nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒03‒08
twenty-two papers chosen by
Stan Miles
Thompson Rivers University

  1. Option-Implied Network Measures of Tail Contagion and Stock Return Predictability By Manuela Pedio
  2. The more the merrier? Evidence from the global financial crisis on the value of multiple requirements in bank regulation By Buckmann, Marcus; Gallego Marquez, Paula; Gimpelewicz, Mariana; Kapadia, Sujit; Rismanchi, Katie
  3. FRM Financial Risk Meter for Emerging Markets By Ben Amor, Souhir; Althof, Michael; Härdle, Wolfgang Karl
  4. Tail Risks and Forecastability of Stock Returns of Advanced Economies: Evidence from Centuries of Data By Afees A. Salisu; Rangan Gupta; Ahamuefula E. Ogbonna
  5. Machine Learning and Credit Risk: Empirical Evidence from SMEs By Alessandro Bitetto; Paola Cerchiello; Stefano Filomeni; Alessandra Tanda; Barbara Tarantino
  6. Conditional Value at Risk and Partial Moments for the Metalog Distributions By Valentyn Khokhlov
  7. Deconstructing systemic risk: A reverse stress testing approach. By Javier Ojea-Ferreiro
  8. Tail Event Driven Factor Augmented Dynamic Model By Wang, Weining; Yu, Lining; Wang, Bingling
  9. On the origin of systemic risk By Montagna, Mattia; Torri, Gabriele; Covi, Giovanni
  10. Evolving United States Stock Market Volatility: The Role of Conventional and Unconventional Monetary Policies By Vasilios Plakandaras; Rangan Gupta; Mehmet Balcilar; Qiang Ji
  11. A Top-down Stress-testing Framework for the Nonfinancial Corporate Sector By Vojtech Siuda
  12. How do secured funding markets behave under stress? Evidence from the gilt repo market By Hüser, Anne-Caroline; Lepore, Caterina; Veraart, Luitgard
  13. Forecasting Realized Volatility of International REITs: The Role of Realized Skewness and Realized Kurtosis By Matteo Bonato; Oguzhan Cepni; Rangan Gupta; Christian Pierdzioch
  14. Estimating the Value at Risk of a bank’s portfolio in sovereign bonds using a DCC-Copula model By Gomez-Gonzalez, Jose Eduardo; Gualtero-Briceño, Daniela; Melo-Velandia, Luis Fernando
  15. Forecasting During the COVID-19 Pandemic: A Structural Analysis of Downside Risk By Martin Bodenstein; Pablo Cuba-Borda; Jay Faris; Nils Gornemann
  16. Robust Estimation of Integrated Volatility By Li, M. Z.; Linton, O.
  17. It's not time to make a change: Sovereign fragility and the corporate credit risk By Fornari, Fabio; Zaghini, Andrea
  18. Development of soft commodity derivative market in function of the risk management in CEE By Kovacevic, Vlado; Subić, Jonel; Jankovic, Irena
  19. Understanding the digital security of products: An in-depth analysis By OECD
  20. Spanish non-financial corporations’ liquidity needs and solvency after the covid-19 shock By Roberto Blanco; Sergio Mayordomo; Álvaro Menéndez; Maristela Mulino
  21. The COVID-19 insolvency gap: First-round effects of policy responses on SMEs By Dörr, Julian Oliver; Murmann, Simona; Licht, Georg
  22. Efficiency of sharing liability rules: An experimental case. By Serge Garcia; Julien Jacob; Eve-Angéline Lambert

  1. By: Manuela Pedio
    Abstract: The Great Financial Crisis of 2008 – 2009 has raised the attention of policy-makers and researchers about the interconnectedness among the volatility of the returns of financial assets as a potential source of risk that extends beyond the usual changes in correlations and include transmission channels that operate through the higher order co-moments of returns. In this paper, we investigate whether a newly developed, forward-looking measure of volatility spillover risk based on option implied volatilities shows any predictive power for stock returns. We also compare the predictive performance of this measure with that of the volatility spillover index proposed by Diebold and Yilmaz (2008, 2012), which is based on realized, backward-looking volatilities instead. While both measures show evidence of in-sample predictive power, only the option-implied measure is able to produce out-of-sample forecasts that outperform a simple historical mean benchmark.
