nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒04‒04
thirteen papers chosen by

  1. Probability equivalent level of Value at Risk and higher-order Expected Shortfalls By Matyas Barczy; Fanni K. Ned\'enyi; L\'aszl\'o S\"ut\H{o}
  2. Life Insurance, Liquidity Risk, Interest Rates, Fire Sales, Systemic Risk By Christian Kubitza; Nicolaus Grochola; Helmut Gründl
  3. Risk Transmission between Green Markets and Commodities By Naeem, Muhammad Abubakr; Karim, Sitara; Jamasb, Tooraj; Nepal, Rabindra
  4. Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning By Giuseppe Cascarino; Mirko Moscatelli; Fabio Parlapiano
  5. Volatility forecasting with machine learning and intraday commonality By Chao Zhang; Yihuang Zhang; Mihai Cucuringu; Zhongmin Qian
  6. Two is better than one: Regularized shrinkage of large minimum variance portfolio By Taras Bodnar; Nestor Parolya; Erik Thors\'en
  7. Legal Weakness, Investment Risks, and Distressed Acquisitions: Evidence from Russian Regions By Adachi, Yuko; Iwasaki, Ichiro
  8. Minimax Risk in Estimating Kink Threshold and Testing By Javier Hidalgo; Heejun Lee; Heejun Lee; Jungyoon Lee; Myung Hwan Seo
  9. Compliance Risk Management: Developing Compliance Improvement Plans By Mr. John D Brondolo; Annette Chooi; Trevor Schloss; Anthony Siouclis
  10. Robust Extreme Quantile Estimation for Pareto-Type tails through an Exponential Regression Model By Minkah, Richard; de Wet, Tertius; Ghosh, Abhik
  12. Building more resilient food systems: Lessons and policy recommendations from the COVID-19 pandemic By McDermott, John; Allison-Reumann, Laura
  13. Cooling the Mortgage Loan Market: The Effect of Recommended Borrower-Based Limits on New Mortgage Lending By Martin Hodula; Milan Szabo; Lukas Pfeifer; Martin Melecky

  1. By: Matyas Barczy; Fanni K. Ned\'enyi; L\'aszl\'o S\"ut\H{o}
    Abstract: We investigate the probability equivalent level of Value at Risk and $n^{\mathrm{th}}$-order Expected Shortfall (called PELVE_n), which can be considered as a variant of the notion of the probability equivalent level of Value at Risk and Expected Shortfall (called PELVE) due to Li and Wang (2019). We study the finiteness, uniqueness and several properties of PELVE_n, we calculate PELVE_2 of some notable distributions, PELVE_2 of a random variable having generalized Pareto excess distribution, and we describe the asymptotic behaviour of PELVE_2 of regularly varying distributions as the level tends to $0$. Some properties of $n^{\mathrm{th}}$-order Expected Shortfall are also investigated. Among others, it turns out that a Gini Shortfall is a linear combination of Expected Shortfall and $2^{\mathrm{nd}}$-order Expected Shortfall.
    Date: 2022–02
  2. By: Christian Kubitza (University of Bonn, Institute of Finance and Statistics, Adenauerallee 24-42, 53113 Bonn, Germany); Nicolaus Grochola (Goethe University Frankfurt, International Center for Insurance Regulation, Germany.); Helmut Gründl (Goethe University Frankfurt, International Center for Insurance Regulation, Germany.)
    Abstract: Life insurers sell savings contracts with surrender options, allowing policyholders to prematurely withdraw guaranteed surrender values. Surrender options move toward the money when interest rates rise. Hence, higher interest rates raise surrender rates, as we document for the German life insurance sector. Using a calibrated model, we estimate that surrender options would force insurers to sell up to 2% of their investments during an enduring interest rate rise of 25 bps per annum. The resulting price impact depends on insurers' investment behavior. Forced asset sales are amplified by insurers' long-term investments but mitigated by reducing the guarantees on surrender values.
