nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒03‒21
seventeen papers chosen by
Stan Miles
Thompson Rivers University

  1. Honour Thesis: A Joint Value at Risk and Expected Shortfall Combination Framework and its Applications in the Cryptocurrency Market By Zhengkun Li
  2. Cyber Loss Model Risk Translates to Premium Mispricing and Risk Sensitivity By Gareth W. Peters; Matteo Malavasi; Pavel V. Shevchenko; Georgy Sofronov; Stefan Tr\"uck; Jiwook Jang
  3. Quantification of systemic risk from overlapping portfolios in the financial system By Poledna, Sebastian; Martínez-Jaramillo, Serafín; Caccioli, Fabio; Thurner, Stefan
  4. FisrEbp: Enterprise Bankruptcy Prediction via Fusing its Intra-risk and Spillover-Risk By Yu Zhao; Shaopeng Wei; Yu Guo; Qing Yang; Gang Kou
  5. Risk Management Practice Adopted in Road Construction Project By Gain, Hemant; Mishra, Anjay Kumar; Aithal, Sreeramana
  6. The Distribution of Crisis Credit: Effects on Firm Indebtedness and Aggregate Risk By Federico Huneeus; Joseph P. Kaboski; Mauricio Larrain; Sergio L. Schmukler; Mario Vera
  7. Machine Learning for Credit Scoring: Improving Logistic Regression with Non-Linear Decision-Tree Effects By Elena Ivona Dumitrescu; Sullivan Hué; Christophe Hurlin; Sessi Tokpavi
  8. On financial market correlation structures and diversification benefits across and within equity sectors By Nick James; Max Menzies; Georg Gottwald
  9. A 2D Levy-flight model for the complex dynamics of real-life financial markets By Hediye Yarahmadi; Abbas Ali Saberi
  10. Tail-risk Comprehension and Protection in Real-time Electricity Pricing : Experimental Evidence By Pretto, Madeline
  11. The Effect of Third-Party Funds, Credit Risk, Market Risk, and Operational Risk on Profitability in Banking for Period 2014-2017 By Sondakh, Jullie J.; Tulung, Joy E.; Karamoy, Herman
  12. Empirical assessment on factors contributing to integrity practices of Malaysian public sector officers By Razana Johari; Md. Mahmudul Alam; Jamaliah Said
  13. Lifecycle Earnings Risk and Insurance: New Evidence from Australia By Darapheak Tin; Chung Tran
  14. Factors Determining Z-score and Corporate Failure in Malaysian Companies By Azhar, Nurul Izzaty Hasanah; Lokman, Norziana; Alam, Md. Mahmudul; Said, Jamaliah
  15. On Solving Robust Log-Optimal Portfolio: A Supporting Hyperplane Approximation Approach By Chung-Han Hsieh
  16. Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings? By Matthew Harding; Gabriel F. R. Vasconcelos
  17. A Safe Asset Perspective for an Integrated Policy Framework By Markus K. Brunnermeier; Sebastian Merkel; Yuliy Sannikov

  1. By: Zhengkun Li
    Abstract: Value at risk and expected shortfall are increasingly popular tail risk measures in the financial risk management field. Both academia and financial institutions are working to improve tail risk forecasts in order to meet the requirements of the Basel Capital Accord; it states that one purpose of risk management and measuring risk accuracy is, since extreme movements cannot always be avoided, financial institutions can prepare for these extreme returns by capital allocation, and putting aside the appropriate amount of capital so as to avoid default in times of extreme price or index movements. Forecast combination has drawn much attention, as a combined forecast can outperform the individual forecasts under certain conditions. We propose two methodology, one is a semiparametric combination framework that can jointly produce combined value at risk and expected shortfall forecasts, another one is a parametric regression framework named as Quantile-ES regression that can produce combined expected shortfall forecasts. The favourability of the semiparametric combination framework has been presented via an empirical study - application in cryptocurrency markets with high-frequency data where the necessity of risk management application increases as the cryptocurrency market becomes more popular and mature. Additionally, the general framework of the parametric Quantile-ES regression has been presented via a simulation study, whereas it still need to be improved in the future. The contributions of this work include but are not limited to the enabling of the combination of expected shortfall forecasts and the application of risk management procedures in the cryptocurrency market with high-frequency data.