    Keywords: connectedness, volatility networks, implied volatility, realized volatility, equity return predictability, spillover risk
    JEL: G12 G17
    Date: 2021
  2. By: Buckmann, Marcus (Bank of England); Gallego Marquez, Paula (Bank of England); Gimpelewicz, Mariana (Bank of England); Kapadia, Sujit (European Central Bank); Rismanchi, Katie (Bank of England)
    Abstract: This paper assesses the value of multiple requirements in bank regulation using a novel empirical rule‑based methodology. Exploiting a dataset of capital and liquidity ratios for a sample of global banks in 2005 and 2006, we apply simple threshold-based rules to assess how different regulations individually and in combination might have identified banks that subsequently failed during the global financial crisis. Our results generally support the case for a small portfolio of different regulatory metrics. Under the objective of correctly identifying a high proportion of banks which subsequently failed, we find that a portfolio of a leverage ratio, a risk-weighted capital ratio, and a net stable funding ratio yields fewer false alarms than any of these metrics individually – and at less stringent calibrations of each individual regulatory metric. We also discuss how these results apply in different robustness exercises, including out-of-sample evaluations. Finally, we consider the potential role of market-based measures of bank capitalisation, showing that they provide complementary value to their accounting-based counterparts.
    Keywords: Banking regulation; Basel III; bank failure; global financial crisis; marketbased metrics; regulatory complexity
    JEL: G01 G18 G21 G28
    Date: 2021–01–29
  3. By: Ben Amor, Souhir; Althof, Michael; Härdle, Wolfgang Karl
    Abstract: The fast-growing Emerging Market (EM) economies and their improved transparency and liquidity have attracted international investors. However, the external price shocks can result in a higher level of volatility as well as domestic policy instability. Therefore, an efficient risk measure and hedging strategies are needed to help investors protect their investments against this risk. In this paper, a daily systemic risk measure, called FRM (Financial Risk Meter) is proposed. The FRM@ EM is applied to capture systemic risk behavior embedded in the returns of the 25 largest EMs' FIs, covering the BRIMST (Brazil, Russia, India, Mexico, South Africa, and Turkey), and thereby reflects the financial linkages between these economies. Concerning the Macro factors, in addition to the Adrian & Brunnermeier (2016) Macro, we include the EM sovereign yield spread over respective US Treasuries and the above-mentioned countries' currencies. The results indicated that the FRM of EMs' FIs reached its maximum during the US financial crisis following by COVID 19 crisis and the Macro factors explain the BRIMST' FIs with various degrees of sensibility. We then study the relationship between those factors and the tail event network behavior to build our policy recommendations to help the investors to choose the suitable market for investment and tail-event optimized portfolios. For that purpose, an overlapping region between portfolio optimization strategies and FRM network centrality is developed. We propose a robust and well-diversified tail-event and cluster risk- sensitive portfolio allocation model and compare it to more classical approaches.
