nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒05‒30
twenty papers chosen by
Stan Miles
Thompson Rivers University

  1. Improving Portfolio Liquidity with Cash-Value-at-Risk for Covariance Estimations in Quantitative Trading By Tuan Tran; Nhat Nguyen
  2. Assessment of Support Vector Machine performance for default prediction and credit rating By Karim Amzile; Mohamed Habachi
  3. Failure of Gold, Bitcoin and Ethereum as safe havens during the Ukraine-Russia war By Alhonita Yatie
  4. What drives the risk of European banks during crises? New evidence and insights By Ion Lapteacru
  5. Asset quality assessment in the absence of quality data towards optimal credit intermediation By Njeru, Andrew Kioi
  6. Will claim history become a deprecated rating factor? An optimal design method for the real-time road risk model By Jiamin Yu
  7. Corporate Vulnerabilities in the Middle East, North Africa, and Pakistan in the Wake of COVID-19 Pandemic By Nordine Abidi; Mohamed Belkhir
  8. CVA in fractional and rough volatility models By Elisa Al\`os; Fabio Antonelli; Alessandro Ramponi; Sergio Scarlatti
  9. Addressing systemic risk in Europe during Covid-19: The role of regulation and the policy mix By Dotta, Vitor
  10. A model of system-wide stress simulation: market-based finance and the Covid-19 event By Giovanni di Iasio; Spyridon Alogoskoufis; Simon Kordel; Dominika Kryczka; Giulio Nicoletti; Nicholas Vause
  11. Modeling dynamic volatility under uncertain environment with fuzziness and randomness By Xianfei Hui; Baiqing Sun; Yan Zhou
  12. Deposit Insurance Coverage Level and Scope By International Association of Deposit Insurers
  13. Climate change and credit risk: the effect of carbon taxes on Italian banks’ business loan default rates By Maria Alessia Aiello; Cristina Angelico
  14. Addressing COVID-19 outliers in BVARs with stochastic volatility By Carriero, Andrea; Clark, Todd E.; Marcellino, Massimiliano; Mertens, Elmar
  15. High-Frequency-Based Volatility Model with Network Structure By Huiling Yuan; Guodong Li; Junhui Wang
  16. Transmission of Cyber Risk Through the Canadian Wholesale Payment System By Anneke Kosse; Zhentong Lu
  17. Kyle's Model with Stochastic Liquidity By Ibrahim Ekren; Brad Mostowski; Gordan \v{Z}itkovi\'c
  18. Race Lévy flights: A mathematically tractable framework for studying heavy-tailed accumulation noise By Hadian Rasanan, Amir Hosein; Evans, Nathan J.; Padash, Amin; Rad, Jamal Amani
  19. Deposit Insurance in 2022: Global Trends and Key Emerging Issues By Bert Van Roosebeke; Ryan Defina
  20. The Determinants of Risk Weighted Asset in Europe By Leogrande, Angelo; Costantiello, Alberto; Laureti, Lucio; Matarrese, Marco Maria

  1. By: Tuan Tran (EPHE - École pratique des hautes études - PSL - Université Paris sciences et lettres); Nhat Nguyen
    Abstract: Understanding characteristics of covariance matrix is an important research topic. In quantitative trading, portfolio liquidity is a hidden dimension and important as others such as portfolio volatility. In this paper, we propose a liquidity impact measure to improve the portfolio liquidity and also a novel Cash Value at Risk to evaluate the liquidity risk from portfolio cash perspective. Experimental results on various scenarios show that our approach improve a portfolio turnover significantly and also better than others on Cash Value at Risk in almost all cases. An interesting finding is that linear shrinkage covariance estimations not only improve the covariance structure but also resolve a large partial of liquidity.
