nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒11‒07
eighteen papers chosen by
Stan Miles
Thompson Rivers University

  1. "Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions". By Xenxo Vidal-Llana; Carlos Salort Sánchez; Vincenzo Coia; Montserrat Guillen
  2. Monte-Carlo Estimation of CoVaR By Weihuan Huang; Nifei Lin; L. Jeff Hong
  3. Multilevel Monte Carlo and its Applications in Financial Engineering By Devang Sinha; Siddhartha P. Chakrabarty
  4. The Distribution of Crisis Credit : Effects on Firm Indebtedness and Aggregate Risk By Huneeus,Federico; Kaboski,Joseph P.; Larrain,Mauricio; Schmukler,Sergio L.; Vera,Mario
  5. Modelling multivariate extreme value distributions via Markov trees By Hu, Shuang; Peng, Zuoxiang; Segers, Johan
  6. A recursive method for computing moments of Hawkes intensities: application to the potential approach of credit risk By Ketelbuters, John-John; Hainaut, Donatien
  7. Sandwiched Volterra Volatility model: Markovian approximations and hedging By Giulia Di Nunno; Anton Yurchenko-Tytarenko
  8. Allocation of benefits in mutual aid and survivor funds By Denuit, Michel; Robert, Christian Y.
  9. Working With Convex Responses: Antifragility From Finance to Oncology By Nassim Nicholas Taleb; Jeffrey West
  10. Privatizing Disability Insurance By Seibold, Arthur; Seitz, Sebastian; Siegloch, Sebastian
  11. Structural Volatility Impulse Response Analysis By Fengler, Matthias; Polivka, Jeannine
  12. It’s not time to make a change: sovereign fragility and the corporate credit risk By Fornari, Fabio; Zaghini, Andrea
  13. Smoke and Mirrors : Infrastructure State-Owned Enterprises and Fiscal Risks By Herrera Dappe,Matias; Musacchio,Aldo; Pan,Carolina; Semikolenova,Yadviga Viktorivna; Turkgulu,Burak; Barboza,Jonathan
  14. Greening capital requirements By Dafermos, Yannis; van Lerven, Frank; Nikolaidi, Maria
  15. Stock Volatility Prediction using Time Series and Deep Learning Approach By Ananda Chatterjee; Hrisav Bhowmick; Jaydip Sen
  16. Potential Applications of Quantum Computing for the Insurance Industry By Michael Adam
  17. Measuring Systemic Banking Resilience : A Simple Reverse Stress Testing Approach By Feyen,Erik H.B.; Mare,Davide Salvatore
  18. A Survey: Credit Sentiment Score Prediction By A. N. M. Sajedul Alam; Junaid Bin Kibria; Arnob Kumar Dey; Zawad Alam; Shifat Zaman; Motahar Mahtab; Mohammed Julfikar Ali Mahbub; Annajiat Alim Rasel

  1. By: Xenxo Vidal-Llana (Universitat de Barcelona. Gran Via de les Corts Catalanes 585. 08007 Barcelona, Spain.); Carlos Salort Sánchez (Universitat de Barcelona. Gran Via de les Corts Catalanes 585. 08007 Barcelona, Spain.); Vincenzo Coia (University of British Columbia. West Mall 2329. Vancouver, BC Canada.); Montserrat Guillen (Gran Via de les Corts Catalanes 585. 08007 Barcelona, Spain.)
    Abstract: When datasets present long conditional tails on their response variables, algorithms based on Quantile Regression have been widely used to assess extreme quantile behaviors. Value at Risk (VaR) and Conditional Tail Expectation (CTE) allow the evaluation of extreme events to be easily interpretable. The state-of-the-art methodologies to estimate VaR and CTE controlled by covariates are mainly based on linear quantile regression, and usually do not have in consideration non-crossing conditions across VaRs and their associated CTEs. We implement a non-crossing neural network that estimates both statistics simultaneously, for several quantile levels and ensuring a list of non-crossing conditions. We illustrate our method with a household energy consumption dataset from 2015 for quantile levels 0.9, 0.925, 0.95, 0.975 and 0.99, and show its improvements against a Monotone Composite Quantile Regression Neural Network approximation.
