nep-rmg New Economics Papers
on Risk Management
Issue of 2020‒08‒17
25 papers chosen by
Stan Miles
Thompson Rivers University

  1. Adjusted Expected Shortfall By Matteo Burzoni; Cosimo Munari; Ruodu Wang
  2. IRB Asset and Default Correlation: Rationale for the Macroprudential Add-ons to the Risk-Weights By Henry Penikas
  3. Lao People’s Democratic Republic; Technical Assistance Report-Risk-Based Banking Supervision By International Monetary Fund
  4. Bank Profitability and Financial Stability By TengTeng Xu; Kun Hu; Udaibir S Das
  5. Pricing equity-linked life insurance contracts with multiple risk factors by neural networks By Karim Barigou; Lukasz Delong
  6. Tail risk forecasting using Bayesian realized EGARCH models By Vica Tendenan; Richard Gerlach; Chao Wang
  7. Dynamic optimal reinsurance and dividend-payout in finite time horizon By Chonghu Guan; Zuo Quan Xu; Rui Zhou
  8. An EM algorithm for fitting a new class of mixed exponential regression models with varying dispersion By Tzougas, George; Karlis, Dimitris
  9. Measuring and Managing COVID-19 Model Risk By Mark J. Jensen
  10. Refundable deductible insurance By Maria Mercè Claramunt; Maite Màrmol
  11. Overview and Inventory of French Funds' Liquidity Management Tools By Pierre-Emmanuel Darpeix; Caroline Le Moign; Nicolas Même; Marko Novakovic
  12. How Risk-taker Evaluate Risk-taking Behaviour Based on Entrepreneurial Orientation By Prastya, Darya Yudanta
  13. Dynamic dependence and extreme risk comovement: The case of oil prices and exchange rates By Ji, Qiang; Liu, Bing-Yue; Nguyen, Duc Khuong; Fan, Ying
  14. The time-varying risk of Italian GDP By Fabio Busetti; Michele Caivano; Davide Delle Monache; Claudia Pacella
  15. Asset Prices and Capital Share Risks: Theory and Evidence By Byrne, Joseph P; Ibrahim, Boulis Maher; Zong, Xiaoyu
  16. All the bottles in one basket? Diversification and product portfolio composition By Friberg, Richard
  17. To securitise or to price credit default risk? By McGowan, Danny; Nguyen, Huyen
  18. Stock Return Predictability and Variance Risk Premia around the ZLB By Toshiaki Ogawa; Masato Ubukata; Toshiaki Watanabe
  19. Systemic Risk-Shifting in Financial Networks By Elliott, M.; Georg, C-P.; Hazell, J.
  20. Prudence and prevention - Empirical evidence* By Thomas Mayrhofer; Hendrik Schmitz
  21. Solving High-Order Portfolios via Successive Convex Approximation Algorithms By Rui Zhou; Daniel P. Palomar
  22. Dysfunctional markets: A spray of prey perspective. By Olivier Mesly; David W. Shanafelt; Nicolas Huck
  23. The Use of Data in Assessing and Designing Insolvency Systems By José Garrido; Wolfgang Bergthaler; Chanda M DeLong; Juliet Johnson; Amira Rasekh; Anjum Rosha; Natalia Stetsenko
  24. Tax-Aware Portfolio Construction via Convex Optimization By Nicholas Moehle; Mykel J. Kochenderfer; Stephen Boyd; Andrew Ang
  25. Towards better understanding of complex machine learning models using Explainable Artificial Intelligence (XAI) - case of Credit Scoring modelling By Marta Kłosok; Marcin Chlebus

  1. By: Matteo Burzoni; Cosimo Munari; Ruodu Wang
    Abstract: We introduce and study the main properties of a class of convex risk measures that refine Expected Shortfall by simultaneously controlling the expected losses associated with different portions of the tail distribution. The corresponding adjusted Expected Shortfalls quantify risk as the minimum amount of capital that has to be raised and injected into a financial position $X$ to ensure that Expected Shortfall $ES_p(X)$ does not exceed a pre-specified threshold $g(p)$ for every probability level $p\in[0,1]$. Through the choice of the benchmark risk profile $g$ one can tailor the risk assessment to the specific application of interest. We devote special attention to the study of risk profiles defined by the Expected Shortfall of a benchmark random loss, in which case our risk measures are intimately linked to second-order stochastic dominance.
