nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒04‒26
twenty-one papers chosen by
Stan Miles
Thompson Rivers University

  1. Accuracies of some Learning or Scoring Models for Credit Risk Measurement By Salomey Osei; Berthine Nyunga Mpinda; Jules Sadefo Kamdem; Jeremiah Fadugba
  2. Tackling the Volatility Paradox: Spillover Persistence and Systemic Risk By Christian Kubitza
  3. Predictability of Tail Risks of Canada and the U.S. Over a Century: The Role of Spillovers and Oil Tail Risks By Afees A. Salisu; Rangan Gupta; Christian Pierdzioch
  4. Ordered Risk Aggregation under Dependence Uncertainty By Yuyu Chen; Liyuan Lin; Ruodu Wang
  5. Loan syndication under Basel II: How do firm credit ratings affect the cost of credit? By Hasan, Iftekhar; Kim, Suk-Joong; Politsidis, Panagiotis; Wu, Eliza
  6. Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability By Tobias Fissler; Yannick Hoga
  7. Semiparametric GARCH models with long memory applied to Value at Risk and Expected Shortfall By Sebastian Letmathe; Yuanhua Feng; André Uhde
  8. Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting By Eric Benhamou; David Saltiel; Serge Tabachnik; Sui Kai Wong; Fran\c{c}ois Chareyron
  9. Accuracies of Model Risks in Finance using Machine Learning By Berthine Nyunga Mpinda; Jules Sadefo Kamdem; Salomey Osei; Jeremiah Fadugba
  10. A Black-Scholes user's guide to the Bachelier model By Jaehyuk Choi; Minsuk Kwak; Chyng Wen Tee; Yumeng Wang
  11. GARCH-UGH: A bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series By Hibiki Kaibuchi; Yoshinori Kawasaki; Gilles Stupfler
  12. Ordering results between the largest claims arising from two general heterogeneous portfolios By Sangita Das; Suchandan Kayal
  13. How Serious is the Measurement-Error Problem in a Popular Risk-Aversion Task? By Fabien, Perez; Guillaume, Hollard; Radu, Vranceanu; Delphine, Dubart
  14. Government Interventions and Sovereign Bond Market Volatility during COVID 19 By Claudiu Albulescu; Eugenia Grecu; Adam Zaremba; David Aharon
  15. Optimal Deposit Insurance By Eduardo Dávila; Itay Goldstein
  16. What is the expected return on a stock? By Martin, Ian; Wagner, Christian
  17. Avoiding the Main Risks in the Recovery Plans of Member States By Núñez Ferrer, Jorge
  18. Bank Credit Risk Events and Peers’ Equity Value By Ana-Maria Fuertes; Maria-Dolores Robles
  19. Deadly Debt Crises: COVID-19 in Emerging Markets By Cristina Arellano; Yan Bai; Gabriel Mihalache
  20. Revisiting Banking Stability Using a New Panel Cointegration Test By Ghassan, Hassan; Boulanouar, Zakaria; Hassan, Kabir Mohammed
  21. Power-law Portfolios By Jan Rosenzweig

  1. By: Salomey Osei (AMMI - African Masters of Machine Intelligence); Berthine Nyunga Mpinda (AMMI - African Masters of Machine Intelligence); Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Jeremiah Fadugba (AMMI - African Masters of Machine Intelligence)
    Abstract: Given the role played by banks in the financial system as well, risks are subject to regulatory attention, and Credit risk is one of the major financial risks faced by banks. According to Basel I to III, banks have the responsibility to implement the credit risk strategy. Nowadays, machine learning techniques have attracted an important interest for different applications to financial institutions and its applications have received much attention from investors and researchers. Hence in this paper, we discuss existing literature by shedding more light on a number of techniques and examine machine learning models for Credit risk by focusing on Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN) for credit risk. Different test performances of these models such as back-testing and stress-testing have been done using Home Credit historical data and simulated data respectively. We realized that the MLP and CNN models were able to predict well with an accuracy of 91% and 67% respectively for back-testing. To test our models in stress scenarios and extreme scenarios, we consider a generated imbalanced data with 80% of defaults and 20% of non-default. Using the same model trained on Home Credit data, we perform a stress-test on the simulated data and we realized that the MLP model did not perform well compared to the CNN model, with an accuracy of 43% as against 89% obtained during the training. Thus, the CNN model was able to perform better during stressed situations for accuracy and for other metrics such as ROC AUC curve, recall, and precision.