    Keywords: FRM (Financial Risk Meter),Lasso Quantile Regression,Network Dynamics,Emerging Markets,Hierarchical Risk Parity
    JEL: C30 C58 G11 G15 G21
    Date: 2021
  4. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Ahamuefula E. Ogbonna (Centre for Econometric and Allied Research and Department of Statistics, University of Ibadan, Ibadan, Oyo State, Nigeria)
    Abstract: This study examines the out-of-sample predictability of market risks measured as tail risks for stock returns of eight (Canada, France, Germany, Japan, Italy, Switzerland, the United Kingdom (UK), and the United States (US)) advanced countries using a long-range monthly data of over a century. We follow the Conditional Autoregressive Value at Risk (CAViaR) of Engle and Manganelli (2004) to measure the tail risks since it utilizes the tail distribution rather the whole distribution. Consequently, we produce results for both 1% and 5% VaRs across four variants (Adaptive, Symmetric absolute value, Asymmetric slope and Indirect GARCH) of the CAViaR. Thereafter, we use relevant model diagnostics such as the Dynamic Quantile test (DQ) test and %Hits to determine the model that best fits the data. The results obtained are then used in the return predictability following the Westerlund and Narayan (2012, 2015) method which allows us to account for some salient features such as persistence, endogeneity and conditional heteroscedasticity effects. We consequently partition our models into three variants (one-predictor, two-predictor and three-predictor models) and examine their forecast performance in contrast with a driftless random walk model. Three findings are discernible from the empirical analysis. First, we find that the choice of VaR matters when determining the “best†fit CAViaR model for each return series as the outcome seems to differ between 1% and 5% VaRs. Second, the predictive model that incorporates both stock tail risk and oil tail risk produces better forecast outcomes than the one with own tail risk indicating the significance of both domestic and global risks in the return predictability of advanced countries.
    Keywords: Stock returns, Tail risks, Forecasting, Advanced equity markets
    JEL: C22 G15 G17 Q02
  5. By: Alessandro Bitetto (University of Pavia); Paola Cerchiello (University of Pavia); Stefano Filomeni (University of Essex); Alessandra Tanda (University of Pavia); Barbara Tarantino (University of Pavia)
    Abstract: In this paper we assess credit risk of SMEs by testing and comparing a classic parametric approach fitting an ordered probit model with a non-parametric one calibrating a machine learning historical random forest (HRF) model. We do so by exploiting a unique and proprietary dataset comprising granular firm-level quarterly data collected from a large European bank and an international insurance company on a sample of 810 Italian small- and medium-sized enterprises (SMEs) over the time period 2015-2017. Our results provide novel evidence that a dynamic Historical Random Forest (HRF) approach outperforms the traditional ordered probit model, highlighting how advanced estimation methodologies that use machine learning techniques can be successfully implemented to predict SME credit risk. Moreover, by using Shapley values for the first time, we are able to assess the relevance of each variable in predicting SME credit risk. Traditionally, credit risk evaluation of informationally-opaque SMEs has relied on soft information-intensive relationship banking. However, the advent of large banking conglomerates and the limits to successfully "harden" and transmit soft information across large banking organizations, challenge the traditional role of relationship banking, urging the need to evaluate SME credit risk by implementing alternative methodologies mostly based on hard information.
    Keywords: Credit Rating, SME, Historical Random Forest, Machine Learning, Relationship Banking, Soft Information
    JEL: C52 C53 D82 D83 G21 G22
    Date: 2021–02
  6. By: Valentyn Khokhlov
    Abstract: The metalog distributions represent a convenient way to approach many practical applications. Their distinctive feature is simple closed-form expressions for quantile functions. This paper contributes to further development of the metalog distributions by deriving the closed-form expressions for the Conditional Value at Risk, a risk measure that is closely related to the tail conditional expectations. It also addressed the derivation of the first-order partial moments and shows that they are convex with respect to the vector of the metalog distribution parameters.
    Date: 2021–02
  7. By: Javier Ojea-Ferreiro
    Abstract: The financial sector faces different systemic events. The early recognition of these events is a key step to monitor and track possible financial crises. Three main questions arise related to systemic risk, and they deal with their quantification, their probability of occurrence and the role of main contributors. This paper proposes a methodology based on a reverse stress test exercise to shed light on these questions. Time series and cross-section information regarding systemic risk are obtained. Further, an assessment of how these results of systemic assessment could change depending on key parameters in a Gaussian framework is undertaken and, finally, a small empirical exercise is performed.