    Date: 2022–02
  2. By: Gareth W. Peters (Statistics & Applied Probability, University of California Santa Barbara); Matteo Malavasi (Actuarial Studies and Business Analytics, Macquarie University, Australia); Pavel V. Shevchenko (Actuarial Studies and Business Analytics, Macquarie University, Australia; Center for Econometrics and Business Analytics, Saint-Petersburg State University, Russia); Georgy Sofronov (Mathematical and Physical Sciences, Macquarie University, Australia); Stefan Tr\"uck (Actuarial Studies and Business Analytics, Macquarie University, Australia); Jiwook Jang (Actuarial Studies and Business Analytics, Macquarie University, Australia)
    Abstract: We focus on model risk and risk sensitivity when addressing the insurability of cyber risk. The standard statistical approaches to assessment of insurability and potential mispricing are enhanced in several aspects involving consideration of model risk. Model risk can arise from model uncertainty, and parameters uncertainty. We demonstrate how to quantify the effect of model risk in this analysis by incorporating various robust estimators for key model parameter estimates that apply in both marginal and joint cyber risk loss process modelling. We contrast these robust techniques with standard methods previously used in studying insurabilty of cyber risk. This allows us to accurately assess the critical impact that robust estimation can have on tail index estimation for heavy tailed loss models, as well as the effect of robust dependence analysis when quantifying joint loss models and insurance portfolio diversification. We argue that the choice of such methods is akin to a form of model risk and we study the risk sensitivity that arise from choices relating to the class of robust estimation adopted and the impact of the settings associated with such methods on key actuarial tasks such as premium calculation in cyber insurance. Through this analysis we are able to address the question that, to the best of our knowledge, no other study has investigated in the context of cyber risk: is model risk present in cyber risk data, and how does is it translate into premium mispricing? We believe our findings should complement existing studies seeking to explore insurability of cyber losses. In order to ensure our findings are based on realistic industry informed loss data, we have utilised one of the leading industry cyber loss datasets obtained from Advisen, which represents a comprehensive data set on cyber monetary losses, from which we form our analysis and conclusions.
    Date: 2022–02
  3. By: Poledna, Sebastian; Martínez-Jaramillo, Serafín; Caccioli, Fabio; Thurner, Stefan
    Abstract: Financial markets create endogenous systemic risk, the risk that a substantial fraction of the system ceases to function and collapses. Systemic risk can propagate through different mechanisms and channels of contagion. One important form of financial contagion arises from indirect interconnections between financial institutions mediated by financial markets. This indirect interconnection occurs when financial institutions invest in common assets and is referred to as overlapping portfolios. In this work we quantify systemic risk from indirect interconnections between financial institutions. Complete information of security holdings of major Mexican financial intermediaries and the ability to uniquely identify securities in their portfolios, allows us to represent the Mexican financial system as a bipartite network of securities and financial institutions. This makes it possible to quantify systemic risk arising from overlapping portfolios. We show that focusing only on direct interbank exposures underestimates total systemic risk levels by up to 50% under the assumptions of the model. By representing the financial system as a multi-layer network of direct interbank exposures (default contagion) and indirect external exposures (overlapping portfolios) we estimate the mutual influence of different channels of contagion. The method presented here is the first quantification of systemic risk on national scales that includes overlapping portfolios.