    Keywords: Model Accuracy,Machine Learning,Credit Risk,Basel III,Risk Management
    Date: 2021–03
  2. By: Christian Kubitza (University of Bonn)
    Abstract: This paper proposes Spillover Persistence as a measure for financial fragility. The volatility paradox predicts that fragility builds up when volatility is low, which challenges existing measures. Spillover Persistence tackles this challenge by exploring a novel dimension of systemic risk: loss dynamics. I document that Spillover Persistence declines when fragility builds up, during the run-up phase of crises and asset price bubbles, and increases when systemic risk materializes. Variation in financial constraints connects Spillover Persistence to fragility. The results are consistent with the volatility paradox in recent macro-finance models, and highlight the usefulness of loss dynamics to disentangle fragility from amplification effects.
    Keywords: Systemic Risk, Fragility, Financial Crises, Asset Price Bubbles, Fire Sales
    JEL: E44 G01 G12 G20 G32
    Date: 2021–04
  3. 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); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: Motivated by the long standing strong economic ties between Canada and the United States (U.S.), we examine whether such relations can be extended to their stock-market tail risks using over a century of monthly data, while also accounting for the role of tail risks of other advanced economies such as France, Germany, Japan, Italy, Switzerland, and the United Kingdom (U.K.) as well as the role of oil-market tail risk. We employ the Conditional Autoregressive Value at Risk (CAViaR) model developed by Engle and Manganelli (2004) to measure tail risks, where we estimate four variants (Adaptive, Symmetric absolute value, Asymmetric slope and Indirect GARCH) of the CAViaR model to compute the 5% Value-at-Risk (VaR). We then use model diagnostics such as the Dynamic Quantile test (DQ) test, %Hits and Regression Quantile (RQ) statistic to determine the model that best fits the data. Relying on the ``best" tail-risk model and a predictive model that additionally accounts for the salient features of the tail-risk data, we find a strong positive relation between the stock-market tail risks of Canada and the U.S., consistent with risk spillovers between the two economies. Our findings hold for various out-of-sample forecast horizons. We also find contrasting evidence for the oil-market tail risk, whose effect is positive for Canada (being a net oil exporter) and negative for the U.S. (being a net oil importer). Further results obtained after accounting for the role of tail risks of other advanced economies combined using a principal-component analysis reveal a positive relation with the U.S. and negative one for Canada, supporting the diversification potential of the latter in the presence of tail risks of advanced economies other than the U.S. Our findings have implications for investors and policymakers, and are robust to alternative VaR measures.
    Keywords: Tail Risks, Equity and Oil Markets, Spillovers, Predictability
    JEL: C22 C32 C53 G15 Q02
    Date: 2021–04
  4. By: Yuyu Chen; Liyuan Lin; Ruodu Wang
    Abstract: We study the aggregation of two risks when the marginal distributions are known and the dependence structure is unknown, under the additional constraint that one risk is no larger than the other. Risk aggregation problems with the order constraint are closely related to the recently introduced notion of the directional lower (DL) coupling. The largest aggregate risk in concave order (thus, the smallest aggregate risk in convex order) is attained by the DL coupling. These results are further generalized to calculate the best-case and worst-case values of tail risk measures. In particular, we obtain analytical formulas for bounds on Value-at-Risk. Our numerical results suggest that the new bounds on risk measures with the extra order constraint can greatly improve those with full dependence uncertainty.