    Keywords: portfolio liquidity,shrinkage estimation,covariance matrix
    Date: 2022–04–21
  2. By: Karim Amzile (Université Mohammed V); Mohamed Habachi (Université Mohammed V)
    Abstract: Predicting the creditworthiness of bank customers is a major concern for banking institutions, as modeling the probability of default is a key focus of the Basel regulations. Practitioners propose different default modeling techniques such as linear discriminant analysis, logistic regression, Bayesian approach, and artificial intelligence techniques. The performance of the default prediction is evaluated by the Receiver Operating Characteristic (ROC) curve using three types of kernels, namely, the polynomial kernel, the linear kernel and the Gaussian kernel. To justify the performance of the model, the study compares the prediction of default by the support vector with the logistic regression using data from a portfolio of particular bank customers. The results of this study showed that the model based on the Support Vector Machine approach with the Radial Basis Function kernel, performs better in prediction, compared to the logistic regression model, with a value of the ROC curve equal to 98%, against 71.7% for the logistic regression model. Also, this paper presents the conception of a support vector machine-based rating tool designed to classify bank customers and determine their probability of default. This probability has been computed empirically and represents the proportion of defaulting customers in each class.
    Keywords: bank,credit risk,data mining,probability of default,scoring,artificial intelligence
    Date: 2022
  3. By: Alhonita Yatie (BSE - Bordeaux Sciences Economiques - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)
    Abstract: This paper studies the impact of fear, uncertainty and market volatility caused by the Ukraine-Russia war on crypto-assets returns (Bitcoin and Ethereum) and Gold returns. We use the searches on Wikipedia trends as proxies of uncertainty and fear and two volatility indices: S&P500 VIX and the Russian VIX (RVIX). The results show that Bitcoin, Ethereum and Gold failed as safe havens during this war.
    Keywords: War,Russia,Ukraine,crypto-assets,Gold,Safe haven
    Date: 2022–03–30
  4. By: Ion Lapteacru (BSE - Bordeaux Sciences Economiques - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)
    Abstract: Based on an extensive dataset of 1,156 European banks over the 1995-2015 period, we aim to provide new insights on the determinants of European banks' risk-taking during crisis events, employing a novel asymmetric Z-score. Our results suggest that more capital, lower ratios of loans to deposits and of liquid assets to total assets and lower share of non-deposit and short-term funding in total funding are associated with lower bank risk and this relationship is stronger during the crises. Moreover, having low costs compared to their revenues reduces the risk of European banks in normal times and has the same impact during the crises. Being involved in non-interest-generating activities makes banks riskier. Finally, being large and having higher net interest margin make banks more stable, but this positive effect is diminished for the size and vanished for the profitability during crisis times. And some differences are observed between Western and Eastern European countries.
    Keywords: European banking,bank risk,financial crisis,Z-score
    Date: 2022–03–30
  5. By: Njeru, Andrew Kioi
    Abstract: The COVID-19 pandemic has posed a significant challenge for credit managers and risk management of financial institutions and regulators worldwide. The challenge faced arises because the consequences of outbreaks and epidemics are not distributed equally throughout, with some sectors of the economy suffering disproportionately. Businesses within the same sector are not affected the same way. This paper uses high-frequency transaction data and data obtained through web scraping to simulate firm's behaviour and performance during a crisis to estimate the sectoral impact of the pandemic and its pass-through to the portfolio of financial institutions and ultimately on economic growth. This proactive approach is critical due to the rapidly evolving nature of the crisis and delays by customers submission of the books of accounts and the impact of various measures such as lockdown and selected sector shutdowns undertaken by authorities that may have diverse implications for different businesses in various sectors of the economy thereby compromising he ability of risk managers to accurately forecast the performance of their portfolios.
    Date: 2022
  6. By: Jiamin Yu
    Abstract: With the popularity of Telematics and Self-driving, more and more rating factors, such as mileage, route, driving behavior, etc., are introduced into actuarial models. There are quite a few doubts and disputes on the rationality and accuracy of the selection of rating variables, but it does not involve the widely accepted historical claim records. Recently, Tesla Insurance released a new generation of Safety Score-based insurance, irrespective of accident history. Forward-looking experts and scholars began to discuss whether claim history will disappear in the future auto insurance rate-making system. Therefore, this paper proposes a new risk variable elimination method as well as a real-time road risk model design framework and concludes that claim history will be regarded as a "noise" factor and deprecated in the Pay-How-You-Drive model.