    Date: 2020–07
  2. By: Henry Penikas (Bank of Russia, Russian Federation)
    Abstract: Basel III allows for the use of statistical models. It is called the internal-ratings-based (IRB) approach and is based on the (Vasicek, 2002) model. It assumes assets returns are standard normally distributed. It suggests incorporating different asset correlation (R) functions to assess credit risk for the loan portfolio, or the risk-weighted assets (RWA). The asset correlation func-tion solely depends on the individual default probability (PD) given certain credit exposure type. At the same time, the IRB approach requires developing PD models to predict the dis-crete default event occurrence. This means that the IRB approach is based on the Bernoulli trials. We investigate the impact of the asset returns’ correlation for the Bernoulli trials. We show that when Bernoulli trials are considered, the credit risk estimation significantly deviate from the val-ues derived under the normality assumption of asset returns. We investigate the simulated and real-world credit rating agencies’ data to specifically demonstrate the scale of the credit risk underestimation by the IRB approach. Therefore, macroprudential add-ons are of use to offset such IRB limitations.
    Keywords: Basel II, IRB, correlated defaults, asset correlation, binomial distribution, Bernoulli trials, macroprudential add-ons (mark-ups)
    JEL: C25 G21 G28 G32 G33
    Date: 2020–07
  3. By: International Monetary Fund
    Abstract: The Banking Supervision Department (BSD) of the BoL is implementing risk-based supervision (RBS) methods. BoL staff are showing favorable results in understanding and applying RBS, recognizing that they are still in the early stages of capacity development. A new commercial banking law became effective in June 2019. The law incorporates expectations that financial institutions establish appropriate risk management systems and maintain adequate capital and liquidity. The law also gives the BoL purview over the adequacy of risk management in banks.
    Keywords: Financial institutions;Financial systems;Banking systems;Banking law;Commercial banks;ISCR,CR,mission team,RAS,RBS,ROX,onsite
    Date: 2020–06–18
  4. By: TengTeng Xu; Kun Hu; Udaibir S Das
    Abstract: We analyze how bank profitability impacts financial stability from both theoretical and empirical perspectives. We first develop a theoretical model of the relationship between bank profitability and financial stability by exploring the role of non-interest income and retail-oriented business models. We then conduct panel regression analysis to examine the empirical determinants of bank risks and profitability, and how the level and the source of bank profitability affect risks for 431 publicly traded banks (U.S., advanced Europe, and GSIBs) from 2004 to 2017. Results reveal that profitability is negatively associated with both a bank’s contribution to systemic risk and its idiosyncratic risk, and an over-reliance on non-interest income, wholesale funding and leverage is associated with higher risks. Low competition is associated with low idiosyncratic risk but a high contribution to systemic risk. Lastly, the problem loans ratio and the cost-to-income ratio are found to be key factors that influence bank profitability. The paper’s findings suggest that policy makers should strive to better understand the source of bank profitability, especially where there is an over-reliance on market-based non-interest income, leverage, and wholesale funding.
    Keywords: Systemic risk;Financial crises;Central banks;Macroprudential policies and financial stability;Financial institutions;Financial markets;Finanical stability,bank profitability,non-interest income,business model,panel regression,General,Models with Panel Data,VaR,NII,retail-based,LTA,profitability
    Date: 2019–01–11
  5. By: Karim Barigou (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon); Lukasz Delong (Warsaw School of Economics - Institut of Econometrics)
    Abstract: This paper considers the pricing of equity-linked life insurance contracts with death and survival benefits in a general model with multiple stochastic risk factors: interest rate, equity, volatility, unsystematic and systematic mortality. We price the equity-linked contracts by assuming that the insurer hedges the risks to reduce the local variance of the net asset value process and requires a compensation for the non-hedgeable part of the liability in the form of an instantaneous standard deviation risk margin. The price can then be expressed as the solution of a system of non-linear partial differential equations. We reformulate the problem as a backward stochastic differential equation with jumps and solve it numerically by the use of efficient neural networks. Sensitivity analysis is performed with respect to initial parameters and an analysis of the accuracy of the approximation of the true price with our neural networks is provided.