    Date: 2021–04
  5. By: Hasan, Iftekhar; Kim, Suk-Joong; Politsidis, Panagiotis; Wu, Eliza
    Abstract: This paper investigates how lenders react to borrowers’ rating changes under heterogeneous conditions and different regulatory regimes. Our findings suggest that corporate downgrades that increase capital requirements for lending banks under the Basel II framework are associated with increased loan spreads and deteriorating non-price loan terms relative to downgrades that do not affect capital requirements. Ratings exert an asymmetric impact on loan spreads, as these remain unresponsive to rating upgrades, even when the latter are associated with a reduction in risk weights for corporate loans. The increase in firm borrowing costs is mitigated in the presence of previous bank-firm lending relationships and for borrowers with relatively strong performance, high cash flows and low leverage.
    Keywords: corporate credit ratings, cost of credit, rating-contingent regulation, capital requirements, Basel II
    JEL: G21 G24 G28 G32
    Date: 2021–03
  6. By: Tobias Fissler; Yannick Hoga
    Abstract: Backtesting risk measure forecasts requires identifiability (for model calibration and validation) and elicitability (for model comparison). We show that the three widely-used systemic risk measures conditional value-at-risk (CoVaR), conditional expected shortfall (CoES) and marginal expected shortfall (MES), which measure the risk of a position $Y$ given that a reference position $X$ is in distress, fail to be identifiable and elicitable on their own. As a remedy, we establish the joint identifiability of CoVaR, MES and (CoVaR, CoES) together with the value-at-risk (VaR) of the reference position $X$. While this resembles the situation of the classical risk measures expected shortfall (ES) and VaR concerning identifiability, a joint elicitability result fails. Therefore, we introduce a completely novel notion of multivariate scoring functions equipped with some order, which are therefore called multi-objective scores. We introduce and investigate corresponding notions of multi-objective elicitability, which may prove beneficial in various applications beyond finance. In particular, we prove that conditional elicitability of two functionals implies joint multi-objective elicitability with respect to the lexicographic order on $\mathbb{R}^2$, which makes it applicable in the context of CoVaR, MES or (CoVaR, CoES), together with VaR. We describe corresponding comparative backtests of Diebold-Mariano type, for two-sided and 'one and a half'-sided hypotheses, which respect the particularities of the lexicographic order and which can be used in a regulatory setting. We demonstrate the viability of these backtesting approaches in simulations and in an empirical application to DAX 30 and S&P 500 returns.
    Date: 2021–04
  7. By: Sebastian Letmathe (Paderborn University); Yuanhua Feng (Paderborn University); André Uhde (Paderborn University)
    Abstract: In this paper new semiparametric GARCH models with long memory are in- troduced. The estimation of the nonparametric scale function is carried out by an adapted version of the SEMIFAR algorithm (Beran et al., 2002). Recurring on the revised recommendations by the Basel Committee to measure market risk in the banks' trading books (Basel Committee on Banking Supervision, 2013), the semi- parametric GARCH models are applied to obtain rolling one-step ahead forecasts for the Value at Risk (VaR) and Expected Shortfall (ES) for market risk assets. In addition, standard regulatory traffic light tests (Basel Committee on Banking Supervision, 1996) and a newly introduced traffic light test for the ES are carried out for all models. The practical relevance of our proposal is demonstrated by a comparative study. Our results indicate that semiparametric long memory GARCH models are an attractive alternative to their conventional, parametric counterparts.
    Keywords: Semiparametric, long memory, GARCH models, forecasting, Value at Risk, Expected Shortfall, traffic light test, Basel Committee on Banking Supervision
    JEL: C14 C51 C52 G17 G32
    Date: 2021–04
  8. By: Eric Benhamou; David Saltiel; Serge Tabachnik; Sui Kai Wong; Fran\c{c}ois Chareyron
    Abstract: Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial markets. In contrast, model-based RL is able to capture some fundamental and dynamical concepts of the environment but suffer from cognitive bias. In this work, we propose to combine the best of the two techniques by selecting various model-based approaches thanks to Model-Free Deep Reinforcement Learning. Using not only past performance and volatility, we include additional contextual information such as macro and risk appetite signals to account for implicit regime changes. We also adapt traditional RL methods to real-life situations by considering only past data for the training sets. Hence, we cannot use future information in our training data set as implied by K-fold cross validation. Building on traditional statistical methods, we use the traditional "walk-forward analysis", which is defined by successive training and testing based on expanding periods, to assert the robustness of the resulting agent. Finally, we present the concept of statistical difference's significance based on a two-tailed T-test, to highlight the ways in which our models differ from more traditional ones. Our experimental results show that our approach outperforms traditional financial baseline portfolio models such as the Markowitz model in almost all evaluation metrics commonly used in financial mathematics, namely net performance, Sharpe and Sortino ratios, maximum drawdown, maximum drawdown over volatility.
