty, systemic risk, default waterfall, financial networks, credit default swaps
    Date: 2020–06–18
  25. By: David Altig; Scott R. Baker; Jose Maria Barrero; Nicholas Bloom; Philip Bunn; Scarlet Chen; Steven J. Davis; Julia Leather; Brent H. Meyer; Emil Mihaylov; Paul Mizen; Nicholas B. Parker; Thomas Renault; Pawel Smietanka; Greg Thwaites
    Abstract: We consider several economic uncertainty indicators for the US and UK before and during the COVID-19 pandemic: implied stock market volatility, newspaper-based economic policy uncertainty, twitter chatter about economic uncertainty, subjective uncertainty about future business growth, and disagreement among professional forecasters about future GDP growth. Three results emerge. First, all indicators show huge uncertainty jumps in reaction to the pandemic and its economic fallout. Indeed, most indicators reach their highest values on record. Second, peak amplitudes differ greatly – from a rise of around 100% (relative to January 2020) in two-year implied volatility on the S&P 500 and subjective uncertainty around year-ahead sales for UK firms to a 20-fold rise in forecaster disagreement about UK growth. Third, time paths also differ: Implied volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices began to recover. In contrast, broader measures of uncertainty peaked later and then plateaued, as job losses mounted, highlighting the difference in uncertainty measures between Wall Street and Main Street.
    JEL: E0
    Date: 2020–06
  26. By: Favero, Carlo A.; Melone, Alessandro
    Abstract: Standard factor-portfolio models focus on returns and leave prices undetermined. This approach ignores information contained in the time-series of asset prices, relevant for long-term investors and for detecting potential mis-pricing. To address this issue, we provide a new (co-)integrated methodology to factor modeling based on both prices and returns. Given a long-run relationship between the value of buy-and-hold portfolios in test assets and factors, we argue that a term---naturally labeled as Equilibrium Correction Term (ECT)---should be included when regressing returns on factors. We also propose to validate factor models by the existence of such a term. Empirically, we show that the ECT predicts equity returns, both in-sample and out-of-sample.
    Keywords: Dynamic Factor-Portfolio Models; Equilibrium Correction Term; mispricing; return predictability
    JEL: C38 G11 G17
    Date: 2020–03
  27. By: Ho Fai Chan; Ahmed Skali; David Savage; David Stadelmann; Benno Torgler
    Abstract: Behavioral responses to pandemics are less shaped by actual mortality or hospitalization risks than they are by risk attitudes. We explore human mobility patterns as a measure of behavioral responses during the COVID-19 pandemic. Our results indicate that risk-taking attitude is a critical factor in predicting reduction in human mobility and increase social confinement around the globe. We find that the sharp decline in movement after the WHO (World Health Organization) declared COVID-19 to be a pandemic can be attributed to risk attitudes. Our results suggest that regions with risk-averse attitudes are more likely to adjust their behavioral activity in response to the declaration of a pandemic even before most official government lockdowns. Further understanding of the basis of responses to epidemics, e.g., precautionary behavior, will help improve the containment of the spread of the virus.
    Date: 2020–06
  28. By: Delis, Manthos; Savva, Christos; Theodossiou, Panayiotis
    Abstract: We study an equilibrium risk and return model to explore the effects of the coronavirus crisis and associated skewness. We derive the moment and equilibrium equations, specifying skew-ness price of risk as an additive component of the effect of variance on mean expected return. We estimate our model using the flexible skewed generalized error distribution, for which we derive the distribution of returns and the likelihood function. Using S&P 500 Index returns from January 1990 to mid-May 2020, our results show that the coronavirus crisis generated the most negative reaction in the skewness price of risk, more negative even than the subprime crisis.
    Keywords: Asset pricing; Risk and return; Skewness; Coronavirus crisis; Subprime crisis
    JEL: C32 C51 G01 G11 G12
    Date: 2020–06–04
  29. By: Gadea Rivas, Maria Dolores; Laeven, Luc; Pérez-Quirós, Gabriel
    Abstract: We study the effects of credit over the business cycle, distinguishing between expansions and contractions. We find that there is a growth and risk trade-off in the pace of credit growth over the business cycle. While rapid credit growth tends to be followed by deeper recessions, we also find that credit growth has a positive impact on the duration of expansions. This poses a trade-off for the policymaker: Limiting the buildup of financial risk to avoid a deep recession can negatively affect the cumulation of economic growth during the expansion. We show that intermediate levels of credit growth maximize long-term growth while limiting volatility. Macroprudential policies should be used to manage this growth and risk trade-off, striking a balance between allowing expansions to last longer and avoiding deep recessions.
    Keywords: business cycles; credit growth; macroprudential policies
    JEL: C22 E32 E61
    Date: 2020–03
  30. By: Ariel Neufeld; Antonis Papapantoleon; Qikun Xiang
    Abstract: We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested in the computation of model-free upper and lower bounds for their arbitrage-free prices. We work in a completely realistic setting, in that we only assume the knowledge of traded prices for other single- and multi-asset derivatives, and even allow for the presence of bid-ask spread in these prices. We provide a fundamental theorem of asset pricing for this market model, as well as a superhedging duality result, that allows to transform the abstract maximization problem over probability measures into a more tractable minimization problem over vectors, subject to certain constraints. Then, we recast this problem into a linear semi-infinite optimization problem, and provide two algorithms for its solution. These algorithms provide upper and lower bounds for the prices that are $\varepsilon$-optimal, as well as a characterization of the optimal pricing measures. Moreover, these algorithms are efficient and allow the computation of bounds in high-dimensional scenarios (e.g. when $d=60$). Numerical experiments using synthetic data showcase the efficiency of these algorithms, while they also allow to understand the reduction of model-risk by including additional information, in the form of known derivative prices.
    Date: 2020–06
  31. By: Irene Monasterolo (Vienna University of Economics and Business; Boston University’s Global Development Policy Initiative; Ca’ Foscari University of Venice); Monica Billio (Department of Economics, University Of Venice Cà Foscari); Stefano Battiston (Department of Economics, University Of Venice Cà Foscari; University of Zurich)
    Abstract: Current approaches to manage the COVID-19 pandemic have a narrow focus on public health and on the short-term economic and financial repercussions. This prevents us to look at how pandemic risk interplays with sustainable and inclusive development goals in the next decade. To fill this gap, we analyse how risk can compound in the nexus of non-linear interactions among pandemic, climate change and finance. We show that neglecting compound risk can lead to a massive underestimation of losses, which can be amplified by financial complexity, as well as to policies that impose unnecessary trade-offs among the economic recovery, health and climate objectives. To address these challenges, we propose an interdisciplinary research agenda to inform effective policies and improve the resilience of our socio-economic systems.
    Keywords: COVID-19, climate change, financial interconnectedness, compound risk, loss amplification, resilience policies
    JEL: G0 Q5
    Date: 2020
  32. By: William C. Horrace (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Yulong Wang (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244)
    Abstract: This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests that the unbounded component distribution has thin tails and that the component tails are equivalent. The tests are useful diagnostic tools for stochastic frontier analysis and kernel deconvolution density estimation. A simulation study and an application to a stochastic cost frontier for 6,100 US banks from 1998 to 2005 are provided. The new tests reject the normal or Laplace distributional assumptions, which are commonly imposed in the existing literature.
    Keywords: Hypothesis Testing, Production, Inefficiency, Deconvolution, Extreme Value Theory
    JEL: C12 C21 D24
    Date: 2020–06

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. 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.