    Keywords: Risk evaluation, Deep learning, Extreme quantiles. JEL classification: C31, C45, C52.
    Date: 2022–10
  2. By: Weihuan Huang; Nifei Lin; L. Jeff Hong
    Abstract: ${\rm CoVaR}$ is one of the most important measures of financial systemic risks. It is defined as the risk of a financial portfolio conditional on another financial portfolio being at risk. In this paper we first develop a Monte-Carlo simulation-based batching estimator of CoVaR and study its consistency and asymptotic normality. We show that the optimal rate of convergence of the batching estimator is $n^{-1/3}$, where $n$ is the sample size. We then develop an importance-sampling inspired estimator under the delta-gamma approximations to the portfolio losses, and we show that the rate of convergence of the estimator is $n^{-1/2}$. Numerical experiments support our theoretical findings and show that both estimators work well.
    Date: 2022–10
  3. By: Devang Sinha; Siddhartha P. Chakrabarty
    Abstract: In this article, we present a review of the recent developments on the topic of Multilevel Monte Carlo (MLMC) algorithm, in the paradigm of applications in financial engineering. We specifically focus on the recent studies conducted in two subareas, namely, option pricing and financial risk management. For the former, the discussion involves incorporation of the importance sampling algorithm, in conjunction with the MLMC estimator, thereby constructing a hybrid algorithm in order to achieve reduction for the overall variance of the estimator. In case of the latter, we discuss the studies carried out in order to construct an efficient algorithm in order to estimate the risk measures of Value-at-Risk (VaR) and Conditional Var (CVaR), in an efficient manner. In this regard, we briefly discuss the motivation and the construction of an adaptive sampling algorithm with an aim to efficiently estimate the nested expectation, which, in general is computationally expensive.
    Date: 2022–09
  4. By: Huneeus,Federico; Kaboski,Joseph P.; Larrain,Mauricio; Schmukler,Sergio L.; Vera,Mario
    Abstract: This paper studies the distribution of credit during crisis times and its impact on firmindebtedness and macroeconomic risk. Whereas policies can help firms in need of financing, they can lead to adverseselection from riskier firms and higher default risk. The paper analyzes a large-scale program of public creditguarantees in Chile during the COVID-19 pandemic using unique transaction-level data on the demand and supply ofcredit, matched with administrative tax data, for the universe of banks and firms. Credit demand channels loanstoward riskier firms, distributing 4.6 percent of gross domestic product and increasing firm leverage. Despiteincreased lending to riskier firms, macroeconomic risks remain small. Several factors mitigate aggregate risk: thesmall weight of riskier firms, the exclusion of the riskiest firms, bank screening, contained expected defaults, and thegovernment absorption of tail risk. The empirical findings are confirmed with a model of heterogeneous firms andendogenous default.
    Keywords: Financial Crisis Management & Restructuring,Financial Regulation & Supervision,Administrative & Civil Service Reform,De Facto Governments,Public Sector Administrative and Civil Service Reform,Public Sector Administrative & Civil Service Reform,Democratic Government,Labor Markets
    Date: 2022–02–14
  5. By: Hu, Shuang; Peng, Zuoxiang; Segers, Johan (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: Multivariate extreme value distributions are a common choice for modelling mul- tivariate extremes. In high dimensions, however, the construction of flexible and par- simonious models is challenging. We propose to combine bivariate extreme value dis- tributions into a Markov random field with respect to a tree. Although in general not an extreme value distribution itself, this Markov tree is attracted by a multivari- ate extreme value distribution. The latter serves as a tree-based approximation to an unknown extreme value distribution with the given bivariate distributions as margins. Given data, we learn an appropriate tree structure by Prim’s algorithm with estimated pairwise upper tail dependence coefficients or Kendall’s tau values as edge weights. The distributions of pairs of connected variables can be fitted in various ways. The resulting tree-structured extreme value distribution allows for inference on rare event probabili- ties, as illustrated on river discharge data from the upper Danube basin.
