nep-rmg New Economics Papers
on Risk Management
Issue of 2021‒12‒06
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Scenario Analysis with the DD-PD Mapping Approach: Stock Market Shocks and U.S. Corporate Default Risk By Mr. Jorge A Chan-Lau
  2. Credit Risk Database: Credit Scoring Models for Thai SMEs By Bhumjai Tangsawasdirat; Suranan Tanpoonkiat; Burasakorn Tangsatchanan
  3. Portfolio analysis with mean-CVaR and mean-CVaR-skewness criteria based on mean-variance mixture models By Nuerxiati Abudurexiti; Kai He; Dongdong Hu; Svetlozar T. Rachev; Hasanjan Sayit; Ruoyu Sun
  4. The Evolving Causal Structure of Equity Risk Factors By Gabriele D'Acunto; Paolo Bajardi; Francesco Bonchi; Gianmarco De Francisci Morales
  5. Impacto del Stress Sistémico en el Crecimiento Económico: Caso Guatemala By Valdivia Coria, Joab Dan; Valdivia Coria, Daney David
  6. Modelling time-varying volatility interactions By Susana Campos-Martins; Cristina Amado
  7. Forecasting the Variability of Stock Index Returns with the Multifractal Random Walk Model for Realized Volatilities By Sattarhoff, Cristina; Lux, Thomas
  8. ESG Screening in the Fixed-Income Universe By Fabio Alessandrini; David Baptista Balula; Eric Jondeau
  9. COVID-19 Containment Measures and Expected Stock Volatility: High-Frequency Evidence from Selected Advanced Economies By Mr. Yunhui Zhao; Yang Liu; Viral V. Acharya
  10. Equity--Linked Life Insurances on Maximum of Several Assets By Battulga Gankhuu
  11. Exponential GARCH-Ito Volatility Models By Donggyu Kim
  12. Global Financial Crisis, Export Credit Insurance, and Scope Adjustment of Multiproduct Exporting Firms By Hea-Jung Hyun; Jung Hur
  13. Predicting Mortality from Credit Reports By Giacomo De Giorgi; Matthew Harding; Gabriel Vasconcelos

  1. By: Mr. Jorge A Chan-Lau
    Abstract: This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms’ DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from quantitative and narrative scenarios informed by expert judgment. At end-2020, risks from stock market corrections in the U.S. are concentrated in the energy, financial and technology sectors, and additional bank capital needs could be large. The paper concludes discussing uses of the mapping beyond PD valuation suitable for capital structure analysis, credit portfolio management, and long-term scenario planning analysis.
    Keywords: probability of default, distance-to-default, default risk, stock markets, quantile regression, scenario analysis, stress test
    Date: 2021–05–20
  2. By: Bhumjai Tangsawasdirat; Suranan Tanpoonkiat; Burasakorn Tangsatchanan
    Abstract: This paper aims to provide an introduction to Credit Risk Database (CRD), a collection of financial and non-financial data for SME credit risk analysis, for Thailand. Aligning with the Bank of Thailand (BOT)’s strategic plan to develop the data ecosystem to help reduce asymmetric information problem in the financial sector, CRD is an initiative to effectively utilize data already collected from financial institutions as a part of the BOT’s supervisory mandate. Our first use case is intended to help improve financial access for SMEs, by building credit risk models that can work as a complementary tool to help financial institutions and Credit Guarantee Corporation assess SMEs financial prospects in parallel with internal credit score. Focusing on SMEs who are new borrowers, we use only SME’s financial and non-financial data as our explanatory variables while disregarding past default-related data such as loan repayment behavior. Credit risk models of various methodologies are then built from CRD data to allow financial institutions to conduct effective risk-based pricing, offering different sets of interest rates and loan terms. Statistical methods (i.e. logit regression and credit scoring) and machine learning methods (i.e. decision tree and random forest) are used to build credit risk models that can help quantify the SME’s one-year forward probability of default. Out-of-sample prediction results indicate that the statistical and machine learning models yield reasonably accurate probability of default predictions, with the maximum Area under the ROC Curve (AUC) at approximately 70-80%. The model with the best performance, as compared by the maximum AUC, is the random forest model. However, the credit scoring model that is developed from logistic regression of weighted-of-evidence variables is more user-friendly for credit loan providers to interpret and develop practical application, achieving the second-best AUC.