    Keywords: financial networks; financial regulation; multi-layer networks; overlapping portfolios; systemic risk
    JEL: D85 G18 G21
    Date: 2021–02
  4. By: Yu Zhao; Shaopeng Wei; Yu Guo; Qing Yang; Gang Kou
    Abstract: In this paper, we propose to model enterprise bankruptcy risk by fusing its intra-risk and spillover-risk. Under this framework, we propose a novel method that is equipped with an LSTM-based intra-risk encoder and GNNs-based spillover-risk encoder. Specifically, the intra-risk encoder is able to capture enterprise intra-risk using the statistic correlated indicators from the basic business information and litigation information. The spillover-risk encoder consists of hypergraph neural networks and heterogeneous graph neural networks, which aim to model spillover risk through two aspects, i.e. hyperedge and multiplex heterogeneous relations among enterprise knowledge graph, respectively. To evaluate the proposed model, we collect multi-sources SMEs data and build a new dataset SMEsD, on which the experimental results demonstrate the superiority of the proposed method. The dataset is expected to become a significant benchmark dataset for SMEs bankruptcy prediction and promote the development of financial risk study further.
    Date: 2022–01
  5. By: Gain, Hemant; Mishra, Anjay Kumar; Aithal, Sreeramana
    Abstract: Purpose: Zero risk construction is only a dream not reality as there is nothing certainity in the real world and scientists are only capable of coverting the same into risk with high level data science. The practice of risk management is an attempt to highlight risk elements with a case of urban road construction in Sindhupalchowk district, Province 3, Nepal. Design/Methodology/Approach: The 5-point Likert scale questionnaire survey was done to collect the primary data. Risk Management Practice is documented based on survey response in percentage through charts and graphs. Field visit were done for visual assessment of the construction procedure along with key informant interview and Secondary data of Detail Project Reports, Design and Drawings were effectively analyzed. Cronbach’s alpha was used to measure the reliability and triangulations were done for validity. Findings/Result: The results from this research indicates that contractor’s organization are averagely aware with a mean score of 3.30 about Risk Management Practice and averagely practicing risk management formally with mean score of 2.83. They are averagely analyzing risk management techniques with mean score of 3.07. Mean score is slightly higher based on client’s perspective with score for awareness being 3.93 and score for risk management being practiced formally is 3.13. Risk analysis score based on client’s management is 3.40. Mostly adopted technique of risk identification is monitoring and evaluation report of similar past projects and direct judgment is widely used technique for risk assessment of road construction projects at Sindhupalchowk district based on both client’s and contractor’s perspective. Risk response strategy based on contractor’s perspective is monitoring the risk and preparing contingency plan whereas that for client is transfer of risk. Originality/Value: It is action research which is significant for professionals to understand the practices of risk management being adopted by Nepalese Contractors in hilly region of Nepal.
    Keywords: Risk, Practice, Identification, Assessment, Response
    JEL: I31 O1 O18 O2 R4
    Date: 2022–01–21
  6. By: Federico Huneeus; Joseph P. Kaboski; Mauricio Larrain; Sergio L. Schmukler; Mario Vera
    Abstract: We study the distribution of credit during crisis times and its impact on firm indebtedness and macroeconomic risk. Whereas policies can help firms in need of financing, they can lead to adverse selection from riskier firms and higher default risk. We analyze a large-scale program of public credit guarantees in Chile during the COVID-19 pandemic using unique transaction-level data of demand and supply of credit, matched with administrative tax data, for the universe of banks and firms. Credit demand channels loans toward riskier firms, distributing 4.6% of GDP and increasing firm leverage. Despite increased lending to riskier firms at the micro level, macroeconomic risks remain small. Several factors mitigate aggregate risk: the small weight of riskier firms, the exclusion of the riskiest firms, bank screening, contained expected defaults, and the government absorption of tail risk. We quantitatively confirm our empirical findings with a model of heterogeneous firms and endogenous default.
    JEL: E44 E5 G01
    Date: 2022–02
  7. By: Elena Ivona Dumitrescu (EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique); Sullivan Hué (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Christophe Hurlin (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours); Sessi Tokpavi (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - UT - Université de Tours)
    Abstract: In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in the credit risk industry mainly because the lack of interpretability of ensemble methods is incompatible with the requirements of financial regulators. In this paper, we propose a high-performance and interpretable credit scoring method called penalised logistic tree regression (PLTR), which uses information from decision trees to improve the performance of logistic regression. Formally, rules extracted from various short-depth decision trees built with original predictive variables are used as predictors in a penalised logistic regression model. PLTR allows us to capture non-linear effects that can arise in credit scoring data while preserving the intrinsic interpretability of the logistic regression model. Monte Carlo simulations and empirical applications using four real credit default datasets show that PLTR predicts credit risk significantly more accurately than logistic regression and compares competitively to the random forest method.