    Keywords: Systemic risk, Expected Shortfall, financial model
    JEL: C14 C52 C53 G12 G13
    Date: 2021
  8. By: Wang, Weining; Yu, Lining; Wang, Bingling
    Abstract: A factor augmented dynamic model for analysing tail behaviour of high dimensional time series is proposed. As a first step, the tail event driven latent factors are extracted. In the second step, a VAR (Vectorautoregression model) is carried out to analyse the interaction between these factors and the macroeconomic variables. Furthermore, this methodology also provides the possibility for central banks to examine the sensitivity between macroeconomic variables and financial shocks via impulse response analysis. Then the predictability of our estimator is illustrated. Finally, forecast error variance decomposition is carried out to investigate the network effect of these variables. The interesting findings are: firstly, GDP and Unemployment rate are very much sensitive to the shock of financial tail event driven factors, while these factors are more affected by inflation and short term interest rate. Secondly, financial tail event driven factors play important roles in the network constructed by the extracted factors and the macroeconomic variables. Thirdly, there is more connectedness during financial crisis than in the stable periods. Compared with median case, the network is more dense in lower quantile level.
    Keywords: Quantile Regression,Expectile Regression,Dynamic Factor Model,Dynamic Network
    JEL: C21 C51 G01 G18 G32 G38
    Date: 2020
  9. By: Montagna, Mattia (European Central Bank); Torri, Gabriele (University of Bergamo); Covi, Giovanni (Bank of England)
    Abstract: Systemic risk in the banking sector is usually associated with long periods of economic downturn and very large social costs. On one hand, shocks coming from correlated exposures towards the real economy may induce correlation in banks’ default probabilities thereby increasing the likelihood for systemic tail events like the 2008 Great Financial Crisis. On the other hand, financial contagion also plays an important role in generating large-scale market failures, amplifying the initial shocks coming from the real economy. To study the sources of these rare phenomena, we propose a new definition of systemic risk (ie the probability of a large number of banks going into distress simultaneously) and thus we develop a multilayer microstructural model to study empirically the determinants of systemic risk. The model is then calibrated on the most comprehensive granular dataset for the euro-area banking sector, capturing roughly 96% or €23.2 trillion of euro-area banks’ total assets over the period 2014–2018. The outputs of the model decompose and quantify the sources of systemic risk showing that correlated economic shocks, financial contagion mechanisms, and their interaction are the main sources of systemic events. The results obtained with the simulation engine resemble common market-based systemic risk indicators and empirically corroborate findings from existing literature. This framework gives regulators and central bankers a tool to study systemic risk and its developments, pointing out that systemic events and banks’ idiosyncratic defaults have different drivers, hence implying different policy responses.
    Keywords: Systemic risk; financial contagion; microstructural models
    JEL: D85 G17 G33 L14
    Date: 2021–01–29
  10. By: Vasilios Plakandaras (Department of Economics, Democritus University of Thrace, Komotini, 69100, Greece); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa); Mehmet Balcilar (Eastern Mediterranean University, Famagusta, via Mersin 10, Northern Cyprus, Turkey); Qiang Ji (Institutes of Science and Development, Chinese Academy of Sciences, Beijing, China; School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, China)
    Abstract: Despite the econometric advances of the last 30 years, the effects of monetary policy stance during the boom and busts of the stock market are not clearly defined. In this paper, we use a structural heterogenous vector autoregressive (SHVAR) model with identified structural breaks to analyze the impact of both conventional and unconventional monetary policies on the U.S. stock market volatility. We find that contractionary monetary policy enhances stock market volatility, but the importance of monetary policy shocks in explaining volatility evolves across different regimes and is relative to supply shocks (and shocks to volatility itself). In comparison to business cycle fluctuations, monetary policy shocks explain a greater fraction of the variance of stock market volatility at shorter horizons, as in medium to longer horizons. Our basic findings of a positive impact of monetary policy on equity market volatility (being relatively stronger during calmer stock markets periods) is also corroborated by analyses conducted at the daily frequency based on an augmented heterogenous autoregressive model of realized volatility (HAR-RV) and a multivariate k-th order nonparametric causality-in-quantiles framework, respectively. Our results have important implications both for investors and policymakers.