    Keywords: Life Insurance, Liquidity Risk, Interest Rates, Fire Sales, Systemic Risk
    JEL: G22 E52 G32 G28
    Date: 2022–03
  3. By: Naeem, Muhammad Abubakr (Accounting and Finance Department, United Arab Emirates University, United Arab Emirates; South Ural State University, Russian Federation); Karim, Sitara (Department of Business Administration, Faculty of Management Sciences, ILMA University, Karachi, Pakistan); Jamasb, Tooraj (Department of Economics, Copenhagen Business School); Nepal, Rabindra (School of Business, Faculty of Business and Law, University of Wollongong, Australia)
    Abstract: The current study examines the risk transmission between green markets and commodities spanning 3 January 2011 to 20 June 2021. We use two novel methodologies of volatility transmission using dynamic conditional correlation (DCC-GARCH) and the other time-varying parameters vector autoregression (TVP-VAR) technique of connectedness. We found parallel results of risk transmission between green markets and commodities using these measures of connectedness. Results demonstrate that green markets and commodities form a weakly knitted sphere of connectedness where intra-group clustering dominates the inter-group connectedness. Clean energy markets and precious metals form two distinct groups of connectedness for respective markets. However, crude oil, natural gas and wheat remained indifferent to the shocks highlighting their potential to serve as diversifiers due to their low risk bearing features. Further, time-varying dynamics emphasize the occurrence of sizable events that disrupted the operations of green and commodity markets, accentuating the attention of investors, portfolio managers, and financial market participants. Intense spillovers shaped the overall connectedness of the network where green markets (commodities) are fashioned in positive (negative) risk spillovers. Finally, we propose recommendations for policymakers, regulators, investors, portfolio managers, and market participants to devise policies and investment goals to shield their investments from unexpected circumstances.
    Keywords: Green markets; Commodities; DCC-GARCH; TVP-VAR; Volatility transmission
    JEL: G10 G11 G19 Q01
    Date: 2022–02–24
  4. By: Giuseppe Cascarino (Bank of Italy); Mirko Moscatelli (Bank of Italy); Fabio Parlapiano (Bank of Italy)
    Abstract: Forecasting models based on machine learning (ML) algorithms have been shown to outperform traditional models in several applications. The lack of an easily interpretable functional form, however, is a major challenge for their adoption, especially when a knowledge of the estimated relationships and an explanation of individual forecasts are needed, for instance due to regulatory requirements or when forecasts are used in policy making. We apply some of the most established methods from the eXplainable Artificial Intelligence (XAI) literature to shed light on the random forest corporate default forecasting model in Moscatelli et al. (2019) applied to Italian non-financial firms. The methods provide insight into the relative importance of financial and credit variables to predict firms’ financial distress. We complement the analysis by showing how the importance of these variables in explaining default risk changes over time in the period 2009-19. When financial conditions deteriorate, the variables characterized by a more complex relationship with financial distress, such as firms’ liquidity and indebtedness indicators, become more important in predicting borrowers’ defaults. We also discuss how ML models could enhance the accuracy of credit assessment for those borrowers with less developed credit relationships such as smaller firms
    Keywords: explainable artificial intelligence, model-agnostic explainability, artificial intelligence, machine learning, credit scoring, fintech
    JEL: G2 C52 C55 D83
    Date: 2022–03
  5. By: Chao Zhang; Yihuang Zhang; Mihai Cucuringu; Zhongmin Qian
    Abstract: We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting one-day-ahead RVs using past intraday RVs as predictors, and highlight interesting diurnal effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs.
    Date: 2022–02
  6. By: Taras Bodnar; Nestor Parolya; Erik Thors\'en
    Abstract: In this paper we construct a shrinkage estimator of the global minimum variance (GMV) portfolio by a combination of two techniques: Tikhonov regularization and direct shrinkage of portfolio weights. More specifically, we employ a double shrinkage approach, where the covariance matrix and portfolio weights are shrunk simultaneously. The ridge parameter controls the stability of the covariance matrix, while the portfolio shrinkage intensity shrinks the regularized portfolio weights to a predefined target. Both parameters simultaneously minimize with probability one the out-of-sample variance as the number of assets $p$ and the sample size $n$ tend to infinity, while their ratio $p/n$ tends to a constant $c>0$. This method can also be seen as the optimal combination of the well-established linear shrinkage approach of Ledoit and Wolf (2004, JMVA) and the shrinkage of the portfolio weights by Bodnar et al. (2018, EJOR). No specific distribution is assumed for the asset returns except of the assumption of finite $4+\varepsilon$ moments. The performance of the double shrinkage estimator is investigated via extensive simulation and empirical studies. The suggested method significantly outperforms its predecessor (without regularization) and the nonlinear shrinkage approach in terms of the out-of-sample variance, Sharpe ratio and other empirical measures in the majority of scenarios. Moreover, it obeys the most stable portfolio weights with uniformly smallest turnover.
    Date: 2022–02
  7. By: Adachi, Yuko; Iwasaki, Ichiro
    Abstract: This paper traces the survival status of 93,260 Russian business firms in the period of 2007–2019 and empirically examines the determinants of the acquisition of financially distressed companies (i.e., distressed acquisitions). We found that, of 93,260 firms, 50,743 failed in management, and among these distressed firms, 10,110 were rescued by acquisition during the observation period. Our empirical results indicate that, in Russian regions, the weakness of the legal system tends to increase the probability of distressed acquisitions, while other socioeconomic risks negatively affect it. These tendencies are common in most industries and regions. It is also revealed that, in the most developed area, monotown enterprises are more likely to be bailed out by acquisition after management failure than other firms, but it is not always true for the whole nation.