    Keywords: Kendall’s tau ; Markov tree ; Multivariate extreme value distribution ; Prim’s algorithm ; probabilistic graphical model ; rare event ; tail dependence
    Date: 2022–08–05
  6. By: Ketelbuters, John-John (Université catholique de Louvain, LIDAM/ISBA, Belgium); Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium)
    Abstract: This paper explores an alternative to the structural models and reduced models in credit risk. The approach we use is called the potential approach. In the context of credit risk, it consists in assuming that the survival probability of a company is equal to the ratio of the expected value of a supermartingale divided by its initial value. This approach, that was previously used for modelling the term structure of interest rates, is extended by the use of a self-exciting processess that is time-changed by the inverse of an alpha-stable subordinator. We derive a new recursive method that allows to compute all the moments of a self-exciting process intensity. We show that this method can be used to approximate the survival probabilities in the potential aproach. More specifically, we prove that the approximation converges and we provide a bound on the numerical error. Finally, we calibrate the model and show that it allows to properly fit survival probability curves that are highly convex.
    Date: 2022–08–29
  7. By: Giulia Di Nunno; Anton Yurchenko-Tytarenko
    Abstract: We consider stochastic volatility dynamics driven by a general H\"older continuous Volterra-type noise and with unbounded drift. For such models, we consider the explicit computation of quadratic hedging strategies. While the theoretical solution is well-known in terms of the non-anticipating derivative for all square integrable claims, the fact that these models are typically non-Markovian provides a concrete difficulty in the direct computation of conditional expectations at the core of the explicit hedging strategy. To overcome this difficulty, we propose a Markovian approximation of the model which stems from an adequate approximation of the kernel in the Volterra noise. We study the approximation of the volatility, the prices as well as the optimal mean-square hedge and provide the corresponding error estimates. We complete the work with numerical simulations performed with different methods.
    Date: 2022–09
  8. By: Denuit, Michel (Université catholique de Louvain, LIDAM/ISBA, Belgium); Robert, Christian Y.
    Abstract: Consider a group of individuals contributing in advance (ex ante) a fixed amount to a pool constituted over a reference period to mutually protect against the financial consequences of the occurrence of a predefined event such as death, survival or being diagnosed with a critical illness for instance. They agree that the sum of the contributions is shared in arrear (ex post) among those participants having experienced the predefined event. The allocation can be uniform among claiming participants, each one receiving an equal share of total contributions, or participants may be offered the choice to select in advance an expected amount of benefit, corresponding to their desired protection level. As an application, the paper considers mutual aid funds and survivor funds. The proposed systems are fully funded since contributions are paid in advance. The benefits in case the event occurs are therefore random but the volatility of the terminal payouts turns out to be limited when the number of participants gets large enough. Insurance at fair price is recovered at the limit, within infinitely large pools. The link with takaful insurance is discussed, as well as minimum guar- antees to make the system more attractive.
    Keywords: Risk pooling ; Actuarial fairness ; Mutual inheritance ; Insurance at fair price ; Takaful
    Date: 2022–09–19
  9. By: Nassim Nicholas Taleb; Jeffrey West
    Abstract: We extend techniques and learnings about the stochastic properties of nonlinear responses from finance to medicine, particularly oncology where it can inform dosing and intervention. We define antifragility. We propose uses of risk analysis to medical problems, through the properties of nonlinear responses (convex or concave). We 1) link the convexity/concavity of the dose-response function to the statistical properties of the results; 2) define "antifragility" as a mathematical property for local beneficial convex responses and the generalization of "fragility" as its opposite, locally concave in the tails of the statistical distribution; 3) propose mathematically tractable relations between dosage, severity of conditions, and iatrogenics. In short we propose a framework to integrate the necessary consequences of nonlinearities in evidence-based oncology and more general clinical risk management.
    Date: 2022–09
  10. By: Seibold, Arthur (University of Mannheim); Seitz, Sebastian (University of Manchester); Siegloch, Sebastian (University of Cologne)
    Abstract: Public disability insurance (DI) programs in many countries face pressure to reduce their generosity in order to remain sustainable. In this paper, we investigate the welfare effects of giving a larger role to private insurance markets in the face of public DI cuts. Exploiting a unique reform that abolished one part of the German public DI system for younger workers, we find that despite significant crowding-in effects, overall private DI take-up remains modest. We do not find any evidence of adverse selection on unpriced risk. On the contrary, private DI tends to be concentrated among high-income, high-education and low-risk individuals. Using a revealed preferences approach, we estimate individual DI valuations, a key input for welfare calculations. We find that observed willingness-to-pay of many individuals is low, such that providing DI partly via a private insurance market with choice improves welfare. However, we show that distributional concerns as well as individual risk misperceptions can provide grounds for justifying a full public DI mandate.
