nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒08‒24
39 papers chosen by
Stan Miles
Thompson Rivers University

  1. Deep xVA solver - A neural network based counterparty credit risk management framework By Alessandro Gnoatto; Athena Picarelli; Christoph Reisinger
  2. Convolution Bounds on Quantile Aggregation By Jose Blanchet; Henry Lam; Yang Liu; Ruodu Wang
  3. Locality in time of the European insurance regulation "risk-neutral" valuation framework, a pre-and post-Covid analysis and further developments By Fabrice Borel-Mathurin; Nicole El Karoui; Stéphane Loisel; Julien Vedani
  4. Compound poisson models for weighted networks with applications in finance By Gandy, Axel; Veraart, Luitgard A. M.
  5. Canada; Financial Sector Assessment Program-Technical Note-Systemic Risk Oversight and Macroprudential Policy By International Monetary Fund
  6. Dynamic Asymmetric Optimal Portfolio Allocation between Energy Stocks and Energy Commodities: Evidence from Clean Energy and Oil and Gas Companies By Mahdi Ghaemi Asl; Giorgio Canarella; Stephen M. Miller
  7. Forecasting macroeconomic risk in real time: Great and Covid-19 Recessions By De Santis, Roberto A.; Van der Veken, Wouter
  8. Austria; Publication of Financial Sector Assessment Program Documentation-Technical Note on Insurance Sector—Regulation, Supervision, Recovery, and Resolution Regime Prospects By International Monetary Fund
  9. Collateral Re-use, Liquidity and Financial Stability By Matteo Accornero
  11. Ordering and Inequalities for Mixtures on Risk Aggregation By Yuyu Chen; Peng Liu; Yang Liu; Ruodu Wang
  12. The Role of Oil and Risk Shocks in the High-Frequency Movements of the Term Structure of Interest Rates of the United States By Rangan Gupta; Syed Jawad Hussain Shahzad; Xin Sheng; Sowmya Subramaniam
  13. HRP performance comparison in portfolio optimization under various codependence and distance metrics By Illya Barziy; Marcin Chlebus
  14. Coverage strategies with derivative financial products: Vueling case By José Ignacio Rubiño Ruiz; Carlos Sanchís Pedregosa
  15. The risk spillover from economic policy uncertainties to the European Union Emission Trading Scheme By Jiqiang Wang; Jianfeng Guo; Peng-Fei Dai; Yinpeng Liu; Ying Fan
  16. The Time-Varying Nature of Risk Aversion: Evidence from 60 Years of U.S. Stock Market Data By Dominique Pépin; Stephen M. Miller
  17. Bankruptcy Risk among Indonesian Stock Exchange Listed Companies By Zuliani Dalimunthe
  18. What is Certain about Uncertainty? By Danilo Cascaldi-Garcia; Deepa Dhume Datta; Thiago Revil T. Ferreira; Olesya V. Grishchenko; Mohammad R. Jahan-Parvar; Juan M. Londono; Francesca Loria; Sai Ma; Marius del Giudice Rodriguez; John H. Rogers; Cisil Sarisoy; Ilknur Zer
  19. Quantitative Easing and Financial Risk Taking: Evidence from Agency Mortgage REITs By W. Scott Frame; Eva Steiner
  20. Tile test for back-testing risk evaluation By Gilles Zumbach
  21. The Bennet Decomposition and Predictability of the U.S. REITs’ Profitability By Zhilan Feng; Stephen M. Miller; Dogan Tirtiroglu
  22. Generalized Autoregressive Score asymmetric Laplace Distribution and Extreme Downward Risk Prediction By Shao-Peng Hong
  23. The EU sustainable finance taxonomy from the perspective of the insurance and reinsurance sector By Marie Scholer; Lazaro Cuesta Barbera
  24. High-Frequency Predictability of Housing Market Movements of the United States: The Role of Economic Sentiment By Mehmet Balcilar; Elie Bouri; Rangan Gupta; Clement Kweku Kyei
  25. A nonparametric eigenvalue-regularized integrated covariance matrix estimator for asset return data By Lam, Clifford; Feng, Phoenix
  26. Real Options: Capital Investment Appraisal; Estimating the Market Price of Risk and Application to the Valuation of a New Business By Rashid, Muhammad Mustafa
  27. On Zipf’s law and the bias of Zipf regressions By Christian Schluter
  28. Optimal defaults with normative ambiguity By Goldin, Jacob; Reck, Daniel
  29. How Did Banks Fund C&I Drawdowns at the Onset of the COVID-19 Crisis? By David P. Glancy; Max Gross; Felicia Ionescu
  30. The role of labor-income risk in household risk-taking? By Hubar, Sylwia; Koulovatianos, Christos; Li, Jian
  31. The rule of conditional probability is valid in quantum theory By Porta Mana, PierGianLuca
  32. Recovery process optimization using survival regression By Jiří Witzany; Anastasiia Kozina
  33. Machine Learning approach for Credit Scoring By A. R. Provenzano; D. Trifir\`o; A. Datteo; L. Giada; N. Jean; A. Riciputi; G. Le Pera; M. Spadaccino; L. Massaron; C. Nordio
  34. Inflation at Risk By López-Salido, J David; Loria, Francesca
  35. The evolution of the banking system and default factors of Russian banks in the period 2013-2018 By Zubarev, Andrey (Зубарев, Андрей); Shilov, Kirill (Шилов, Кирилл); Bekirova, Olga (Бекирова, Ольга)
  36. Republic of Armenia; Technical Assistance Report-Strategic Choices for Tax Administration to Enhance Tax Compliance By International Monetary Fund
  37. Variations associées à la notion de risque By Yvon Pesqueux
  38. Long-term stock returns in Brazil: volatile equity returns for U.S.-like investors By Eurilton Araújo; Ricardo D. Brito; Antônio Z. Sanvicente
  39. Does Better Information Curb Customs Fraud? By Cyril Chalendard; Alice Duhaut; Ana Margarida Fernandes; Aaditya Mattoo; Gael Raballand; Bob Rijkers

  1. By: Alessandro Gnoatto (Department of Economics (University of Verona)); Athena Picarelli (Department of Economics (University of Verona)); Christoph Reisinger (University of Oxford)
    Abstract: In this paper, we present a novel computational framework for portfolio-wide risk management problems where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account.