    Keywords: Credit Risk Database; Credit Score; Credit Risk Assessment; Credit Scoring Model; Thai SMEs
    JEL: C52 C53 C55 D81 G21 G32
    Date: 2021–11
  3. By: Nuerxiati Abudurexiti; Kai He; Dongdong Hu; Svetlozar T. Rachev; Hasanjan Sayit; Ruoyu Sun
    Abstract: The paper Zhao et al. (2015) shows that mean-CVaR-skewness portfolio optimization prob- lems based on asymetric Laplace (AL) distributions can be transformed into quadratic optimiza- tion problems under which closed form solutions can be found. In this note, we show that such result also holds for mean-risk-skewness portfolio optimization problems when the underlying distribution is a larger class of normal mean-variance mixture (NMVM) models than the class of AL distributions. We then study the value at risk (VaR) and conditional value at risk (CVaR) risk measures on portfolios of returns with NMVM distributions. They have closed form expres- sions for portfolios of normal and more generally elliptically distributed returns as discussed in Rockafellar & Uryasev (2000) and in Landsman & Valdez (2003). When the returns have gen- eral NMVM distributions, these risk measures do not give closed form expressions. In this note, we give approximate closed form expressions for VaR and CVaR of portfolios of returns with NMVM distributions. Numerical tests show that our closed form formulas give accurate values for VaR and CVaR and shortens the computational time for portfolio optimization problems associated with VaR and CVaR considerably.
    Date: 2021–11
  4. By: Gabriele D'Acunto; Paolo Bajardi; Francesco Bonchi; Gianmarco De Francisci Morales
    Abstract: In recent years, multi-factor strategies have gained increasing popularity in the financial industry, as they allow investors to have a better understanding of the risk drivers underlying their portfolios. Moreover, such strategies promise to promote diversification and thus limit losses in times of financial turmoil. However, recent studies have reported a significant level of redundancy between these factors, which might enhance risk contagion among multi-factor portfolios during financial crises. Therefore, it is of fundamental importance to better understand the relationships among factors. Empowered by recent advances in causal structure learning methods, this paper presents a study of the causal structure of financial risk factors and its evolution over time. In particular, the data we analyze covers 11 risk factors concerning the US equity market, spanning a period of 29 years at daily frequency. Our results show a statistically significant sparsifying trend of the underlying causal structure. However, this trend breaks down during periods of financial stress, in which we can observe a densification of the causal network driven by a growth of the out-degree of the market factor node. Finally, we present a comparison with the analysis of factors cross-correlations, which further confirms the importance of causal analysis for gaining deeper insights in the dynamics of the factor system, particularly during economic downturns. Our findings are especially significant from a risk-management perspective. They link the evolution of the causal structure of equity risk factors with market volatility and a worsening macroeconomic environment, and show that, in times of financial crisis, exposure to different factors boils down to exposure to the market risk factor.
    Date: 2021–11
  5. By: Valdivia Coria, Joab Dan; Valdivia Coria, Daney David
    Abstract: Since financial crisis in 2008 and global health crisis COVID-19, systemic risk monitoring has become a relevant variable to anticipate possible credit crunch episodes. In this paper a Composite Indicator of Systemic Stress (CISS) was constructed for Guatemala, throughout recursive Panel Vector Autoregressive Regression (PVAR) adverse effects on the performance of the economy were estimated. Results shows that shocks in systemic risk generate a fall between 0.04pp and 0.05pp on economic growth with different persistence, when the CISS is at low or high stress levels, respectively.
    Keywords: Systemic Risk, Financial Stability, Panel VAR, recursive estimation.
    JEL: C51 E44 G29
    Date: 2021–02
  6. By: Susana Campos-Martins; Cristina Amado
    Abstract: In this paper, we propose an additive time-varying (or partially time-varying) multivariate model of volatility, where a time-dependent component is added to the extended vector GARCH process for modelling the dynamics of volatility interactions. In our framework, co-dependence in volatility is allowed to change smoothly between two extreme states and second-moment interdependence is identified from these crisis-contingent structural changes. The estimation of the new time-varying vector GARCH process is simplified using an equation-by-equation estimator for the volatility equations in the first step, and estimating the correlation matrix in the second step. A new Lagrange multiplier test is derived for testing the null hypothesis of constancy co-dependence volatility against a smoothly time-varying interdependence between financial markets. The test appears to be a useful statistical tool for evaluating the adequacy of GARCH equations by testing the presence of significant changes in cross-market volatility transmissions. Monte Carlo simulation experiments show that the test statistic has satisfactory empirical properties in finite samples. An application to sovereign bond yield returns illustrates the modelling strategy of the new specification.
    Keywords: Multivariate time-varying GARCH; Volatility spillovers; Time-variation; Lagrange multiplier test; Financial market interdependence.
    Date: 2021–09–17
  7. By: Sattarhoff, Cristina; Lux, Thomas
    Abstract: We adapt the multifractal random walk model by Bacry et al. (2001) to realized volatilities (denoted RV-MRW) and take stock of recent theoretical insights on this model in Duchon et al. (2012) to derive forecasts of financial volatility. Moreover, we propose a new extension of the binomial Markov-switching multifractal (BMSM) model by Calvet and Fisher (2001) to the RV framework. We compare the predictive ability of the two against seven classical and multifractal volatility models. Forecasting performance is evaluated out-of-sample based on the empirical MSE and MAE as well as using model confidence sets following the methodology of Hansen et al. (2011). Overall, our empirical study for 14 international stock market indices has a clear message: The RV-MRW is throughout the best model for all forecast horizons under the MAE criterium as well as for large forecast horizons h=50 and 100 days under the MSE criterion. Moreover, the RV-MRW provides most accurate 20-day ahead forecasts in terms of MSE for the great majority of indices, followed by RV-ARFIMA, the latter dominating the competition at the 5-day-horizon. These results are very promising if we consider that this is the first empirical application of the RV-MRW. Moreover, whereas RV-ARFIMA forecasts are often a time consuming task, the RV-MRW stands out due to its fast execution and straightforward implementation. The new RV-BMSM appears to be specialized in short term forecasting, the model providing most accurate one-day ahead forecasts in terms of MSE for the same number of cases as RV-ARFIMA.