    Keywords: Stock Market Volatility, Conventional and Unconventional Monetary Policies, Structural Breaks, Structural Heterogenous Vector Autoregressive Model, Multivariate Nonparametric higher-Order Causality-in-Quantiles Test, Intraday Data
    JEL: C22 C32 E32 E52 G10
    Date: 2021–02
  11. By: Vojtech Siuda
    Abstract: This paper provides a framework for conducting simulations and stress testing in the non-financial corporations sector. It relies on national accounting and uses a set of input-output tables to track the propagation of shocks between parts of the sector while staying fully consistent with the big picture framed by the core forecasting model and the underlying scenario. The simulation framework allows standard macroeconomic developments to be captured, but one-off measures such as government wage and salary compensation and loan moratoria can also be easily implemented. The main output of the simulation is a set of industry-level performance and profitability variables. These variables can be used for various types of analysis, such as credit risk modelling and profitability and liquidity analysis. Some of them - such as the forecasting of portfolio default rates via learning process - are shown in the paper. The historical default rate estimates obtained are accurate and economically sensible for the majority of industries and exhibit a high degree of reliability even under very severe economic conditions. Given its national accounting framework and its level of detail, the model can be used to support decision-making processes and to evaluate the effects of existing or planned economic policies. Two different scenarios are considered to demonstrate the benefits of the proposed approach.
    Keywords: Credit default, default rate forecast, economic shock propagation, input-output tables
    JEL: G01 G32 H63
    Date: 2020–12
  12. By: Hüser, Anne-Caroline (Bank of England); Lepore, Caterina (International Monetary Fund); Veraart, Luitgard (London School of Economics and Political Science)
    Abstract: We examine how the overnight gilt repo market operates during three episodes of liquidity stress, using novel transaction-level data on repurchase agreements on gilts. Using network analysis we document that the structure of the repo market significantly changes during stress relative to normal times, with a focus on how sectors adjust volumes, spreads and haircuts in their repo transactions. We find several common patterns in the two most recent stress episodes (the US repo turmoil in 2019 and the Covid-19 crisis in 2020): a preference for dealers and banks to transact in the cleared rather than the bilateral segment of the market, increased usage of the market by hedge funds and central counterparties increasing their reinvestment of cash margin into reverse repo.
    Keywords: Repo market; liquidity risk; financial networks; market microstructure; Brexit referendum; US repo turmoil; Covid-19 crisis
    JEL: D85 G01 G21 G23
    Date: 2021–02–26
  13. By: Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: We use an international dataset on 5-minutes interval intraday data covering nine leading markets and regions to construct measures of realized volatility, realized jumps, realized skewness, and realized kurtosis of returns of international Real Estate Investment Trusts (REITs) over the daily period of September, 2008 to August, 2020. We study out-of-sample the predictive value of realized skewness and realized kurtosis for realized volatility over and above realized jumps, where we also differentiate between measures of ``good" realized volatility and ``bad" realized volatility. We find that realized skewness and realized kurtosis significantly improve forecasting performance at a daily, weekly, and monthly forecast horizon, and that their contribution to forecasting performance outweighs in terms of significance the contribution of realized jumps. Our results have important implications for investors and policymakers.
    Keywords: REITs, International data, Realized volatility, Forecasting
    JEL: C22 C53 G15
    Date: 2021–02
  14. By: Gomez-Gonzalez, Jose Eduardo; Gualtero-Briceño, Daniela; Melo-Velandia, Luis Fernando
    Abstract: Rises in sovereign risk adversely affect banks reducing their profits and increasing their funding costs. Impacts are specially strong on banks holding important positions of government debt in the investment portfolios. This study applies a DCC-Copula model to estimate the VaR for a portfolio composed of 30 sovereign bonds from ten different countries and three different maturities. Results indicate that the model proposed in this study outperforms competing benchmark models under various back-testing criteria. The method here developed is useful for global banks holding a diversified portfolio of sovereign bonds, especially in emerging market countries in which banks mostly invest in public debt.