    Keywords: CVA, DVA, FVA, ColVA, xVA, EPE, Collateral, xVA hedging, Deep BSDE Solver
    JEL: G12 G13 C63
    Date: 2020–05
  2. By: Jose Blanchet; Henry Lam; Yang Liu; Ruodu Wang
    Abstract: Quantile aggregation with dependence uncertainty has a long history in probability theory with wide applications in problems in finance, risk management, statistics, and operations research. Using a recent result on inf-convolution of Range-Value-at-Risk, which includes Value-at-Risk and Expected Shortfall as special cases, we establish new analytical bounds which we call convolution bounds. These bounds are easy to compute, and we show that they are sharp in many relevant cases. We pay a special attention to the problem of quantile aggregation, and the convolution bounds help us to identify approximations for the extremal dependence structure. The convolution bound enjoys several advantages, including interpretability, tractability and theoretical properties. To the best of our knowledge, there is no other theoretical result on quantile aggregation which is not covered by the convolution bounds, and thus the convolution bounds are genuinely the best one available. The results can be applied to compute bounds on the distribution of the sum of random variables. Some applications to operations research are discussed.
    Date: 2020–07
  3. By: Fabrice Borel-Mathurin (ACPR - Autorité de Contrôle Prudentiel et de Résolution - Autorité de Contrôle Prudentiel et de Résolution); Nicole El Karoui (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique, LPSM UMR 8001 - Laboratoire de Probabilités, Statistiques et Modélisations - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique); Stéphane Loisel (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Julien Vedani (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)
    Abstract: The so-called market-consistency of the European life insurance valuation as shaped by regulation guidelines embeds numerous theoretical and practical misstatements. Since El Karoui et al. (2017) the manipulation risk induced by the framework imprecision and, in particular, its high dependency to regulatory and non-regulatory calibration data is clear. In this paper we update some results and analysis of El Karoui et al. (2017) using data from a more recent "classical" year (2017) and from an exceptional year (first quarter of 2020, with Covid-19 effects), and test additional sensitivities. Based on the updated values we obtain up to-45% in the V IF estimates values depending on the swaption implied volatilities matrix used to calibrate the interest rates model. Then trying different calibration sets we obtain up to 105% difference. In parallel, we see that using 3-month averages to calibrate Economic Scenario Generators do not make effects of crises like Covid-19 disappear. We then address the "simulation seed" setting issue, and the interest and limits of keeping the same seed when estimating and comparing economic valuations, be it on horizontal (comparing valuations through time) or vertical (studying sensitivities at the same date) analysis. We finally open our study to propose various tools for a better risk management of economic scenarios and valuation, through a better understanding of Asset-Liability Management models.
    Date: 2020–07–23
  4. By: Gandy, Axel; Veraart, Luitgard A. M.
    Abstract: We develop a modelling framework for estimating and predicting weighted network data. The edge weights in weighted networks often arise from aggregating some individual relationships be- tween the nodes. Motivated by this, we introduce a modelling framework for weighted networks based on the compound Poisson distribution. To allow for heterogeneity between the nodes, we use a regression approach for the model parameters. We test the new modelling framework on two types of financial networks: a network of financial institutions in which the edge weights represent exposures from trading Credit Default Swaps and a network of countries in which the edge weights represent cross-border lending. The compound Poisson Gamma distributions with regression fit the data well in both situations. We illustrate how this modelling framework can be used for predicting unobserved edges and their weights in an only partially observed network. This is for example relevant for assessing systemic risk in financial networks.
    Keywords: eighted directed networks; compound Poisson distribution; regression; subnetwork prediction; financial networks; systemic risk
    JEL: C02 C46 C53 D85 G32
    Date: 2020–05–29
  5. By: International Monetary Fund
    Abstract: This Technical Note provides a summary of the review of systemic risk oversight arrangements and macroprudential policy issues in Canada. The paper discusses the existing systemic risk oversight arrangements and potential challenges, and then presents steps that can be taken to modernize the framework to ensure its effectiveness going forward. The paper focuses on systemic risk surveillance, including the current approaches and existing challenges such as data gaps and coordination. It also covers macroprudential policy issues, including the toolkit, the current policy stance and overall policy effectiveness. The review recommends that steps can be taken to improve the current system with a more formalized arrangement for systemic risk oversight. A single body in charge of systemic risk oversight would be the first-best option. Over time, the authorities should review whether systemic risk oversight under the Heads of Agencies Committee leadership with no statutory mandate is adequate. Macroprudential policy at the federal level has been effective; however, better coordination is essential given multiple provincial authorities’ ownership of prudential tools.
    Date: 2020–01–24
  6. By: Mahdi Ghaemi Asl (Kharazmi University); Giorgio Canarella (University of Nevada, Las Vegas); Stephen M. Miller (University of Nevada, Las Vegas)
    Abstract: This paper investigates returns and volatility transmission between SPGCE (index of clean energy stocks), SPGO (index of oil and gas stocks), two non-renewable energy commodities (natural gas and crude oil), and three products of crude oil distillation (heating oil, gasoline, and propane). We estimate a VAR(1) asymmetric BEKK-MGARCH(1,1) using daily U.S. data from March 1, 2010, to February 25, 2020. The empirical findings reveal a vast heterogeneity in spillover patterns of returns, volatilities, and shocks. We employ the empirical results to derive optimal portfolio weights, hedge ratios, and effectiveness measures for SPGCE and SPGO diversified portfolios. We find dynamic diversification advantages of energy commodities, especially heating oil, for energy-related stock markets. We also find that SPGCE and SPGO stocks possess the highest average optimal weight and hedging effectiveness for each other, implying that the positive performance of SPGCE stocks considerably compensates for the negative performance of SPGO stocks. For investors and regulators, the advancement and implementation of clean energy programs and policies, while reducing environmental debt and enhancing “green” growth and sustainable development, provide instruments and strategies to hedge the equity risks inherent in the oil and gas industry.