    Keywords: Risk management,Credit scoring,Machine learning,Interpretability,Econometrics
    Date: 2022–03–16
  8. By: Nick James; Max Menzies; Georg Gottwald
    Abstract: We study how to assess the potential benefit of diversifying an equity portfolio by investing within and across equity sectors. We analyse 20 years of US stock price data, which includes the global financial crisis (GFC) and the COVID-19 market crash, as well as periods of financial stability, to determine the `all weather' nature of equity portfolios. We establish that one may use the leading eigenvalue of the cross-correlation matrix of log returns as well as graph-theoretic diagnostics such as modularity to quantify the collective behaviour of the market or a subset of it. We confirm that financial crises are characterised by a high degree of collective behaviour of equities, whereas periods of financial stability exhibit less collective behaviour. We argue that during times of increased collective behaviour, risk reduction via sector-based portfolio diversification is ineffective. Using the degree of collectivity as a proxy for the benefit of diversification, we perform an extensive sampling of equity portfolios to confirm the old financial adage that 30-40 stocks provide sufficient diversification. Using hierarchical clustering, we discover a `best value' equity portfolio for diversification consisting of 36 equities sampled uniformly from 9 sectors. We further show that it is typically more beneficial to diversify across sectors rather than within. Our findings have implications for cost-conscious retail investors seeking broad diversification across equity markets.
    Date: 2022–02
  9. By: Hediye Yarahmadi; Abbas Ali Saberi
    Abstract: We report on the emergence of scaling laws in the temporal evolution of the daily closing values of the S\&P 500 index prices and its modeling based on the L\'evy flights in two dimensions (2D). The efficacy of our proposed model is verified and validated by using the extreme value statistics in random matrix theory. We find that the random evolution of each pair of stocks in a 2D price space is a scale-invariant complex trajectory whose tortuosity is governed by a $2/3$ geometric law between the gyration radius $R_g(t)$ and the total length $\ell(t)$ of the path, i.e., $R_g(t)\sim\ell(t)^{2/3}$. We construct a Wishart matrix containing all stocks up to a specific variable period and look at its spectral properties over 30 years. In contrast to the standard random matrix theory, we find that the distribution of eigenvalues has a power-law tail with a decreasing exponent over time -- a quantitative indicator of the temporal correlations. We find that the time evolution of the distance of a 2D L\'evy flights with index $\alpha=3/2$ from origin generates the same empirical spectral properties. The statistics of the largest eigenvalues of the model and the observations are in perfect agreement.
    Date: 2022–02
  10. By: Pretto, Madeline (Monash University)
    Abstract: Do households comprehend the nature of price tail-risks inherent to real-time electricity pricing plans? Through an incentivised online experiment, we find that a probabilistic risk disclosure elicits greater demand for real-time pricing (RTP) products relative to a low-risk fixed-price alternative, without improving comprehension of tail-risk in RTP. Participants also show a tendency to place low value on tail-risk protection. Finally, the experience of a bill shock improves risk comprehension and drives choice away from RTP, suggesting that personal experience plays a greater role in self-imposed risk protection than does a probabilistic risk disclosure. We discuss the implications these findings may have for regulators with a consumer protection mandate.