    Date: 2021–04
  9. By: Berthine Nyunga Mpinda; Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Salomey Osei; Jeremiah Fadugba
    Abstract: There is increasing interest in using Artificial Intelligence (AI) and machine learning techniques to enhance risk management from credit risk to operational risk. Moreover, recent applications of machine learning models in risk management have proved efficient. That notwithstanding, while using machine learning techniques can have considerable benefits, they also can introduce risk of their own, when the models are wrong. Therefore, machine learning models must be tested and validated before they can be used. The aim of this work is to explore some existing machine learning models for operational risk, by comparing their accuracies. Because a model should add value and reduce risk, particular attention is paid on how to evaluate it's performance, robustness and limitations. After using the existing machine learning and deep learning methods for operational risk, particularly on risk of fraud, we compared accuracies of these models based on the following metrics: accuracy, F1-Score, AUROC curve and precision. We equally used quantitative validation such as Back-testing and Stress-testing for performance analysis of the model on historical data, and the sensibility of the model for extreme but plausible scenarios like the Covid-19 period. Our results show that, Logistic regression out performs all deep learning models considered for fraud detection
    Keywords: Machine Learning,Model Risk,Credit Card Fraud,Decisions Support,Stress-Testing
    Date: 2021–04–07
  10. By: Jaehyuk Choi; Minsuk Kwak; Chyng Wen Tee; Yumeng Wang
    Abstract: To cope with the negative oil futures price caused by the COVID-19 recession, global commodity futures exchanges switched the option model from Black-Scholes to Bachelier in April 2020. This study reviews the literature on Bachelier's pioneering option pricing model and summarizes the practical results on volatility conversion, risk management, stochastic volatility, and barrier options pricing to facilitate the model transition. In particular, using the displaced Black-Scholes model as a model family with the Black-Scholes and Bachelier models as special cases, we not only connect the two models but also present a continuous spectrum of model choices.
    Date: 2021–04
  11. By: Hibiki Kaibuchi; Yoshinori Kawasaki; Gilles Stupfler
    Abstract: The Value-at-Risk (VaR) is a widely used instrument in financial risk management. The question of estimating the VaR of loss return distributions at extreme levels is an important question in financial applications, both from operational and regulatory perspectives; in particular, the dynamic estimation of extreme VaR given the recent past has received substantial attention. We propose here a two-step bias-reduced estimation methodology called GARCH-UGH (Unbiased Gomes-de Haan), whereby financial returns are first filtered using an AR-GARCH model, and then a bias-reduced estimator of extreme quantiles is applied to the standardized residuals to estimate one-step ahead dynamic extreme VaR. Our results indicate that the GARCH-UGH estimates are more accurate than those obtained by combining conventional AR-GARCH filtering and extreme value estimates from the perspective of in-sample and out-of-sample backtestings of historical daily returns on several financial time series.
    Date: 2021–04
  12. By: Sangita Das; Suchandan Kayal
    Abstract: This work is entirely devoted to compare the largest claims from two heterogeneous portfolios. It is assumed that the claim amounts in an insurance portfolio are nonnegative absolutely continuous random variables and belong to a general family of distributions. The largest claims have been compared based on various stochastic orderings. The established sufficient conditions are associated with the matrices and vectors of model parameters. Applications of the results are provided for the purpose of illustration.