    Keywords: Clean energy stocks, Oil and gas stocks, Asymmetric BEKK, Dynamic Optimal Portfolios.
    JEL: Q43 G11 C33
    Date: 2020–08
  7. By: De Santis, Roberto A.; Van der Veken, Wouter
    Abstract: We show that financial variables contribute to the forecast of GDP growth during the Great Recession, providing additional insights on both first and higher moments of the GDP growth distribution. If a recession is due to an unforeseen shock (such as the Covid-19 recession), financial variables serve policymakers in providing timely warnings about the severity of the crisis and the macroeconomic risk involved, because downside risks increase as financial stress and corporate spreads become tighter. We use quantile regression and the skewed t-distribution and evaluate the forecasting properties of models using out-of-sample metrics with real-time vintages. JEL Classification: C53, E23, E27, E32, E44
    Keywords: Covid-19 Recession, downside risks, Great Recession, non-linear models, real-time forecast
    Date: 2020–07
  8. By: International Monetary Fund
    Abstract: This review provides an update on the Austrian insurance sector and an analysis of certain key aspects of the regulatory and supervisory regime. The note analyzes regulation and supervision in relation to key issues identified in previous Financial Sector Assessment Programs (FSAP), as well as material changes since the last FSAP. This note also covers the current situation and potential changes in the crisis management and early intervention framework of the insurance sector. It focuses on issues relevant to a long-standing policyholder protection mechanism, early intervention powers—existing and under discussion—and crisis management and resolution arrangements for insurance companies and groups. The analysis recommends that proper implementation of Solvency II needs ongoing validation and scrutiny by regulators, which could be at risk if supervisory resources with skills and expertise are not retained. Higher legal, reputational, and conduct risks are posing additional pressures to the life insurance sector. Market conduct supervision should be enhanced, with active use of enforcement powers in addition to the insights that studies launched by the government will provide.
    Keywords: Financial regulation and supervision;Financial crises;Financial markets;Financial institutions;Macroprudential policies and financial stability;ISCR,CR,FMA,insurer,solvency,policyholder,insurance sector
    Date: 2020–03–02
  9. By: Matteo Accornero (Department of Social Sciences and Economics, Sapienza University of Rome)
    Abstract: This work provides a model where the repercussions on financial stability of collateral re-use in repo contracts can be analysed and assessed. In the model, the rationale for repo contracts is the arbitrage activity of a leveraged hedge fund, which is profitably financed by a dealer bank. Repo contracts, in connection with collateral re-use, lubricate both the credit and the financial system, increasing the financial operators’ profits and improving equilibrium rates and volumes. At the same time, they amplify the leverage of the whole economy, making it vulnerable to shocks. Introducing a default risk for the hedge fund, the proposed model identifies diverging effects of collateral re-use on financial stability. In states with low dealer bank profitability, the increase in collateral re-use renders a sound dealer bank management style the profit maximising strategy. Where an unsound balance sheet expansion is highly profitable, the increase in collateral re-use provides destabilising incentives to the dealer bank.
    Keywords: repo markets, collateral re-use, rehypothecation, systemic risk
    JEL: E58 G01 G21 G23
    Date: 2020–05
  10. By: Loïc Berger (LEM - Lille économie management - LEM - UMR 9221 - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique, CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux]); Louis Eeckhoudt (LEM - Lille économie management - LEM - UMR 9221 - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)
    Abstract: Diversification is a basic economic principle that helps to hedge against uncertainty. It is therefore intuitive that both risk aversion and ambiguity aversion should positively affect the value of diversification. In this paper, we show that this intuition (1) is true for risk aversion but (2) is not necessarily true for ambiguity aversion. We derive sufficient conditions, showing that, contrary to the economic intuition, ambiguity and ambiguity aversion may actually reduce the diversification value.
    Keywords: Diversification,ambiguity aversion,model uncertainty,hedging
    Date: 2020–08–03
  11. By: Yuyu Chen; Peng Liu; Yang Liu; Ruodu Wang
    Abstract: Aggregation sets, which represent model uncertainty due to unknown dependence, are an important object in the study of robust risk aggregation. In this paper, we investigate ordering relations between two aggregation sets for which the sets of marginals are related by two simple operations: distribution mixtures and quantile mixtures. Intuitively, these operations "homogenize" marginal distributions by making them similar. As a general conclusion from our results, more "homogeneous" marginals lead to a larger aggregation set, and thus more severe model uncertainty, although the situation for quantile mixtures is much more complicated than that for distribution mixtures. We proceed to study inequalities on the worst-case values of risk measures in risk aggregation, which represent conservative calculation of regulatory capital. Among other results, we obtain an order relation on VaR under quantile mixture for marginal distributions with monotone densities. Numerical results are presented to visualize the theoretical results and further inspire some conjectures. Finally, we discuss the connection of our results to joint mixability and to merging p-values in multiple hypothesis testing.