    Date: 2022–04
  7. By: Nordine Abidi; Mohamed Belkhir
    Abstract: This paper analyzes corporate vulnerabilities in the Middle East, North Africa and Pakistan (MENAP hereafter) in the wake of the COVID-19 pandemic shock. Using a sample of nearly 700 firms from eleven countries in MENAP, we assess the non-financial corporate (NFC) sector’s liquidity and solvency risk and viability over the medium term under different stress test scenarios. Our findings suggest that the health crisis has exacerbated vulnerabilities in the corporate sector, though the effects are heterogenous across the region. Small firms, which entered the pandemic in a more vulnerable position, would remain under high liquidity stress over the medium term, putting a substantial share of these firms’ debt at risk of default. Similarly, liquidity needs of firms in contact-intensive sectors have also worsened and would remain elevated in 2022-23. We also show that an adverse scenario of subdued growth and premature withdrawal of policy support would impair the capacity to service interest expenses, especially among small firms, resulting in higher insolvency risk. Overall, our results indicate that some segments of the MENAP corporate sector could remain reliant on policy support during the recovery phase and that structural reforms are critical to save distressed but viable firms from bankruptcy and ensure an efficient liquidation of “zombie” firms.
    Keywords: MENAP, COVID-19 crisis, non-financial corporate vulnerabilities, stress tests, “zombification”, policy support.; non-financial corporate; solvency risk; liquidity needs; NFC stress tests; vulnerabilities in the Middle East; COVID-19; Liquidity; Solvency; Stress testing; Corporate sector; Middle East; North Africa; Middle East and Central Asia; Global
    Date: 2022–04–29
  8. By: Elisa Al\`os; Fabio Antonelli; Alessandro Ramponi; Sergio Scarlatti
    Abstract: In this work we present a general representation formula for the price of a vulnerable European option, and the related CVA in stochastic (either rough or not) volatility models for the underlying's price, when admitting correlation with the default event. We specialize it for some volatility models and we provide price approximations, based on the representation formula. We study numerically their accuracy, comparing the results with Monte Carlo simulations, and we run a theoretical study of the error. We also introduce a seminal study of roughness influence on the claim's price.
    Date: 2022–04
  9. By: Dotta, Vitor
    Abstract: This work examines the impacts which the Covid-19 pandemic brought to the stability of the European financial sector. Lockdowns, businesses unable to operate and uncertainty about how the pandemic would evolve fueled a sharp recession. From the lessons learned in the global financial crises and the Eurozone debt crises, there's an increasing role of macroprudential policies, especially the regiments of the Basel III framework and the monetary policy toolkit. Alongside macroprudential regulation, the European Central Bank provided substantial monetary policy easing, for instance the release of capital buffers and other capital requirements, expanding the TLTRO III and Pandemic Emergency Program which facilitated monetary policy transmission. Authorities also deployed strong fiscal policies which encompassed from tax holidays to direct transfers to households and firms. The combination of fiscal, monetary, and regulatory policy was unprecedented and helped the economy during the shutdown moments. As a result, indicators of systemic risks in the banking sector during the pandemic remained relatively stable.
    Keywords: Systemic Risk,Covid-19 pandemic,banks,banking sector,Europe,Policy Mix,Monetary and Fiscal policy
    JEL: G21 G28 G38 E58 E62 E63
    Date: 2022
  10. By: Giovanni di Iasio (Bank of Italy); Spyridon Alogoskoufis (European Central Bank); Simon Kordel (European Central Bank); Dominika Kryczka (European Central Bank); Giulio Nicoletti (European Central Bank); Nicholas Vause (Bank of England)
    Abstract: We build a model to simulate how the euro-area market-based financial system may function under stressed conditions, such as the COVID-19 turmoil. The core of the model is a set of representative agents reflecting key economic sectors, which interact in asset, funding and derivatives markets and face solvency and liquidity constraints on their behaviour. We illustrate the model’s behaviour with a two-layer approach. In Layer 1, we consider the deterioration in the outlook for the nonfinancial corporate sector. Agents reallocate their portfolios and risky asset prices fall. Layer 2 adds a rating downgrade shock to Layer 1, where a fraction of investment grade nonfinancial corporate bonds is downgraded to high yield. The additional shock creates further rebalancing pressure and price movements. For both layers we present asset flows (i.e. buying and selling marketable securities) across agents and balance sheet losses. The model provides quantitative support to the equilibrium effects of the macroprudential regulation of investment funds, which we illustrate by varying their liquidity buffers.