    Keywords: Value at Risk; Banks' market risk; Dynamic copula models; Back-testing
    JEL: C46 C52 C58 G32
    Date: 2021–02
  15. By: Martin Bodenstein; Pablo Cuba-Borda; Jay Faris; Nils Gornemann
    Abstract: The global collapse in economic activity triggered by individual and policy-mandated responses to the spread of COVID-19 is unprecedented both in scale and origin. At the time of writing, U.S. GDP is expected by professional forecasters to contract a staggering 6 percent over the course of 2020 driven by its 32 percent collapse in the second quarter (measured at an annual rate).
    Date: 2021–02–01
  16. By: Li, M. Z.; Linton, O.
    Abstract: We introduce a new method to estimate the integrated volatility (IV) based on noisy high-frequency data. Our method employs the ReMeDI approach introduced by Li and Linton (2021a) to estimate the moments of the microstructure noise and thereby eliminate their influence, and the pre-averaging method to target the volatility parameter. The method is robust: it can be applied when the efficient price exhibits stochastic volatility and jumps, the observation times are random and endogenous, and the noise process is nonstationary, autocorrelated and dependent on the efficient price. We derive the limit distribution for the proposed estimators under infill asymptotics in a general setting. Our simulation and empirical studies demonstrate the robustness, accuracy and computational efficiency of our estimators compared to several alternatives recently proposed in the literature.
    Date: 2021–02–24
  17. By: Fornari, Fabio; Zaghini, Andrea
    Abstract: Relying on a perspective borrowed from monetary policy announcements and introducing an econometric twist in the traditional event study analysis, we doc- ument the existence of an "event risk transfer", namely a significant credit risk transmission from the sovereign to the corporate sector after a sovereign rating downgrade. We find that after the delivery of the downgrade, corporate CDS spreads rise by 36% per annum and there is a widespread contagion across coun- tries, in particular among those which were most exposed to the sovereign debt crisis. This effect exists on top of the standard relation between sovereign and corporate credit risk.
    Keywords: sovereign rating,corporate credit risk,CDS spreads
    JEL: G15 G32 G38
    Date: 2021
  18. By: Kovacevic, Vlado; Subić, Jonel; Jankovic, Irena
    Abstract: This aim of this paper is to analyse possibilities and potential effects of soft commodity derivative market on the development of risk management practice within the CEE. Agricultural producers and other participants in the soft commodity market in CEE are lacking local commodity market. As a consequence, they are relying on hedging strategies on remote derivative markets that results in basis risk. The local soft commodity derivative market with delivery in CEE ports could significantly improve the risk management practice. One of the most important barriers in developing commodity derivatives market is market liquidity. Joint commodity market between different commodity exchanges in the CEE could lead to increase of necessary liquidity. Attempts to develop commodity derivative markets in individual countries within the region were proven to be inefficient lacking the volume of trade. Methodology used in this paper is based on relevant literature review, consultation with experts in commodity tradeand market participants and descriptive statistics applied in order to determine grain price volatility. Results of the research indicate that grain price volatility is high causing the need for application of hedging strategies at the commodity exchanges markets. Second, new EU common regulative is providing improved framework for joint commodity exchange clearing by single clearinghouse. Established market with delivery on Black See ports is of special importance for regional stakeholders.
    Keywords: derivative commodity exchanges, hedging strategies, commodity market, futures contract, basis risk.
    JEL: G23 Q14 Q2
    Date: 2020–11–19
  19. By: OECD
    Abstract: Economies and societies are increasingly reliant upon “smart products” that contain code and can connect to one another, e.g. through the Internet. Recent cyber-attacks such as Mirai, WannaCry, NotPetya and SolarWinds have underlined that the exploitation of vulnerabilities in smart products can have severe economic and social consequences. Such attacks increasingly threaten users’ safety and well-being, as well. This report shows that economic factors play an important role in the relative “insecurity” of smart products. It develops an analytical framework based on the value chain and lifecycle of smart products, and applies the framework to three case studies: computers and smartphones, consumer Internet of Things (IoT) devices and cloud services. It demonstrates that complex and opaque value chains lead to a misallocation of responsibility for digital security risk management, while significant information asymmetries and externalities often limit stakeholders’ ability to behave optimally.