    Keywords: Equity-linked contracts,Neural networks,Stochastic mortality,BSDEs with jumps,Hull-White stochastic interest rates,Heston model
    Date: 2020–07–16
  6. By: Vica Tendenan; Richard Gerlach; Chao Wang
    Abstract: This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model, which can incorporate multiple realized volatility measures for the modelling of a return series. The realized EGARCH model is extended by adopting a standardized Student-t and a standardized skewed Student-t distribution for the return equation. Different types of realized measures, such as sub-sampled realized variance, sub-sampled realized range, and realized kernel, are considered in the paper. The Bayesian Markov chain Monte Carlo (MCMC) estimation employs the robust adaptive Metropolis algorithm (RAM) in the burn in period and the standard random walk Metropolis in the sample period. The Bayesian estimators show more favourable results than maximum likelihood estimators in a simulation study. We test the proposed models with several indices to forecast one-step-ahead Value at Risk (VaR) and Expected Shortfall (ES) over a period of 1000 days. Rigorous tail risk forecast evaluations show that the realized EGARCH models employing the standardized skewed Student-t distribution and incorporating sub-sampled realized range are favored, compared to a range of models.
    Date: 2020–08
  7. By: Chonghu Guan; Zuo Quan Xu; Rui Zhou
    Abstract: This paper studies a dynamic optimal reinsurance and dividend-payout problem for an insurer in a finite time horizon. The goal of the insurer is to maximize its expected cumulative discounted dividend payouts until bankruptcy or maturity which comes earlier. The insurer is allowed to dynamically choose reinsurance contracts over the whole time horizon. This is a singular control problem and the corresponding Hamilton-Jacobi-Bellman equation is a variational inequality with fully nonlinear operator and with gradient constraint. A comparison principle and $C^{2,1}$ smoothness for the solution are established by penalty approximation method. We find that the surplus-time space can be divided into three non-overlapping regions by a ceded risk and time dependent reinsurance barrier and a time dependent dividend-payout barrier. The insurer should be exposed to higher risk as surplus increases; exposed to all risk once surplus upward crosses the reinsurance barrier; and pay out all reserves in excess of the dividend-payout barrier. The localities of these regions are explicitly estimated.
    Date: 2020–08
  8. By: Tzougas, George; Karlis, Dimitris
    Abstract: Regression modelling involving heavy-tailed response distributions, which have heavier tails than the exponential distribution, has become increasingly popular in many insurance settings including non-life insurance. Mixed Exponential models can be considered as a natural choice for the distribution of heavy-tailed claim sizes since their tails are not exponentially bounded. This paper is concerned with introducing a general family of mixed Exponential regression models with varying dispersion which can efficiently capture the tail behaviour of losses. Our main achievement is that we present an Expectation-Maximization (EM)-type algorithm which can facilitate maximum likelihood (ML) estimation for our class of mixed Exponential models which allows for regression specifications for both the mean and dispersion parameters. Finally, a real data application based on motor insurance data is given to illustrate the versatility of the proposed EM-type algorithm.
    Keywords: mixed exponential distributions; EM algorithm; regression models for the mean and dispersion parameters; non-life insurance; heavy-tailed losses
    JEL: C1
    Date: 2020–05
  9. By: Mark J. Jensen
    Abstract: One of the many lessons learned from the financial crisis is the increased awareness of model risk. In this article, I apply the best practices of model risk management found in SR 11-7 (which offers regulatory guidance on the best practices for managing model risk) to COVID-19 models. In particular, I investigate the Institute of Health Metrics and Evaluation's (IHME) model to see if it has been effectively challenged with a critical assessment of its conceptual soundness, ongoing monitoring, and outcomes analysis.