    Date: 2020–07
  12. By: Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Syed Jawad Hussain Shahzad (Montpellier Business School, Montpellier, France; South Ural State University, Chelyabinsk, Russian Federation); Xin Sheng (Lord Ashcroft International Business School, Anglia Ruskin University, Chelmsford, CM1 1SQ, United Kingdom); Sowmya Subramaniam (Indian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, India)
    Abstract: We use daily data for the period 5 January 2000 to 31 October 2018 to analyse the impact of structural oil supply, oil demand and financial market risk shocks on the level, slope and curvature factors derived from the term structure of interest rates of the United States covering maturities of 1 to 30 years. Linear causality tests detect no evidence of predictability of these shocks on the three latent factors. However, statistical tests performed on the linear model provide evidence of nonlinearity and structural breaks, and hence indicate that the Granger causality test results are based on a misspecified framework, and cannot be relied upon. Given this, we use a nonparametric causality in-quantiles test to reconsider the predictive ability of the three shocks on the three latent factors, with this model being robust to misspecification due to nonlinearity and regime change, as it is a data-driven approach. Moreover, this framework allows us to model the entire conditional distribution of the level, slope and curvature factors, and hence can accommodate, via the lower quantiles, the zero lower bound situation seen in our sample period. Using this robust model, we find overwhelming evidence of causality from the two oil shocks and the risk shock for the entire conditional distribution of the three factors, suggesting the predictability of the entire US term structure based on information contained in oil and financial market innovations. Our results have important implications for academics, investors and policymakers.
    Keywords: Yield Curve Factors, Oil Supply and Demand Shocks, Risk Shock, Causality-in-Quantiles Test
    JEL: E43 C22 C32 G12 Q41
    Date: 2020–07
  13. By: Illya Barziy; Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw)
    Abstract: Problem of portfolio optimization was formulated almost 70 years ago in the works of Harry Markowitz. However, the studies of possible optimization methods are still being provided in order to obtain better results of asset allocation using the empirical approximations of codependences between assets. In this work various codependences and metrics are tested in the Hierarchical Risk Parity algorithm to determine whether the results obtained are superior to those of the standard Pearson correlation as a measure of codependence. In order to compare how HRP uses the information from alternative codependence metrics, the MV, IVP, and CLA optimization algorithms were used on the same data. Dataset used for comparison consisted of 32 ETFs representing equity of different regions and sectors as well as bonds and commodities. The time period tested was 01.01.2007-20.12.2019. Results show that alternative codependence metrics show worse results in terms of Sharpe ratios and maximum drawdowns in comparison to the standard Pearson correlation for each optimization method used. The added value of this work is using alternative codependence and distance metrics on real data, and including transaction costs to determine their impact on the result of each algorithm.
    Keywords: Hierarchical Risk Parity, portfolio optimization, ETF, hierarchical structure, clustering, backtesting, distance metrics, risk management, machine learning
    JEL: C32 C38 C44 C51 C52 C61 C65 G11 G15
    Date: 2020
  14. By: José Ignacio Rubiño Ruiz (Universidad de Sevilla); Carlos Sanchís Pedregosa (Universidad de Sevilla)
    Abstract: El presente trabajo trata de dar solución al riesgo de commodities. Veremos cómo, desde una óptica coberturista, pueden utilizarse los productos financieros derivados para evitar y transferir el riesgo. En concreto, profundizaremos en las opciones financieras y llevaremos la teoría a la práctica con datos reales mediante el caso de la empresa Vueling. Descubriremos que la supervivencia de la aerolínea depende en gran medida de la correcta gestión de dicho riesgo, pues su grado de exposición es muy elevado. Para solventar esta situación, propondremos diversas estrategias y analizaremos sus resultados.
    Abstract: The present work tries to solve the commodities risk. We will see how, from a hedging perspective, derivative financial products can be used to avoid and transfer risk. In particular, we will delve into the financial options and we will take the theory into practice with real data through the case of the Vueling company. We will discover that the survival of the airline depends to a large extent on the correct management of said risk, since its degree of exposure is very high. To solve this situation, we will propose various strategies and analyze their results.
    Keywords: Risk,Derivatives,Options,Coverage,Cobertura,Derivados,Opciones,Riesgo
    Date: 2019
  15. By: Jiqiang Wang; Jianfeng Guo; Peng-Fei Dai; Yinpeng Liu; Ying Fan
    Abstract: The European Union Emission Trading Scheme is a carbon emission allowance trading system designed by Europe to achieve emission reduction targets. The amount of carbon emission caused by production activities is closely related to the socio-economic environment. Therefore, from the perspective of economic policy uncertainty, this article constructs the GARCH-MIDAS-EUEPU and GARCH-MIDAS-GEPU models for the impact of European and global economic policy uncertainty on carbon price fluctuations. The results show that both European and global economic policy uncertainty will exacerbate the volatility of carbon price returns, with the latter having a stronger impact. Moreover, the volatility of carbon price returns can be forecasted better with the predictor of global economic policy uncertainty. This research can provide some implications for market managers in grasping market trends and helping participants control the risk of fluctuations in carbon allowances.
    Date: 2020–07
  16. By: Dominique Pépin (University of Poitiers); Stephen M. Miller (University of Nevada, Las Vegas)
    Abstract: We investigate the time variations of the relative risk aversion parameter of a U.S. representative agent using 60 years of stock market data. We develop a methodology to identify the variables that explain the variations of risk aversion, based on an asset pricing model without valuation (or preference) risk. In this framework, the variables that predict the excess return of a market index (but not the second moments) also explain the variations of risk aversion. To wit, the variables include the price-dividend ratio and the short-term interest rate. A shock on the dividend-price ratio exerts a positive, highly persistent, though modest, effect on risk aversion, while a shock on the short-term interest rate exerts a highly negative, less persistent effect. The resulting measure of risk aversion follows a macroeconomically and financially countercyclical pattern.