    Keywords: Vietnam retail electricity market ; block rate pricing ; welfare effect ; electricity externalities ; demand function ; cash transfer ; quantity-based subsidy JEL Classification: D12 ; D63 ; Q41 ; Q48
    Date: 2021
  11. By: Sondakh, Jullie J.; Tulung, Joy E.; Karamoy, Herman
    Abstract: The study aimed to investigate the effect of third-party funds, credit risk, market risk, and operational risk on profitability in banking, especially on the banks included in BUKU 2 category simultaneously or partially. The sampling technique used in the study was saturated sampling. Therefore, a number of 54 banks were obtained as samples. The data in the study were quantitative data, namely in form of financial statements of banking companies included in BUKU 2 category for period 2014-2017. The data were obtained from websites of the concerned banks. The research method used was multiple linear regression analysis. In the study, to measure the Third-Party Funds variable used DPK ratio, to measure the Credit Risk variable used NPL and NPF ratio, to measure the Market Risk variable used NIM ratio, to measure the Operational Risk variable used BOPO ratio, and to measure the Profitability variable used ROA ratio. The result of the study showed that partially third-party funds and credit risk had no significant effect on profitability, partially market risk had significant positive effect on profitability, and partially credit risk had significant negative effect on profitability. While simultaneously, third-party funds, credit risk, market risk, and operational risk had significant effect on profitability.
    Keywords: Third-Party Funds, Credit Risk, Market Risk, Operational Risk
    JEL: G21 G32
    Date: 2021–03–07
  12. By: Razana Johari (UiTM - Universiti Teknologi MARA [Shah Alam]); Md. Mahmudul Alam (UUM - Universiti Utara Malaysia); Jamaliah Said (UiTM - Universiti Teknologi MARA [Shah Alam])
    Abstract: Purpose Integrity-related issues are now endemic to public service bureaucracies. It is claimed that corruption in the public sector is very common in various departments/agencies. Lack of integrity will lead to failings in governance and proper oversight of procedures, and subsequently poor financial management and incidents of fraud. Based on the stakeholder theory perspective, this study examines the influences of accountability, risk management and managerial commitment on practices of integrity in the Malaysian public sector. Design/methodology/approach Primary data were collected through both printed and online questionnaires given to 210 department heads operating within selected Malaysian federal ministries. Data were analysed via the partial least squares-structural equation modelling (PLS-SEM) approach to examine the research hypotheses. Findings It is evident that integrity practices in Malaysia's public sector are statistically significantly related to risk management, accountability and management commitment. Practical implications The findings will help the Malaysian federal ministries to take the necessary steps to improve integrity so that dependability and efficiency are the hallmarks of public sector services. Originality/value To the best of the authors' knowledge, this study is one of the first to examine the role of accountability, risk management and managerial commitment to integrity in the public sector of a developing market economy.
    Keywords: Public Sector,Integrity,Risk Management,Accountability,Management Commitment
    Date: 2020–12–25
  13. By: Darapheak Tin; Chung Tran
    Abstract: We study the nature of lifecycle earnings dynamics by documenting higher-order moments of earnings shocks over the lifecycle, using the Household, Income and Labour Dynamics in Australia (HILDA) Survey 2001-2020. Similar to other countries (e.g. see Guvenen et al. (2021) and De Nardi et al. (2021)), the distribution of earnings shocks in Australia displays negative skewness and excess kurtosis, deviating from the conventional linearity and normality assumptions. However, the sources of fluctuations and the role of family and government insurance are quite different. Wages account more for the dispersion of earnings shocks (second-order risk), while hours drive the negative skewness and excess kurtosis (third- and fourth-order risks, respectively). Wage changes are strongly associated with earnings changes, whereas hour changes are largely absent in upward movement and relatively small in downward movement of earnings changes. Family insurance via pooling income of family members and adjusting labor market activities of secondary earners, and government insurance embedded in the progressive tax and transfer system play distinct roles in reducing risks over age and by income group. Government insurance is more important in mitigating the dispersion of earnings shocks; meanwhile, family insurance is more dominant in mitigating the magnitude and likelihood of extreme and rare shocks. Family insurance interacts with government insurance; however, their joint forces fail to eliminate the non-Gaussian and non-linear features. Furthermore, comparison between groups reveals: (i) the risk equalizing effect of government insurance, and (ii) the persistent nature of risks for certain demographics such as female heads of household and non-parents. Hence, our findings shed new insights into the complexity of earnings dynamics and the importance of family and government insurance.