    Keywords: COVID-19; model risk management; SR 11-7
    JEL: C1 C11 C52
    Date: 2020–06–18
  10. By: Maria Mercè Claramunt (UB - Universitat de Barcelona); Maite Màrmol
    Abstract: Most insurance policies include a deductible, so that a part of the claim is assumed by the insured. In order to get a full coverage of the claim, the insured has two options: hire a Zero Deductible Insurance or take out an insurance policy with deductible and, simultaneously, a Refundable Deductible Insurance. The objective of this paper is to analyze these two options, comparing the premium paid. We define dif (F) as the difference between the premiums paid. This function depends on the parameters of the deductible applied, and we focus our attention on the sign of this difference and the calculation of the optimal deductible, that is, the values of the parameters of the deductible that allows us to obtain the greatest reduction in the global premium.
    Keywords: premium calculation,variance criterion,optimization
    Date: 2020–07–30
  11. By: Pierre-Emmanuel Darpeix; Caroline Le Moign; Nicolas Même; Marko Novakovic
    Abstract: This article constitutes a first presentation of the prospectus analysis tool developed through a cooperation between the Banque de France and the Autorité des marchés financiers (AMF). This work aims to identify the liquidity management tools (LMT) implemented funds regulated by the AMF under French law (“French funds”). This work is part of a logic to follow up on IOSCO recommendations (2018) to strengthen the overall liquidity risk management framework in line with the recommendations adopted by the FSB (2017). In order to better manage the liquidity risk of investment funds, numerous measures are provided for by international texts, and imposed at the level of European Directives and Regulations governing funds and management companies or recommended by ESMA. In addition to the rules for monitoring and managing liquidity, certain tools can be used in normal times or in times of stress, in order to limit the risks of fire-sales or to mitigate their impact. This paper presents these liquidity management tools as precisely as possible and describes the appropriation of these tools by French funds at the end of 2019, based on a textual analysis of the prospectuses.
    Keywords: Investment Funds, Prospectus, Liquidity Management Tools, Information Retrieval .
    JEL: G23
    Date: 2020
  12. By: Prastya, Darya Yudanta
    Abstract: Dalam mengambil keputusan, para pengambil keputusan dan juga pengusaha akan mempertimbangkan resiko dan dampak apa saja yang akan diterima dan perlu untuk ditanggung agar dapat meningkatkan kinerja perusahaan dan juga memperoleh keungulan kompetitif diantara perusahaan pesaing. Hal ini perlu dipelajari di kalangan para pengambil keputusan dan juga wirausahawan dalam rangka memperoleh keunggulan kompetitif dengan resiko paling minim
    Date: 2020–06–23
  13. By: Ji, Qiang; Liu, Bing-Yue; Nguyen, Duc Khuong; Fan, Ying
    Abstract: This paper aims at investigating the dynamic dependence and extreme risk comovement of oil price and exchange rates in seven oil-importing and seven oil-exporting countries. For this purpose, we use six representative time- varying copula models and four types of tail dependences to assess the downside and upside conditional value-at-risk measures (CoVaRs). Our findings indicate that the dependence of crude oil returns and exchange rates is negative for most pairs, i.e., the rise (fall) in oil prices was accompanied by the appreciation (depreciation) of foreign currency against the US dollar. The oil price – exchange rate dependences in oil exporters are slightly larger than in oil importers, even though the dependence is weak in general. More interestingly, we find strong evidence of significant risk comovement between crude oil returns and exchange rates through the analysis of downside and upside CoVaRs. This comovement particularly showed asymmetric effects.
    Keywords: Time-varying copulas; tail dependence; CoVaR; oil price; US dollar exchange rate.