    Keywords: legal weakness, investment risk, financial distress, distressed acquisitions, Russia
    JEL: C35 D02 D22 E02 G34 K20 L22
    Date: 2022–03
  8. By: Javier Hidalgo; Heejun Lee; Heejun Lee; Jungyoon Lee; Myung Hwan Seo
    Abstract: We derive a risk lower bound in estimating the threshold parameter without knowing whether the threshold regression model is continuous or not. The bound goes to zero as the sample size n grows only at the cube root rate. Motivated by this nding, we develop a continuity test for the threshold regression model and a bootstrap to compute its p-values. The validity of the bootstrap is established, and its nite sample property is explored through Monte Carlo simulations.
    Keywords: Continuity Test, Kink, Risk lower bound, Unknown Threshold
    Date: 2021–09
  9. By: Mr. John D Brondolo; Annette Chooi; Trevor Schloss; Anthony Siouclis
    Abstract: All tax administrations seek to maximize the overall level of compliance with tax laws. Compliance improvement plans (CIPs) are a valuable tool for increasing taxpayers’ compliance and boosting tax revenue. This note is intended to help tax administrations develop a CIP, by providing guidance on the following issues: (1) how to identify and rate compliance risks; (2) how to treat risks to achieve the best possible outcome; and (3) how to measure the impacts that treatments have had on compliance outcomes.
    Keywords: Taxes, tax compliance, revenue, revenue administration, risk management
    Date: 2022–03–18
  10. By: Minkah, Richard; de Wet, Tertius; Ghosh, Abhik
    Abstract: The estimation of extreme quantiles is one of the main objectives of statistics of extremes ( which deals with the estimation of rare events). In this paper, a robust estimator of extreme quantile of a heavy-tailed distribution is considered. The estimator is obtained through the minimum density power divergence criterion on an exponential regression model. The proposed estimator was compared with two estimators of extreme quantiles in the literature in a simulation study. The results show that the proposed estimator is stable to the choice of the number of top order statistics and show lesser bias and mean square error. Practical application of the proposed estimator is illustrated with data from pedochemical and insurance industries.
    Date: 2022–03–25
  11. By: 子, 鬼谷
    Abstract: This paper aims at answering the question whether the VN30 index futures introduction has an impact on stock market volatility in Vietnam. Apply GARCH model of volatility with additive dummy variable from 28/7/2000 to 10/9/2020, the result shows that when the first listed index futures contract appears, it makes the volatility of VNIndex increases. The result is still robust after excluding the turmoil period of Vietnam stock market. This paper implies that policy maker should be more careful in promoting derivatives market in Vietnam.
    Date: 2020–11–04
  12. By: McDermott, John; Allison-Reumann, Laura
    Abstract: Two years in, the long-term health and economic effects of the COVID-19 pandemic continue to influence poverty, food systems, and food security. Drawing on CGIAR research on the COVID-19 pandemic thus far, this brief presents key lessons learned and policy recommendations to inform decision-making processes around managing risks, addressing structural vulnerabilities, and building resilient and sustainable food systems.
    Keywords: WORLD; resilience; food systems; policies; Coronavirus; coronavirus disease Coronavirinae; COVID-19; health; economic impact; poverty; food security; sustainability; decision making; risk management
    Date: 2022
  13. By: Martin Hodula; Milan Szabo; Lukas Pfeifer; Martin Melecky
    Abstract: This paper studies the effects of regulatory recommendations concerning maximum (i) loan-to-value (LTV), (ii) debt-to-income (DTI) and (iii) debt service-to-income ratios (DSTI) on new loans secured by residential property. It uses loan-level regulatory survey data on about 82,000 newly granted residential mortgage loans in the Czech Republic from 2016 to 2019 to estimate the average effects of the Czech National Bank's regulatory recommendations and their heterogeneous effects depending on borrower, loan, bank and regional characteristics. The studied response variables include the mortgage loan size and lending rate and the value of the property with which loans are secured. The machine learning method of causal forests is employed to estimate the effects of interest and to identify any heterogeneity and its likely drivers. We highlight two important facts: (i) value-based (LTV) and income-based (DTI and DSTI) limits have different impacts on the mortgage market and (ii) borrower, loan, bank and regional characteristics play an important role in the transmission of the recommended limits.
    Keywords: Borrower-based measures, causal forests, Czech Republic, macroprudential recommendations, residential mortgage loans
    JEL: E44 G21 G28 G51 R31
    Date: 2022–03

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