    Keywords: Income dynamics; Earnings risk; Higher-order moments; Non-Gaussian shocks; Family insurance; Government insurance; Inequality
    JEL: E24 H24 H31 J31
    Date: 2022–03
  14. By: Azhar, Nurul Izzaty Hasanah; Lokman, Norziana; Alam, Md. Mahmudul (Universiti Utara Malaysia); Said, Jamaliah
    Abstract: Predicting the sustainability of a business is crucial to prevent financial losses among shareholders and investors. This study attempts to evaluate the Altman model for predicting corporate failure in distressed and non-distressed Malaysian companies based on the data of financially troubled companies which are classified as Practice Note 17 (PN17) and matching similar non-PN17 companies during the period 2013 to 2017. This study utilizes panel ordinal and panel random effects regressions. Findings show that the liquidity, profitability, leverage, solvency, and efficiency ratios are negatively significantly associated with corporate failure and bankruptcy. The leverage ratio is determined to be the strongest indicator of bankruptcy, followed by profitability, liquidity, solvency, and efficiency ratios. The findings will help companies’ management bodies implement suitable strategies to prevent further financial leakage, thereby ensuring continuous and sustainable return on investment and profits for investors and shareholders.
    Date: 2021–11–30
  15. By: Chung-Han Hsieh
    Abstract: A {log-optimal} portfolio is any portfolio that maximizes the expected logarithmic growth (ELG) of an investor's wealth. This maximization problem typically assumes that the information of the true distribution of returns is known to the trader in advance. However, in practice, the return distributions are indeed {ambiguous}; i.e., the true distribution is unknown to the trader or it is partially known at best. To this end, a {distributional robust log-optimal portfolio problem} formulation arises naturally. While the problem formulation takes into account the ambiguity on return distributions, the problem needs not to be tractable in general. To address this, in this paper, we propose a {supporting hyperplane approximation} approach that allows us to reformulate a class of distributional robust log-optimal portfolio problems into a linear program, which can be solved very efficiently. Our framework is flexible enough to allow {transaction costs}, {leverage and shorting}, {survival trades}, and {diversification considerations}. In addition, given an acceptable approximation error, an efficient algorithm for rapidly calculating the optimal number of hyperplanes is provided. Some empirical studies using historical stock price data are also provided to support our theory.
    Date: 2022–02
  16. By: Matthew Harding; Gabriel F. R. Vasconcelos
    Abstract: We use machine learning techniques to investigate whether it is possible to replicate the behavior of bank managers who assess the risk of commercial loans made by a large commercial US bank. Even though a typical bank already relies on an algorithmic scorecard process to evaluate risk, bank managers are given significant latitude in adjusting the risk score in order to account for other holistic factors based on their intuition and experience. We show that it is possible to find machine learning algorithms that can replicate the behavior of the bank managers. The input to the algorithms consists of a combination of standard financials and soft information available to bank managers as part of the typical loan review process. We also document the presence of significant heterogeneity in the adjustment process that can be traced to differences across managers and industries. Our results highlight the effectiveness of machine learning based analytic approaches to banking and the potential challenges to high-skill jobs in the financial sector.
    Date: 2022–02
  17. By: Markus K. Brunnermeier (Princeton University); Sebastian Merkel (Princeton University); Yuliy Sannikov (Stanford University)
    Abstract: Borrowing from Brunnermeier and Sannikov (2016, 2019) this policy paper sketches a policy framework for emerging market economies by mapping out the roles and interactions of monetary policy, macroprudential policies, foreign exchange interventions, and capital controls. Safe assets are central in a world in which financial frictions, distribution of risk, and risk premia are important elements. The paper also proposes a global safe asset for a more self-stabilizing global financial architecture.
    Keywords: Safe asset, bubbles, international capital flows, capital controls, monetary policy, macroprudential policy, FX interventions
    JEL: E52 F38
    Date: 2020–05

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