    JEL: G1 G15 Q4
    Date: 2019–04
  14. By: Fabio Busetti (Bank of Italy); Michele Caivano (Bank of Italy); Davide Delle Monache (Bank of Italy); Claudia Pacella (Bank of Italy)
    Abstract: The uncertainty surrounding economic forecasts is generally related to multiple sources of risks, of domestic and foreign origin. This paper studies the predictive distribution of Italian GDP growth as a function of selected risk indicators, related to both financial and real economic developments. The conditional distribution is characterized by means of expectile regressions. Expectiles are closely related to the Expected Shortfall, a well-known measure of risk with desirable properties. Here a decomposition of Expected Shortfall in terms of contributions of different indicators is proposed, which allows to track over time the main drivers of risk. Our analysis of the predictive distribution of GDP confirms that financial conditions are relevant for the left tail of the distribution but it also highlights that indicators of global trade and uncertainty have strong explanatory power for both left and right tail. Their usefulness is supported also in a pseudo real-time predictive context. Overall, our findings suggest that Italian GDP risks have been mostly driven by foreign developments around the Great Recession, by domestic financial conditions at the time of the Sovereign Debt Crisis and by economic policy uncertainty in more recent years.
    Keywords: asymmetric least squares, expectiles, density forecasts, GDP growth, risks
    JEL: C53 E17
    Date: 2020–07
  15. By: Byrne, Joseph P; Ibrahim, Boulis Maher; Zong, Xiaoyu
    Abstract: An asset pricing model using long-run capital share growth risk has recently been found to successfully explain U.S. stock returns. Our paper adopts a recursive preference utility framework to derive an heterogeneous asset pricing model with capital share risks.While modeling capital share risks, we account for the elevated consumption volatility of high income stockholders. Capital risks have strong volatility effects in our recursive asset pricing model. Empirical evidence is presented in which capital share growth is also a source of risk for stock return volatility. We uncover contrasting unconditional and conditional asset pricing evidence for capital share risks.
    Keywords: Asset Pricing, Capital Share, Recursive Preference, Consumption Growth, Bayesian Methods.
    JEL: C21 C30 E25 G11 G12
    Date: 2020–05–12
  16. By: Friberg, Richard
    Abstract: This paper develops a framework using Monte Carlo simulation to examine risk/return properties of intra-industry product portfolio composition and diversification. We use product-level data covering all Swedish sales of alcoholic beverages to describe the risk profiles of wholesalers and how they are affected by actual and hypothetical changes to product portfolios. Using a large number of counterfactual portfolios we quantify the diversification benefits of different product portfolio compositions. In this market the most important reductions in variability come from focusing on domestic products and from focusing on product categories that have low variability. The number of products also has a large effect in the simulations, moving from a portfolio of 10 products to one of 20 products cuts standard deviation of cash flows in relation to mean cash flows by more than half. The concentration of import origins plays a minor quantitative role on risk/return profiles in this market.
    Keywords: Diversification; Enterprise risk management; Monte Carlo; Product portfolios; Risk-return relation
    Date: 2019–11
  17. By: McGowan, Danny; Nguyen, Huyen
    Abstract: We evaluate lenders' incentives to mitigate credit default risk through pricing or securitisation. Exploiting exogenous variation in credit default risk created by differences in foreclosure law along US state borders, we find that lenders in the mortgage market respond to the law in heterogeneous ways. In the agency market where the GSEs mandate a common interest rate policy, foreclosure law provokes a 4.5% increase in securitisation rates but does not affect interest rates. For nonagency loans where market participants demand risk premium, foreclosure law does not incentivise lenders to transfer the risk through the use of securitisation but causes a 625 basis point increase in interest rates. The results highlight how the GSEs' common interest rate policy inhibits lenders' risk-based pricing incentives, increases the GSEs' debt holdings by $70 billion per annum, and exposes taxpayers to preventable losses in the housing market.
    Keywords: loan pricing,securitisation,credit risk,GSEs
    JEL: G21 G28 K11
    Date: 2020
  18. By: Toshiaki Ogawa (Deputy Director and Economist, Institute for Monetary and Economic Studies, Bank of Japan (E-mail:; Masato Ubukata (Professor, Faculty of Economics, Meiji Gakuin University (E-mail:; Toshiaki Watanabe (Professor, Institute of Economic Research, Hitotsubashi University (E-mail:
    Abstract: We make an empirical analysis of whether and how variance risk premia (VRP) contribute to predicting excess stock returns in the US and Japan. Our new findings to be added to the literature are that (i) the correlation between VRP and future excess returns in the US is insignificant when the risk-free rate is close to zero, and (ii) the correlation in Japan is significantly negative. To explain these findings, we also conduct a preliminary theoretical analysis with a structural model of asset pricing based on two assumptions: the zero lower bound ( ZLB) for the risk-free rate, and a negative correlation between the consumption growth rate and the volatility-of-volatility. These allow excess returns to follow a hump-shaped pattern. This affects the sign and significance of the correlation of the returns with the VRP.