    Date: 2021–04
  13. By: Fabien, Perez (ENSAE); Guillaume, Hollard (Ecole Polytechnique); Radu, Vranceanu (ESSEC Research Center, ESSEC Business School); Delphine, Dubart (ESSEC Research Center, ESSEC Business School)
    Abstract: This paper uses the test/retest data from the Holt and Laury (2002) experiment to provide estimates of the measurement error in this popular risk-aversion task. Maximum likelihood estimation suggests that the variance of the measurement error is approximately equal to the variance of the number of safe choices. Simulations confirm that the coefficient on the risk measure in univariate OLS regressions is approximately half of its true value. Unlike measurement error, the discrete transformation of continuous riskaversion is not a major issue. We discuss the merits of a number of different solutions: increasing the number of observations, IV and the ORIV method developed by Gillen et al. (2019).
    Keywords: ORIV; Experiments; Measurement error; Risk-aversion; Test/retest
    JEL: C18 C26 C91 D81
    Date: 2019–09
  14. By: Claudiu Albulescu (CRIEF - Centre de Recherche sur l'Intégration Economique et Financière - Université de Poitiers); Eugenia Grecu (UPT - Politehnica University of Timisoara); Adam Zaremba (Montpellier Business School - Montpellier Business School); David Aharon
    Abstract: We test the interaction between COVID-19 governments' interventions, COVID-19-induced uncertainty, and the volatility of sovereign bonds. Using a panel-quantile approach and a comprehensive dataset of 31 countries worldwide, we document that containment and closure policies tend to amplify volatility. Furthermore, the price variability is augmented by the spread of the pandemic itself. On the contrary, economic support policies have a substantial stabilizing effect on bond price fluctuations. Both phenomena are not subsumed by additional control variables and are robust to multiple considerations. Our findings may serve financial market participants in their risk management decisions, as well as policy makers to better shape their preparedness for future pandemics.
    Keywords: government bond price volatility,COVID-19,government policy responses,international financial markets,containment and closure,economic support,panel quantile regression. JEL classifications: G01
    Date: 2021–04–12
  15. By: Eduardo Dávila; Itay Goldstein
    Abstract: This paper studies the optimal determination of deposit insurance when bank runs are possible. We show that the welfare impact of changes in the level of deposit insurance coverage can be generally expressed in terms of a small number of sufficient statistics, which include the level of losses in specific scenarios and the probability of bank failure. We characterize the wedges that determine the optimal ex-ante regulation, which map to asset- and liability-side regulation. We demonstrate how to employ our framework in an application to the most recent change in coverage, which took place in 2008.
    JEL: G01 G21 G28
    Date: 2021–04
  16. By: Martin, Ian; Wagner, Christian
    Abstract: We derive a formula for the expected return on a stock in terms of the risk-neutral variance of the market and the stock's excess risk-neutral variance relative to that of the average stock. These quantities can be computed from index and stock option prices; the formula has no free parameters. The theory performs well empirically both in and out of sample. Our results suggest that there is considerably more variation in expected returns, over time and across stocks, than has previously been acknowledged.
    Keywords: Starting Grant 639744
    JEL: F3 G3
    Date: 2019–08–01
  17. By: Núñez Ferrer, Jorge
    Abstract: This is the first of a series of reports dedicated to the preparation and implementation of the Recovery and Resilience Facility of the EU. It sets out some of the main risks to the success of the recovery programmes, such as a lack of focus and mistargeting, maintaining unsustainable sectors, delays in implementation, and the lack of both a European dimension and the capacity to implement such a complex programme over and above the normal EU budget. These risks can be mitigated or avoided, however. The report also presents a number of solutions to ensure that aspects critical to the recovery programmes – and the key objective of longer-term resilience – are implemented. It highlights the necessity for serious structural reforms in member states, better management and control systems at EU level, well-designed active labour market policies, and a clear framework for public private partnerships to ensure that they are used more effectively.
    Date: 2021–03
  18. By: Ana-Maria Fuertes (Cass Business School, City, University of London, ECIY.); Maria-Dolores Robles (Universidad Complutense de Madrid (UCM).)