    Keywords: Time-varying risk aversion, Price-dividend ratio, Short-term interest rate, Return predictors
    JEL: G10 G12 G17
    Date: 2020–08
  17. By: Zuliani Dalimunthe (Universitas Indonesia, Faculty of Economics and Business, 16424, Indonesia Author-2-Name: Anton Setiawan Author-2-Workplace-Name: Universitas Indonesia, Faculty of Economics and Business, 16424, Indonesia Author-3-Name: Eko Rizkianto Author-3-Workplace-Name: Universitas Indonesia, Faculty of Economics and Business, 16424, Indonesia Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)
    Abstract: Objective - This research analyses whether there was a change in bankruptcy risk of companies in Indonesia for the period between 2015–2018, during the first presidency period of Joko Widodo, when Indonesia experienced tremendous dynamic economic, political and technological change. Previous research generally discusses the predictability of bankruptcy models, whereas this study analyses how the dynamics of the bankruptcy risk of companies in Indonesia using the most widely known and recognized models.Methodology/Technique – We employed a Wilcoxon-rank test to evaluate whether there are differences in the bankruptcy risk of companies year to year for the overall company and for each sector. We evaluated 154 companies listed on the Indonesian Stock Exchange.Findings – We found that the number of companies categorized in the bankruptcy zone increased every year and almost doubled during the studied time period (49 companies in 2013 compared to 90 companies in 2018). Novelty – The analysis shows that there is a significant number of companies that experienced bankruptcy risk each year, except for the periods between 2013 to 2014 and 2016 to 2017. On average, when we look at more detail for sectors and each year, the results show statistically significant increasing bankruptcy risk in all sectors except the transportation sector. Type of Paper - Empirical.
    Keywords: Bankruptcy Risk; Corporate Bankruptcy Prediction; Altman Z-score; Wilcoxon Rank-test; Indonesia.
    JEL: G32 G33 E66
    Date: 2019–12–31
  18. By: Danilo Cascaldi-Garcia; Deepa Dhume Datta; Thiago Revil T. Ferreira; Olesya V. Grishchenko; Mohammad R. Jahan-Parvar; Juan M. Londono; Francesca Loria; Sai Ma; Marius del Giudice Rodriguez; John H. Rogers; Cisil Sarisoy; Ilknur Zer
    Abstract: Researchers, policymakers, and market participants have become increasingly focused on the effects of uncertainty and risk on financial market and economic outcomes. This paper provides a comprehensive survey of the many existing measures of risk, uncertainty, and volatility. It summarizes what these measures capture, how they are constructed, and their effects, paying particular attention to large uncertainty spikes, such as those appearing concurrently with the outbreak of COVID-19. The measures are divided into three types: (1) news-based, survey- based, and econometric; (2) asset market based; and (3) Knightian uncertainty. While uncertainty has significant real and financial effects and spills over across countries, the size and persistence of these effects depend crucially on the source of uncertainty.
    Keywords: Global risk; Uncertainty; Volatility; Crises; Economic policy; Monetary policy; Geopolitical risk; Trade policy; Downside risk
    JEL: E60 G10 G15
    Date: 2020–07–16
  19. By: W. Scott Frame; Eva Steiner
    Abstract: An emerging literature documents a link between central bank quantitative easing (QE) and financial institution credit risk-taking. This paper tests the complementary hypothesis that QE may also affect financial risk-taking. We study Agency MREITs – levered shadow banks that invest in guaranteed U.S. Agency mortgage-backed securities (MBS) principally funded with repo debt. We show that Agency MREIT growth is inversely related to the Federal Reserve’s Agency MBS purchases, reflecting investor portfolio rebalancing. We also find that these institutions increased leverage during the later stages of QE, consistent with “reaching for yield” behavior. Agency MREITs seem to concurrently adjust their liquidity and interest rate risk profiles.
    Keywords: Quantitative Easing; Risk Taking; GSEs; Mortgages; Agency MBS
    JEL: E58 G21 G23 G28
    Date: 2020–06–30
  20. By: Gilles Zumbach
    Abstract: A new test for measuring the accuracy of financial market risk estimations is introduced. It is based on the probability integral transform (PIT) of the ex post realized returns using the ex ante probability distributions underlying the risk estimation. If the forecast is correct, the result of the PIT, that we called probtile, should be an iid random variable with a uniform distribution. The new test measures the variance of the number of probtiles in a tiling over the whole sample. Using different tilings allow to check the dynamic and the distributional aspect of risk methodologies. The new test is very powerful, and new benchmarks need to be introduced to take into account subtle mean reversion effects induced by some risk estimations. The test is applied on 2 data sets for risk horizons of 1 and 10 days. The results show unambiguously the importance of capturing correctly the dynamic of the financial market, and exclude some broadly used risk methodologies.
    Date: 2020–07
  21. By: Zhilan Feng (Clarkson University); Stephen M. Miller (University of Nevada, Las Vegas); Dogan Tirtiroglu (Ryerson University)
    Abstract: This paper examines empirically the predictability of operating profitability and whether any observed predictability stems from the asset or debt management policies of a portfolio of REITs. Return on assets (ROA), return on equity (ROE), Change in ROA and Change in ROE are the profitability measures of the sample portfolio, which covers, on average, about 84% of U.S. REITs included in the FTSE NAREIT All Equity Index between 1989 and 2015. While the asset management policies of sample REITs engenders ROA and Change in ROA, their asset and debt management policies jointly engender the ROE and Change in ROE. Our empirical work focuses on the coefficient estimates of (i) the own lags of each of these four profitability measures, and (ii) the lags of the “between,” “within,” “entry,” and “exit” effects, obtained from the first-ever application of the Bennet (1920) dynamic decomposition to the temporal changes - between (t) and (t-1) - in the ROA and ROE of the sample portfolio. A comparison of the estimates -- between the ROA and ROE as well as between the Change in ROA and Change in ROE estimations -- in (i) and (ii) provides evidence about the root of the predictability. Our work repeats all the estimations above under the funds from operations (FFO) and net income (NI) metrics, which are used in computing the ROA and ROE measures and also their temporal changes. A comparison of the FFO- and NI-based results at the portfolio level is important since there is a growing literature and debate on whether the information content of FFO differs incrementally from that of NI. We find that (i) the predictability of profitability of the sample portfolio of REITs is highly visible and statistically strong; (ii) the estimates of the first own lags of the dependent variables or the first and second lags of some of the Bennet (1920) dynamic decomposition effects - especially the “within” effect - provide strong evidence of predictability; and (iii) the use of FFO unearths evidence that the sample REITs’ asset management policies, as embodied in ROA and Change in ROA, have more to do with predictability than a combination of their asset and debt management policies, as embodied in ROE and Change in ROE, does. These findings should be useful to the investors and REIT managers and the REIT literature.