    Keywords: Systemic risk, market-based finance, stress testing, COVID-19
    JEL: G17 G21 G22 G23
    Date: 2022–04
  11. By: Xianfei Hui; Baiqing Sun; Yan Zhou
    Abstract: Predicting the dynamic volatility in financial market provides a promising method for risk prediction, asset pricing and market supervision. Barndorff-Nielsen and Shephard model (BN-S) model, used to capture the stochastic behavior of high-frequency time series, is an accepted stochastic volatility model with L\' evy process. Although this model is attractive and successful in theory, it needs to be improved in application. We build a new generalized BN-S model suitable for uncertain environment with fuzziness and randomness. This new model considers the delay phenomenon between price fluctuation and volatility changes, solves the problem of the lack of long-range dependence of classic models. Calculation results show that new model outperforms the classic model in volatility forecasting. Experiments on Dow Jones Industrial Average futures price data are conducted to verify feasibility and practicability of our proposed approach. Numerical examples are provided to illustrate the theoretical result. Three machine learning algorithms are applied to estimate new model parameter. Compared with the classical model, our method effectively combines the uncertain environmental characteristics, which makes the prediction of dynamic volatility more flexible and has ideal performance.
    Date: 2022–04
  12. By: International Association of Deposit Insurers
    Abstract: Coverage levels and scope are key components of efficient deposit insurance systems. This paper maps important considerations and practices in this connection by analysing the results of a 2020 IADI survey on the topic. This is complemented by findings from the 2020 IADI annual survey and by case studies provided by selected jurisdictions which detail issues that were identified as unique or novel. The paper ends with specific suggestions about how these findings could inform future revisions of the IADI Core Principles regarding Core Principle 8 - Coverage.
    Keywords: deposit insurance, bank resolution
    JEL: G21 G33
    Date: 2021–12
  13. By: Maria Alessia Aiello (Bank of Italy); Cristina Angelico (Bank of Italy)
    Abstract: Climate change poses severe systemic risks to the financial sector through multiple transmission channels. In this paper, we estimate the potential impact of different carbon taxes (€50, €100, €200 and €800 per ton of CO2) on the Italian banks’ default rates at the sector level in the short term using a counterfactual analysis. We build on the micro-founded climate stress test approach proposed by Faiella et al. (2021), which estimates the energy demand of Italian firms using granular data and simulates the effects of the alternative taxes on the share of financially vulnerable agents (and their debt). Credit risks stemming from introducing a carbon tax – during periods of low default rates – are modest on banks: on average, in a one-year horizon, the default rates of firms increase but remain below their historical averages. The effect is heterogeneous across different sectors and rises with the tax value; however, even assuming a tax of €800 per ton of CO2, the default rates are lower than the historical peaks.
    Keywords: climate change, carbon tax, climate stress test, banks’ credit risk
    JEL: Q43 Q48 Q58 G21
    Date: 2022–04
  14. By: Carriero, Andrea; Clark, Todd E.; Marcellino, Massimiliano; Mertens, Elmar
    Abstract: The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting density forecasts are much less sensitive to outliers in the data than standard VARs. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best data fit for the pandemic period, as well as for earlier subsamples of relatively high volatility. In historical forecasting, outlier-augmented SV schemes fare at least as well as a conventional SV model.
    Keywords: Bayesian VARs,stochastic volatility,outliers,pandemics,forecasts
    JEL: C53 E17 E37 F47
    Date: 2022
  15. By: Huiling Yuan; Guodong Li; Junhui Wang
    Abstract: This paper introduces one new multivariate volatility model that can accommodate an appropriately defined network structure based on low-frequency and high-frequency data. The model reduces the number of unknown parameters and the computational complexity substantially. The model parameterization and iterative multistep-ahead forecasts are discussed and the targeting reparameterization is also presented. Quasi-likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation experiments are carried out to assess the performance of the estimation in finite samples. An empirical example is demonstrated that the proposed model outperforms the network GARCH model, with the gains being particularly significant at short forecast horizons.