    Abstract: This paper documents a negative cross-transmission of bank-idiosyncratic credit risk events to the equity value of peers comprising other banks, insurance and real estate firms inter alia. Large jumps in the idiosyncratic component of bank CDS spreads significantly reduce the equity value of peers, particularly on the event day. The negative externality does not hinge on the “information connectedness” between the two entities as proxied by characteristics such as common core line of business, common country or region, and inter-country common legal tradition. The negative externality is stronger in turmoil market conditions when risk-aversion levels are higher and/or investors are subject to pessimism. The more fragile the risk profile of the event bank and peer firm prior to the event the stronger the cross-transmission. The findings lend support to the wake-up call paradigm at micro level, and are insightful towards a better assessment of the vulnerability of the financial system.
    Keywords: Credit Risk Events; Credit Default Swaps; Equity value; European banking; Cross-transmission; Wake-up Call.
    JEL: C13 C58 G14 G20
    Date: 2021–03
  19. By: Cristina Arellano; Yan Bai; Gabriel Mihalache
    Abstract: The coronavirus pandemic has severely impacted emerging markets by generating a large death toll, deep recessions, and a wave of sovereign defaults. We study this compound health, economic, and debt crisis and its mitigation by integrating epidemiological dynamics into a sovereign default model. The epidemic leads to an urgent need for social distancing measures, a large drop in economic activity, and a protracted debt crisis. The presence of default risk restricts fiscal space and presents emerging markets with a trade-off between mitigation of the pandemic and fiscal distress. A quantitative analysis of our model accounts well for the dynamics of deaths, social distance measures, and sovereign spreads in Latin America. In the model, the welfare cost of the pandemic is higher because of financial market frictions: about a third of the cost comes from default risk, compared with a version of the model with perfect financial markets. We study debt relief programs through counterfactuals and find a compelling case for their implementation, as they deliver large social gains.
    Date: 2021
  20. By: Ghassan, Hassan; Boulanouar, Zakaria; Hassan, Kabir Mohammed
    Abstract: Using a new panel cointegration test that considers serial correlation and cross-section dependence on a mixed and heterogenous sample of Saudi banks, we revisit the cointegrating equation of the z-score index of banking stability. Our results show that even when we consider the cross-section dependency and serial correlation of the errors, there is a possibility of a long-run relationship, which holds in our sample of banks. Furthermore, in the medium term, we found some banks to be integrated, whereas others were non-cointegrated. We interpret this to suggest that some banks contribute to banking stability, whereas others do not. In other words, there exists at least one bank that acts as a destabilizer and the challenge for financial regulators is to identify which banks these are. However, the current version of the Hadri et al. test does not allow for the identification of the non-cointegrated banks. If the test was able to do that, the regulatory authorities would be able to develop corrective policies/measures specifically tailored to the non-cointegrated units.
    Keywords: panel cointegration; banking stability; z-score
    JEL: C51 G21 G28
    Date: 2020
  21. By: Jan Rosenzweig
    Abstract: Portfolio optimization methods suffer from a catalogue of known problems, mainly due to the facts that pair correlations of asset returns are unstable, and that extremal risk measures such as maximum drawdown are difficult to predict due to the non-Gaussianity of portfolio returns. \\ In order to look at optimal portfolios for arbitrary risk penalty functions, we construct portfolio shapes where the penalty is proportional to a moment of the returns of arbitrary order $p>2$. \\ The resulting component weight in the portfolio scales sub-linearly with its return, with the power-law $w \propto \mu^{1/(p-1)}$. This leads to significantly improved diversification when compared to Kelly portfolios, due to the dilution of the winner-takes-all effect.\\ In the limit of penalty order $p\rightarrow\infty$, we recover the simple trading heuristic whereby assets are allocated a fixed positive weight when their return exceeds the hurdle rate, and zero otherwise. Infinite order power-law portfolios thus fall into the class of perfectly diversified portfolios.
    Date: 2021–04

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