    Date: 2021–02–09
  20. By: Roberto Blanco (Banco de España); Sergio Mayordomo (Banco de España); Álvaro Menéndez (Banco de España); Maristela Mulino (Banco de España)
    Abstract: The COVID-19 pandemic is exerting an unprecedented adverse impact on economic activity and, in particular, on firms’ income. In some cases this means firms’ income is insufficient to meet payments to which they have committed. This article presents the results of an exercise simulating Spanish non-financial corporations’ liquidity needs for the four quarters of this year. The needs derive both from the possible shortfalls caused by developments in operating activity, and from investments in fixed assets and debt repayments. According to the results, these liquidity needs, between April and December, might exceed €230 billion. It is estimated that, through the public guarantee programmes for lending to firms, almost three-quarters of this shortfall might be covered. To finance the remainder, companies could use their liquidity buffers and/or resort to new debt without public guarantee. In this respect, it should be borne in mind that, in recent months, firms with better access to credit have managed to raise a high volume of funds without resorting to public guarantees. Further, despite the unprecedented fall in business turnover, it is estimated that a significant percentage of companies (more than 40%) would be able to withstand this situation without undergoing a deterioration in their financial position. However, at the remaining companies, the fall-off in activity would have led to significant increases in their level of financial vulnerability, more sharply within the SME segment and especially among the firms in the sectors most affected by the pandemic, such as tourism and leisure, motor vehicles, and transport and storage.
    Keywords: COVID-19, firms’ liquidity needs, credit, guarantees, insolvency risk
    JEL: E51 E52 G21
    Date: 2020–08
  21. By: Dörr, Julian Oliver; Murmann, Simona; Licht, Georg
    Abstract: COVID-19 placed a special role to fiscal policy in rescuing companies short of liquidity from insolvency. In the first months of the crisis, SMEs as the backbone of Europe's real economy benefited from large and mainly indiscriminate aid measures. Avoiding business failures in a whatever it takes fashion contrasts, however, with the cleansing mechanism of economic crises: a mechanism which forces unviable firms out of the market, thereby reallocating resources efficiently. By focusing on firms' pre-crisis financial standing, we estimate the extent to which the policy response induced an insolvency gap and analyze whether the gap is characterized by firms which had already struggled before the pandemic. With the policy measures being focused on smaller firms, we also examine whether this insolvency gap differs with respect to firm size. Based on credit rating and insolvency data for the near universe of actively rated German firms, our results suggest that the policy reponse to COVID-19 has triggered a backlog of insolvencies in Germany that is particularly pronounced among financially weak, small firms, having potential long term implications on economic recovery.
    Keywords: COVID-19 policy response,Corporate bankruptcy,Cleansing e ect,SMEs
    JEL: C83 G33 H12 O38
    Date: 2021
  22. By: Serge Garcia; Julien Jacob; Eve-Angéline Lambert
    Abstract: We experimentally investigate the relative performance of two liability sharing rules for managing environmental harms that are jointly caused by two firms which can make ex ante safety investments in order to reduce the magnitude of the harm. The investment levels are chosen non-cooperatively and assumed to be non-observable by the regulator. If one firm is unable to cover its part of the damages, third parties might not receive full compensation for their harm. Through an experiment, we analyze the investment choices under two widely used liability sharing rules and compare the decisions to theoretical predictions. In line with theory, we show that insolvency leads to under-investment. Moreover, we show that the relative performance of each rule depends on the firms’ relative degree of solvency. Our results indicate that the legislator should make the default liability sharing rule dependent upon the degree of capitalization of firms.
    Keywords: Environmental Regulation; Liability Sharing Rules; Multiple Tortfeasors; Firms’ Insolvency.
    JEL: K13 K32 Q53
    Date: 2021

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