    Keywords: disability insurance, social insurance, mandate, privatization, risk-based selection, welfare
    JEL: H55 G22 G52
    Date: 2022–09
  11. By: Fengler, Matthias; Polivka, Jeannine
    Abstract: In this paper, we make three contributions to the volatility impulse response function (VIRF) developed by Hafner and Herwartz (2006), the most widely applied impulse response function in the context of multivariate volatility models. First, we derive its law for multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models of the BEKK type. Second, we present a structural embedding of the VIRF by relying on recent developments concerning identification of MGARCH models. This broadens the use cases of the VIRF, which has previously been limited to historical analyses, by allowing for counterfactual and out-of-sample scenario analyses of volatility responses. Third, we show how to endow the VIRF with a causal interpretation. We illustrate the merits of a structural VIRF analysis by investigating the impacts of historical shock events as well as the consequences of well-defined future shock scenarios on the U.S. equity, government bond and foreign exchange markets. Our findings suggest that it is vital to be able to assess the statistical significance of volatility impulse responses.
    Keywords: causality in volatility, multivariate GARCH models, proxy identification, structural identification, volatility impulse response functions
    JEL: C32 C58 G17
    Date: 2022–10
  12. By: Fornari, Fabio; Zaghini, Andrea
    Abstract: Relying on a perspective borrowed from monetary policy announcements and introducing an econometric twist in the traditional event study analysis, we document the existence of an "event risk transfer", namely a significant credit risk transmission from the sovereign to the corporate sector after a sovereign rating downgrade. We find that after the delivery of the downgrade, corporate CDS spreads rise by 36% per annum and there is a widespread contagion across countries, in particular among those which were most exposed to the sovereign debt crisis. This effect exists on top of the standard relation between sovereign and corporate credit risk. JEL Classification: C21, G12, G14
    Keywords: credit default swaps, credit rating, sovereign risk spillover
    Date: 2022–10
  13. By: Herrera Dappe,Matias; Musacchio,Aldo; Pan,Carolina; Semikolenova,Yadviga Viktorivna; Turkgulu,Burak; Barboza,Jonathan
    Abstract: Infrastructure is critical to economic development. When infrastructure companies areowned and operated by the government, however, they create significant sources of fiscal risk. These fiscal risks canbe sizable, but they are often preventable with proper planning, risk assessment, and strict rules and proceduresfor corporate and fiscal governance. This paper examines fiscal risk stemming from state-owned enterprises (SOEs) inthe infrastructure sector in a sample of 135 firms in 19 countries from an original database of SOE financials for2009–18. The paper develops a typology of fiscal risks and their determinants, builds new measures of fiscal injectionsto SOEs, and documents them using the novel database. The results show that governments support SOEs through aremarkably wide range of fiscal instruments. The fiscal costof supporting infrastructure SOEs is usually below 1 percent of gross domestic product. Support is more prevalent andfrequent than previously thought. The findings show that fiscal risk stems not only from “tail risk,” but also fromthe everyday operation of infrastructure SOEs. The paper calculates the Altman Z” score (a measure of default risk)and shows that it can be used to forecast the need for fiscal injections in SOEs.
    Date: 2022–03–15
  14. By: Dafermos, Yannis; van Lerven, Frank; Nikolaidi, Maria
    Abstract: Capital requirements play a central role in financial regulation and have significant implications for financial stability and credit allocation. However, in their existing form, they fail to capture environment-related financial risks and act as a barrier to the transition to an environmentally sustainable economy. This paper considers how capital requirements can become green and explores how green differentiated capital requirements (GDCRs) can be incorporated into financial regulation frameworks.