    Keywords: predictability, operating profits, ROA and ROA, dynamic decomposition, FFO and NI, asset management, debt management
    JEL: L1 G2
    Date: 2020–08
  22. By: Shao-Peng Hong
    Abstract: Due to the skessed distribution, high peak and thick tail and asymmetry of financial return data, it is difficult to describe the traditional distribution. In recent years, generalized autoregressive score (GAS) has been used in many fields and achieved good results. In this paper, under the framework of generalized autoregressive score (GAS), the asymmetric Laplace distribution (ALD) is improved, and the GAS-ALD model is proposed, which has the characteristics of time-varying parameters, can describe the peak thick tail, biased and asymmetric distribution. The model is used to study the Shanghai index, Shenzhen index and SME board index. It is found that: 1) the distribution parameters and moments of the three indexes have obvious time-varying characteristics and aggregation characteristics. 2) Compared with the commonly used models for calculating VaR and ES, the GAS-ALD model has a high prediction effect.
    Date: 2020–08
  23. By: Marie Scholer (EIOPA); Lazaro Cuesta Barbera (EIOPA)
    Abstract: This article investigates how much investment held by insurers may be eligible to the EU sustainable finance taxonomy. To this aim, Solvency II item-by-item investment data is employed. As part of the Green Deal, the Commission presented the European Green Deal Investment Plan, which will mobilize at least €1 trillion of sustainable investments over the next decade. Our results suggest that currently only a small portion of the insurer’s investments are made in economic activities which might be eligible to the EU sustainable finance taxonomy as the insurer’s exposures are mainly concentrating toward financial activities. On one hand, this can be interpreted as an indicator of limited exposure to transition risk for the insurance sector but on the other hand also indicates that insurers have the possibility to contribute more significantly to transitioning to a lower carbon society in the future. As major long-term investors, insurers could play a key role in the transition towards more sustainable society. In this respect, the taxonomy can help insurers by providing clarity in identifying sustainable economic activities and avoiding reputational risks.
    Keywords: insurance, sutainable finance, green taxonomy
    JEL: G11 G12 G22
    Date: 2020–07
  24. By: Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Famagusta, via Mersin 10, Northern Cyprus, Turkey); Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Clement Kweku Kyei (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)
    Abstract: We analyse the ability of a newspaper-based economic sentiment index of the United States to predict housing market movements using daily data over the period 2nd August, 2007 to 19th June, 2020. For this purpose, we use a k-th order nonparametric causality-in-quantiles test, which allows us to test for predictability over the entire conditional distribution of not only housing returns, but also volatility, by controlling for misspecification due to nonlinearity and structural breaks – both of which we show to exist between housing returns and the economic sentiment index. Our results show that economic sentiment does indeed predict housing returns (unlike the conditional mean-based, i.e., linear, Granger causality test and volatility), barring the extreme upper ends of the respective conditional distributions. Our results have important implications for academics, policymakers, and investors.
    Keywords: Economic Sentiment; Housing Returns and Volatility; Higher-Order Nonparametric Causality in Quantiles Test
    JEL: C22 C32 R30
    Date: 2020–07
  25. By: Lam, Clifford; Feng, Phoenix
    Abstract: In high-frequency data analysis, the extreme eigenvalues of a realized covariance matrix are biased when its dimension p is large relative to the sample size n. Furthermore, with non-synchronous trading and contamination of microstructure noise, we propose a nonparametrically eigenvalue-regularized integrated covariance matrix estimator (NERIVE) which does not assume specific structures for the underlying integrated covariance matrix. We show that NERIVE is positive definite in probability, with extreme eigenvalues shrunk nonlinearly under the high dimensional framework p=n ! c > 0. We also prove that in portfolio allocation, the minimum variance optimal weight vector constructed using NERIVE has maximum exposure and actual risk upper bounds of order p. Incidentally, the same maximum exposure bound is also satisfied by the theoretical minimum variance portfolio weights. All these results hold true also under a jump-diffusion model for the log-price processes with jumps removed using the wavelet method proposed in Fan and Wang (2007). They are further extended to accommodate the existence of pervasive factors such as a market factor under the setting p3=2=n ! c > 0. The practical performance of NERIVE is illustrated by comparing to the usual two-scale realized covariance matrix as well as some other nonparametric alternatives using different simulation settings and a real data set.
    Keywords: High frequency data; Microstructure noise; Non-synchronous trading; Integrated covariance matrix; Minimum variance portfolio; Nonlinear shrinkage
    JEL: C13 C14 C5
    Date: 2018–09–01
  26. By: Rashid, Muhammad Mustafa
    Abstract: The risk-neutral valuation approach to evaluating an investment avoids the need to estimate risk-adjusted discount rates, but it does require the market price of risk parameters for all stochastic variables. When historical data is available on a particular variable, its market price of risk can be estimated using the capital asset pricing model.