    Keywords: Excess returns, Heterogeneous autoregressive model, Nikkei 225, Realized volatility, S&P500, Variance risk premium, Zero lower bound
    JEL: C52 C53 G17
    Date: 2020–07
  19. By: Elliott, M.; Georg, C-P.; Hazell, J.
    Abstract: Banks face different but potentially correlated risks from outside the financial system. Financial connections can share these risks, but also create the means by which shocks can propagate. We examine this tradeoff in the context of a new stylised fact we present: German banks are more likely to have financial connections when they face more similar risks—potentially undermining the risk sharing role of financial connections and contributing to systemic risk. We find that such patterns are socially suboptimal, but can be explained by risk-shifting. Risk-shifting motivates banks to correlate their failures with their counterparties, even though it creates systemic risk.
    Keywords: financial networks, asset correlation, contagion
    JEL: G21 G11 D85
    Date: 2020–07–20
  20. By: Thomas Mayrhofer (Stralsund University of Applied Sciences, Harvard Medical School); Hendrik Schmitz (Paderborn University, RWI Essen, Leibniz Science Campus Ruhr)
    Abstract: Theoretical papers show that optimal prevention decisions in the sense of self-protection (i.e., primary prevention) depend not only on the level of (second-order) risk aversion but also on higher-order risk preferences such as prudence (third-order risk aversion). We study empirically whether these theoretical results hold and whether prudent individuals show less preventive (self-protection) effort than non-prudent individuals. We use a unique dataset that combines data on higher-order risk preferences and various measures of observed real-world prevention behavior. We find that prudent individuals indeed invest less in self-protection as measured by influenza vaccination. This result is driven by high risk individuals such as individuals >60 years of age or chronically ill. We do not find a clear empirical relationship between risk-preferences and prevention in the sense of self-insurance (i.e. secondary prevention). Neither risk aversion nor prudence is related to cancer screenings such as mammograms, Pap smears or X-rays of the lung.
    Keywords: prudence, risk preferences, prevention, vaccination, screening
    JEL: D12 D81 I12
    Date: 2020–08
  21. By: Rui Zhou; Daniel P. Palomar
    Abstract: The first moment and second central moments of the portfolio return, a.k.a. mean and variance, have been widely employed to assess the expected profit and risk of the portfolio. Investors pursue higher mean and lower variance when designing the portfolios. The two moments can well describe the distribution of the portfolio return when it follows the Gaussian distribution. However, the real world distribution of assets return is usually asymmetric and heavy-tailed, which is far from being a Gaussian distribution. The asymmetry and the heavy-tailedness are characterized by the third and fourth central moments, i.e., skewness and kurtosis, respectively. Higher skewness and lower kurtosis are preferred to reduce the probability of extreme losses. However, incorporating high-order moments in the portfolio design is very difficult due to their non-convexity and rapidly increasing computational cost with the dimension. In this paper, we propose a very efficient and convergence-provable algorithm framework based on the successive convex approximation (SCA) algorithm to solve high-order portfolios. The efficiency of the proposed algorithm framework is demonstrated by the numerical experiments.
    Date: 2020–08
  22. By: Olivier Mesly; David W. Shanafelt; Nicolas Huck
    Abstract: We revisit the theory of financial crises using a predator-prey metaphor, highlighting the relationship between greed, risk aversion and debt accumulation and aggregating concepts from economics, finance and psychology. We argue that regulations that are implemented inefficiently, with weak enforcement or at the wrong time can have deleterious effects on the market, worsening the ailment they initially intended to correct and leaving a spray of prey in their wake. To illustrate our hypothesis, we examine the role of regulations in the years leading up to and during the Global Financial Crisis (GFC) in the U.S., when the Federal Reserve tried to restrain the over-heated housing market fuelled by the predatory mortgage frenzy and the increased use of securitization in risk-hiding financial tools such as Collateralized Debt Obligations (CDOs). Our results indicate that deleterious government interventions may act as a chemotherapy of sorts, causing harm followed by a slow recovery. This understanding can help governments draft better regulations to lower market frictions and better protect investors.