    JEL: F3 G3
    Date: 2022–10–07
  15. By: Ananda Chatterjee; Hrisav Bhowmick; Jaydip Sen
    Abstract: Volatility clustering is a crucial property that has a substantial impact on stock market patterns. Nonetheless, developing robust models for accurately predicting future stock price volatility is a difficult research topic. For predicting the volatility of three equities listed on India's national stock market (NSE), we propose multiple volatility models depending on the generalized autoregressive conditional heteroscedasticity (GARCH), Glosten-Jagannathan-GARCH (GJR-GARCH), Exponential general autoregressive conditional heteroskedastic (EGARCH), and LSTM framework. Sector-wise stocks have been chosen in our study. The sectors which have been considered are banking, information technology (IT), and pharma. yahoo finance has been used to obtain stock price data from Jan 2017 to Dec 2021. Among the pulled-out records, the data from Jan 2017 to Dec 2020 have been taken for training, and data from 2021 have been chosen for testing our models. The performance of predicting the volatility of stocks of three sectors has been evaluated by implementing three different types of GARCH models as well as by the LSTM model are compared. It has been observed the LSTM performed better in predicting volatility in pharma over banking and IT sectors. In tandem, it was also observed that E-GARCH performed better in the case of the banking sector and for IT and pharma, GJR-GARCH performed better.
    Date: 2022–10
  16. By: Michael Adam
    Abstract: This paper is the documentation of a pre-study performed by AXA Konzern AG in collaboration with Fraunhofer ITWM to assess the relevance of quantum computing for the insurance industry. Beside a general overview of the status quo of quantum computing technologies, we investigate its applicability for the valuation of insurance contracts as a concrete use case. This valuation is a computationally intensive problem because the lack of closed pricing formulas requires the use of Monte Carlo methods. Therefore current technical capabilities force insurers to apply approximation methods for many subsequent tasks like economic capital calculation or optimization of strategic asset allocations. The business-criticality of these tasks combined with the existence of a quantum algorithm called Amplitude Estimation which promises a quadratic speed-up of Monte Carlo simulation makes this use case obvious. We provide a detailed explanation of Amplitude Estimation and present two quantum circuits which describe insurance-related payoff features in a quantum circuit model. An exemplary circuit that encodes dynamic lapse is evaluated both on a simulator and on real quantum hardware.
    Date: 2022–10
  17. By: Feyen,Erik H.B.; Mare,Davide Salvatore
    Abstract: Reverse stress tests can be a useful tool to evaluate bank resilience to a credit shock,especially in environments where financial data are limited or opaque. This paper develops a simple and transparentcountry-level banking sector resilience indicator that focuses on tail risks, the Consolidated Distance toBreakpoint. Based on individual bank reverse stress test results, this novel metric quantifies the increase innonperforming loans needed to deplete capital buffers for a subset of the most fragile banks that collectively representat least 20 percent of total banking system assets, a level commonly associated with a systemic banking crisis. Thepaper calculates the Consolidated Distance to Breakpoint using public data for more than 1,500 banks in 59 emergingmarket and developing economies during the COVID-19 pandemic. The paper explores the value added of this metricin relation to widely used country-level macro-financial and soundness indicators. The results show that the associationof the Consolidated Distance to Breakpoint with these macro-financial and financial soundness indicators islimited. This suggests that this new indicator encapsulates complementary information, possibly because aggregatemeasures may obscure challenges in individual banks. As such, the Consolidated Distance to Breakpoint metric couldserve as a useful input to establish a basic understanding of a banking sector’s resilience.
    Keywords: Banks & Banking Reform,Financial Sector Policy,Economic Growth,Health Care Services Industry
    Date: 2021–11–29
  18. By: A. N. M. Sajedul Alam; Junaid Bin Kibria; Arnob Kumar Dey; Zawad Alam; Shifat Zaman; Motahar Mahtab; Mohammed Julfikar Ali Mahbub; Annajiat Alim Rasel
    Abstract: Manual approvals are still used by banks and other NGOs to approve loans. It takes time and is prone to mistakes because it is controlled by a bank employee. Several fields of machine learning mining technologies have been utilized to enhance various areas of credit rating forecast. A major goal of this research is to look at current sentiment analysis techniques that are being used to generate creditworthiness.
    Date: 2022–09

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