    Keywords: Real Options, Capital Investment Appraisal, Market Price of Risk, New Business Valuation, Internet Companies, Amazon
    JEL: G3 G31 G32 M10 N0 N8 N82
    Date: 2020–03–19
  27. By: Christian Schluter (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université)
    Abstract: City size distributions are not strictly Pareto, but upper tails are rather Pareto like (i.e. tails are regularly varying). We examine the properties of the tail exponent estimator obtained from ordinary least squares (OLS) rank size regressions (Zipf regressions for short), the most popular empirical strategy among urban economists. The estimator is then biased towards Zipf's law in the leading class of distributions. The Pareto quantile-quantile plot is shown to offer a simple diagnostic device to detect such distortions and should be used in conjunction with the regression residuals to select the anchor point of the OLS regression in a data-dependent manner. Applying these updated methods to some well-known data sets for the largest cities, Zipf's law is now rejected in several cases.
    Keywords: regular variation,city size distributions,Zipf's law,rank size regression,extreme value index,heavy tails
    Date: 2020
  28. By: Goldin, Jacob; Reck, Daniel
    Abstract: Default effects are pervasive, but the reason they arise is often unclear. We study optimal policy when the planner does not know whether an observed default effect reflects a welfare-relevant preference or a mistake. Within a broad class of models, we find that determining optimal policy is impossible without resolving this ambiguity. Depending on the resolution, optimal policy tends in opposite directions: either minimizing the number of non-default choices or inducing active choice. We show how these considerations depend on whether active choosers make mistakes when selecting among non-default options. We illustrate our results using data on pension contribution defaults.
    Keywords: forthcoming
    JEL: J1
    Date: 2020–06–19
  29. By: David P. Glancy; Max Gross; Felicia Ionescu
    Abstract: Banks experienced significant balance sheet expansions in March 2020 due to unprecedented increases in commercial and industrial (C&I) loans and deposit funding. According to the Federal Reserve's H.8 data, "Assets and Liabilities of Commercial Banks in the U.S.", C&I loans increased by nearly $480 billion in March—the largest monthly increase in the history of this series, surpassing the nearly $90 billion increase in C&I loans in the six weeks following Lehman Brothers' collapse in 2008.
    Date: 2020–07–31
  30. By: Hubar, Sylwia; Koulovatianos, Christos; Li, Jian
    Abstract: In fifteen European countries, China, and the US, stocks and business equity as a share of total household assets are represented by an increasing and convex function of income/wealth. A parsimonious model fitted to the data shows why background labor-income risk can explain much of this risk-taking pattern. Uncontrollable labor-income risk stresses middle-income households more because labor income is a larger fraction of their total lifetime resources compared with the rich. In response, middle-income households reduce (controllable) financial risk. Richer households, having less pressure, can afford more risk-taking. The poor take low risk because they avoid jeopardizing their subsistence consumption.
    Keywords: background risk,household-portfolio shares,business equity,subsistence consumption,wealth inequality
    JEL: G11 D91 D81 D14 D11 E21
    Date: 2020
  31. By: Porta Mana, PierGianLuca (Norwegian University of Science and Technology)
    Abstract: In a recent manuscript, Gelman & Yao (2020) claim that "the usual rules of conditional probability fail in the quantum realm" and that "probability theory isn't true (quantum physics)" and purport to support these statements with the example of a quantum double-slit experiment. The present note recalls some relevant literature in quantum theory and shows that (i) Gelman & Yao's statements are false; in fact, the quantum example confirms the rules of probability theory; (ii) the particular inequality found in the quantum example can be shown to appear also in very non-quantum examples, such as drawing from an urn; thus there is nothing peculiar to quantum theory in this matter. A couple of wrong or imprecise statements about quantum theory in the cited manuscript are also corrected.
    Date: 2020–07–12
  32. By: Jiří Witzany; Anastasiia Kozina
    Abstract: The goal of this paper is to propose, empirically test and compare different logistic and survival analysis techniques in order to optimize the debt collection process. This process uses various actions, such as phone calls, mails, visits, or legal steps to recover past due loans. We focus on the soft collection part, where the question is whether and when to call a past-due debtor with regard to the expected financial return of such an action. We propose using the survival analysis technique, in which the phone call can be compared to a medical treatment, and repayment to the recovery of a patient. We show on a real banking dataset that, unlike ordinary logistic regression, this model provides the expected results and can be efficiently used to optimize the soft collection process.
    Date: 2020–07–16
  33. By: A. R. Provenzano; D. Trifir\`o; A. Datteo; L. Giada; N. Jean; A. Riciputi; G. Le Pera; M. Spadaccino; L. Massaron; C. Nordio
    Abstract: In this work we build a stack of machine learning models aimed at composing a state-of-the-art credit rating and default prediction system, obtaining excellent out-of-sample performances. Our approach is an excursion through the most recent ML / AI concepts, starting from natural language processes (NLP) applied to economic sectors' (textual) descriptions using embedding and autoencoders (AE), going through the classification of defaultable firms on the base of a wide range of economic features using gradient boosting machines (GBM) and calibrating their probabilities paying due attention to the treatment of unbalanced samples. Finally we assign credit ratings through genetic algorithms (differential evolution, DE). Model interpretability is achieved by implementing recent techniques such as SHAP and LIME, which explain predictions locally in features' space.
    Date: 2020–07
  34. By: López-Salido, J David; Loria, Francesca
    Abstract: We find that the recent muted response of the conditional mean of inflation to economic conditions does not convey an adequate representation of the overall pattern of inflation dynamics. Analyzing data from the 1970s reveals ample variability in the entire conditional distribution of inflation. Focusing on the period from 2000 onward bolsters this evidence. Using time-series data for the United States and the Euro Area, we document that looking at the entire conditional distribution of inflation uncovers - after controlling for the state of the labor market and inflation expectations - that heightened financial conditions carry substantial and persistent low-inflation risks, a feature overlooked by much of the literature. Our paper offers a new empirical perspective to existing macroeconomic models, showing that changes in credit conditions are also key to understand the dynamics of the inflation tails.