    Keywords: risk aversion; predation; regulations; contagion; debt trap.
    JEL: G01 G18 G21 H31 H63
    Date: 2020
  23. By: José Garrido; Wolfgang Bergthaler; Chanda M DeLong; Juliet Johnson; Amira Rasekh; Anjum Rosha; Natalia Stetsenko
    Abstract: To date, the use of empirical data in insolvency law analysis has been sporadic. This paper provides a conceptual framework for the use of data to assess the effectiveness and efficiency of insolvency systems. The paper analyzes the existing sources of data on insolvency proceedings, including general insolvency statistics, judicial statistics, statistics of insolvency regulators and other sources, and advocates for the design of special data collection mechanisms and statistics to conduct detailed assessments of insolvency systems and to assist in the design of legal reforms.
    Keywords: Economic growth;Social security;Bank credit;Risk management;Interest costs;Bankruptcy, Insolvency, Statistics, Assessment of Laws,Law Design,insolvency,recovery rate,secured creditor,data collection system,statistical report
    Date: 2019–02–04
  24. By: Nicholas Moehle; Mykel J. Kochenderfer; Stephen Boyd; Andrew Ang
    Abstract: We describe an optimization-based tax-aware portfolio construction method that adds tax liability to a standard Markowitz-based portfolio construction approach that models expected return, risk, and transaction costs. Our method produces a trade list that specifies the number of shares to buy of each asset and the number of shares to sell from each tax lot held. To avoid wash sales (in which some realized capital losses are disallowed), we assume that we trade monthly, and cannot simultaneously buy and sell the same asset. The tax-aware portfolio construction problem is not convex, but it becomes convex when we specify, for each asset, whether we buy or sell it. It can be solved using standard mixed-integer convex optimization methods at the cost of very long solve times for some problem instances. We present a custom convex relaxation of the problem that borrows curvature from the risk model. This relaxation can provide a good approximation of the true tax liability, while greatly enhancing computational tractability. This method requires the solution of only two convex optimization problems: the first determines whether we buy or sell each asset, and the second generates the final trade list. This method is therefore extremely fast even in the worst case. In our numerical experiments, which are based on a realistic tax-loss harvesting scenario, our method almost always solves the nonconvex problem to optimality, and when in does not, it produces a trade list very close to optimal. Backtests show that the performance of our method is indistinguishable from that obtained using a globally optimal solution, but with significantly reduced computational effort.
    Date: 2020–08
  25. By: Marta Kłosok (Faculty of Economic Sciences, University of Warsaw); Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw)
    Abstract: recent years many scientific journals have widely explored the topic of machine learning interpretability. It is important as application of Artificial Intelligence is growing rapidly and its excellent performance is of huge potential for many. There is also need for overcoming the barriers faced by analysts implementing intelligent systems. The biggest one relates to the problem of explaining why the model made a certain prediction. This work brings the topic of methods for understanding a black-box from both the global and local perspective. Numerous agnostic methods aimed at interpreting black-box model behavior and predictions generated by these complex structures are analyzed. Among them are: Permutation Feature Importance, Partial Dependence Plot, Individual Conditional Expectation Curve, Accumulated Local Effects, techniques approximating predictions of the black-box for single observations with surrogate models (interpretable white-boxes) and Shapley values framework. Our prospect leads toward the question to what extent presented tools enhance model transparency. All of the frameworks are examined in practice with a credit default data use case. The overview presented prove that each of the method has some limitations, but overall almost all summarized techniques produce reliable explanations and contribute to higher transparency accountability of decision systems.
    Keywords: machine learning, explainable Artificial Intelligence, visualization techniques, model interpretation, variable importance
    JEL: C25
    Date: 2020

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