    Date: 2022–04
  16. By: Anneke Kosse; Zhentong Lu
    Abstract: In this paper, we study how the impact of a cyber-attack that paralyzes one or multiple banks’ ability to send payments would transmit to other banks through the Canadian wholesale payments system. Based on historical payment data, we simulate a wide range of scenarios and evaluate the total payment disruption in the system. We find that depending on the type and number of banks under attack, the time of the attack and the design of the payments system, the attack can quickly become systemic and result in a significant loss of liquidity in the system. For instance, a three-hour attack on one bank can in the worst case impair the payments capacity of seven other banks within less than an hour and eventually disrupt 25% of the daily payments value. We also demonstrate that the system-wide impact of an attack can be significantly reduced by contingency plans that enable attacked banks to still send high-value payments. Given the interconnectedness of banks, we conclude that the cyber-resilience of a wholesale payment system strongly depends on the cyber-resilience of its participants and underline the importance of strong sectoral collaboration and coordination.
    Keywords: Payment clearing and settlement systems; Financial institutions; Financial stability
    JEL: C49 E47 G21
    Date: 2022–05
  17. By: Ibrahim Ekren; Brad Mostowski; Gordan \v{Z}itkovi\'c
    Abstract: We construct an equilibrium for the continuous time Kyle's model with stochastic liquidity, a general distribution of the fundamental price, and correlated stock and volatility dynamics. For distributions with positive support, our equilibrium allows us to study the impact of the stochastic volatility of noise trading on the volatility of the asset. In particular, when the fundamental price is log-normally distributed, informed trading forces the log-return up to maturity to be Gaussian for any choice of noise-trading volatility even though the price process itself comes with stochastic volatility. Surprisingly, we find that in equilibrium both Kyle's Lambda and its inverse (the market depth) are submartingales.
    Date: 2022–04
  18. By: Hadian Rasanan, Amir Hosein; Evans, Nathan J.; Padash, Amin; Rad, Jamal Amani
    Abstract: Lévy flights is a particular exemplar of the generalised random walk processes, in which the jump lengths are drawn from a power-law asymptote ($\alpha$-stable) distribution. While employing this heavy-tailed distribution for accumulation noise within the diffusion decision model framework provides improved fitting performance over the standard Gaussian accumulation noise, the Lévy flight model contains two key limitations. Specifically, the use of the diffusion framework limits the model to only being applicable to two alternative decision-making tasks, and the lack of an exact likelihood function can make fitting behavioral data a challenging and computationally costly task. This paper aims to provide a mathematically tractable framework for modeling $n$-alternative ($n\geq1$) decision-making tasks with an $\alpha$-stable distribution for the accumulation noise. We propose a race Lévy flight model, with a racing architecture, making the model directly applicable to decision tasks with any number of alternatives. Moreover, with the corresponding space-fractional diffusion-advection equation for the accumulation process, a numerical scheme based on approximating of the joint probability distribution for each accumulator is provided. In addition, we fit our proposed model to two perceptual decision-making data sets, and show its improved performance compared to the standard racing diffusion model.
    Date: 2022–04–10
  19. By: Bert Van Roosebeke (International Association of Deposit Insurers); Ryan Defina (International Association of Deposit Insurers)
    Abstract: Deposit insurers operate within an ever-evolving global financial system and hence are subject to change themselves. This report explores some of the key dynamics in the deposit insurance industry and identifies the five emerging issues which are expected to significantly affect the activities of deposit insurers in the near future.
    Keywords: deposit insurance, bank resolution
    JEL: G21 G33
    Date: 2022–02
  20. By: Leogrande, Angelo; Costantiello, Alberto; Laureti, Lucio; Matarrese, Marco Maria
    Abstract: We have estimated the level of Risk Weighted Assets among 30 countries in Europe, in 30 trimesters, using data of the European Banking Authority-EBA of 139 variables. We perform an econometric model using Pooled OLS, Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Least Squares. We found that Risk Weighted Assets is negatively associated, among others, to the level of NFC loans in mining and quarrying, in public administration and defence, and in financial and insurance activities and positively associated, among others to distribution of NFC loans in human health services and social work activities, in education and the level of net fee and commission income. Furthermore, we apply a cluster analysis with the k-Means algorithm, and we find the presence of two clusters. A comparison was then made between eight different machine learning algorithms for predicting the value of the RWAs and we found that the best predictor is the linear regression. The RWA value is predicted to increase by 1.5%.
    Keywords: Financial Institutions and Services; General; Banks, Depository Institutions, Micro Finance Institutions, Mortgages; Investment Banking, Government Policy, and Regulation
    JEL: G0 G20 G21 G24 G28
    Date: 2022–05–01

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