    Keywords: Inflation risk; Quantile regression
    JEL: C21 E31
    Date: 2019–10
  35. By: Zubarev, Andrey (Зубарев, Андрей) (The Russian Presidential Academy of National Economy and Public Administration); Shilov, Kirill (Шилов, Кирилл) (The Russian Presidential Academy of National Economy and Public Administration); Bekirova, Olga (Бекирова, Ольга) (The Russian Presidential Academy of National Economy and Public Administration)
    Abstract: This study analyzes evolution of the Russian banking system in the period 2013–2018 using monthly balance sheet data. We provide an econometric analysis of main factors that affected banks’ sustainability during that period. We also distinguish factors of bank default in the period from 2013 to 2017, including the crisis of 2014-2015, and afterwards.
    Date: 2020–04
  36. By: International Monetary Fund
    Abstract: This technical assistance report on Republic of Armenia advices on advises on strategic choices for tax administration and compliance risk management. It complements the March 2018 tax administration mission, which provided the State Revenue Committee (SRC) with general guidance to develop and implement a compliance improvement framework. Armenia’s tax policy setting creates challenges for the SRC to effectively manage tax compliance. The Government’s tax policy framework is likely to create new noncompliance opportunities and result in revenue leakages. Strengthened fundamental functions and processes are needed for the delivery of effective tax administration. Two issues raised in the 2018 tax administration mission report need to be highlighted again. The mission provided an analysis of SRC case selection and advised on the adoption of analytical tools to achieve better results. The SRC’s current additive risk rule scoring approaches need to be supplemented by predictive modeling giving better predictions and prioritization of the likelihood and potential consequences of noncompliance—the use of such model is envisaged in the SRC’s draft strategic plan.
    Keywords: Revenue administration;Tax revenue;Tax policy;Tax administration;Tax reforms;ISCR,CR,SRC,taxpayer,tax regime,compliance,tax obligation
    Date: 2020–02–14
  37. By: Yvon Pesqueux (EESD - Equipe en émergence sécurité défense - CNAM - Conservatoire National des Arts et Métiers [CNAM])
    Abstract: Ce texte est organisé de la manière suivante. Après une introduction qui positionne les termes qui seront ensuite abordés sous le prisme du risque, il va dérouler dans l'ordre alphabétique un ensemble de termes associés : Accident, Anéantissement (destruction, effondrement, néant), Aléa, Angoisse et anxiété, Assistance, Assurance - « confiance – défiance », Attention, Atténuation, Audace, Aventure, Biais, Calamité, Catastrophe, Certitude (et incertitude), Certification, Chaos, Choc, Conformité & conformisme, déviance, transgression, triche, fraude, Confort, Contrainte, Contrôle, Crainte, Crise, Criticité (critique), Danger, Déboire, Défaillance, Dégât, Dégradation, Désastre, Destin (destinée), Destruction (cf. son opposé, la construction), Détection (détectabilité), Disponibilité, Dommage (cf. endommagé comme résultat du dommage), Doute & soupçon & garantie & redoutable, Effondrement, Elimination, Enjeu, Erreur, Espoir et espérance, Evaluation, Fatalité, Faute, Fléau, Fortune (« bonne » ou « mauvaise »), Garantie, Gravité, Hasard, Homologation, Ignorance, Incident, Innocuité, Intégrité, Impondérable, Imprévu, Maîtrise, Malaise, Malchance (cf. son opposé, la chance), Malheur (cf. son opposé, le bonheur), Mauvais (cf. son opposé, bon), Menace, Occurrence, Panique, Pari, Péril, Peur, Précipitation, Préjudice, Préparation, Prévention, Prévision (prospective), Prise (reprise), Procédure, Protection, Prudence, Qualification, Résilience (et résistance), Réparation, Responsabilité, Réversibilité / irréversibilité, Revue, Rupture (disruption), Scénario catastrophe, Sévérité, Sinistre, Solidarité & charité, Sollicitude & soutien, Sort, Superstition, Stress, Surprise, Sûreté, Terreur & terrorisme, Tolérance, Tort, Tranquillité, Traumatisme, Urgence, Validation, Vandalisme, Vérification, Victime, VUCA (volatility, uncertainty, complexity, ambiguity), Vulnérabilité.
    Keywords: risque
    Date: 2020–07–30
  38. By: Eurilton Araújo; Ricardo D. Brito; Antônio 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.
    Date: 2020–07
  39. By: Cyril Chalendard; Alice Duhaut; Ana Margarida Fernandes; Aaditya Mattoo; Gael Raballand; Bob Rijkers
    Abstract: This paper examines how providing better information to customs inspectors and monitoring their actions affects tax revenue and fraud detection in Madagascar. First, an instrumental variables strategy is used to show that transaction-specific, third-party valuation advice on a subset of high-risk import declarations increases fraud findings by 21.7 percentage points and tax collection by 5.2 percentage points. Second, a randomized control trial is conducted in which a subset of high-risk declarations is selected to receive detailed risk comments and another subset is explicitly tagged for ex-post monitoring. For declarations not subject to third-party valuation advice, detailed comments increase reporting of fraud by 3.1 percentage points and improve tax yield by 1 percentage point. However, valuation advice and detailed comments have a significantly smaller impact on revenue when potential tax losses and opportunities for graft are large. Monitoring induces inspectors to scan more shipments but does not result in the detection of more fraud or the collection of additional revenue. Better information thus helps curb customs fraud, but its effectiveness appears compromised by corruption.
    Keywords: tariff evasion, tax enforcement, third-party information, performance monitoring, risk management, information provision, randomized control trial
    JEL: D73 F14 H26 K42
    Date: 2020

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