    Keywords: Realized volatility,multiplicative volatility models,multifractal random walk,longmemory,international volatility forecasting
    JEL: C20 G12
    Date: 2021
  8. By: Fabio Alessandrini (University of Lausanne; Banque Cantonale Vaudoise); David Baptista Balula (University of Lausanne); Eric Jondeau (University of Lausanne - Faculty of Business and Economics (HEC Lausanne); affiliation not provided to SSRN; Swiss Finance Institute)
    Abstract: This paper evaluates the impact of a screening process based on Environment, Social, and Governance (ESG) scores for an otherwise passive portfolio of investment-grade corporate bonds. The main result is that this filtering leads to a substantial improvement of the targeted ESG score without reducing the risk-adjusted performance but with significant biases in regional, sectoral, and risk factor exposures. We find that screening is very often associated with a substantial improvement in the risk profile. In particular, ESG-tilted portfolios lead to large negative exposure (i.e., protection) to credit risk. Screening based on the Environment score is where most of the reduction in risk takes place, making this criterion particularly relevant in moving the portfolio toward a more defensive composition. We demonstrate that screening at the regional and sectoral levels allows investors to eliminate undesirable regional and sectoral exposures while delivering similar ESG scores and risk-adjusted performances.
    Keywords: Corporate bonds, ESG investing, Portfolio construction, Bond risk factors
    JEL: G11 G24 M14 Q01
    Date: 2021–11
  9. By: Mr. Yunhui Zhao; Yang Liu; Viral V. Acharya
    Abstract: We study the effect of COVID-19 containment measures on expected stock price volatility in some advanced economies, using event studies with hand-collected minute-level data and panel regressions with daily data. We find that six-month-ahead volatility indices dropped following announcements of initial or re-imposed lockdowns, and that they did not drop significantly following the easing of lockdowns. Such patterns are not as strong for three-month-ahead expected volatility and generally absent for one-month-ahead expected volatility. These results provide suggestive evidence for the existence of an intertemporal trade-off: although stringent containment measures cause short-term economic disruptions, they may reduce medium-term uncertainty (reflected in expected stock volatility) by boosting markets’ confidence that the outbreak would be under control more quickly.
    Date: 2021–06–04
  10. By: Battulga Gankhuu
    Abstract: This paper presents pricing and hedging methods for segregated funds and unit-linked life insurance products that are based on a Bayesian Markov--Switching Vector Autoregressive (MS--VAR) process. Here we assumed that a regime-switching process is generated by a homogeneous Markov process. An advantage of our model is it depends on economic variables and is not complicated.
    Date: 2021–11
  11. By: Donggyu Kim
    Abstract: This paper introduces a novel Ito diffusion process to model high-frequency financial data, which can accommodate low-frequency volatility dynamics by embedding the discrete-time non-linear exponential GARCH structure with log-integrated volatility in a continuous instantaneous volatility process. The key feature of the proposed model is that, unlike existing GARCH-Ito models, the instantaneous volatility process has a non-linear structure, which ensures that the log-integrated volatilities have the realized GARCH structure. We call this the exponential realized GARCH-Ito (ERGI) model. Given the auto-regressive structure of the log-integrated volatility, we propose a quasi-likelihood estimation procedure for parameter estimation and establish its asymptotic properties. We conduct a simulation study to check the finite sample performance of the proposed model and an empirical study with 50 assets among the S\&P 500 compositions. The numerical studies show the advantages of the new proposed model.
    Date: 2021–11
  12. By: Hea-Jung Hyun (College of International Studies, Kyung Hee University, Korea); Jung Hur (Department of Economics, Sogang University, Korea)
    Abstract: We examine the role of export credit insurance (ECI) in Korea during two periods of financial crisis by focusing on insured exporting firms' response on the scope of exported products and destination countries. Using unique Korean firm-level data over 1995–2010, our empirical analyses show that during financial crises (e.g., the 1998 Asian financial crisis and the 2008 global financial crisis), multiproduct firms reduced the scope of their export products and the number of destination countries. However, firms with above-median levels of ECI reduced the scope of product and country significantly less than firms with below-median ECI. Core products were less likely to be dropped during the two crisis periods. After the 2008 global financial crisis, countries with high political risk and a low level of financial development were less likely to be dropped from the export market portfolios of firms with high ECI. These findings may imply that larger ECI may be related to higher risk-taking behavior of exporters under asymmetric information.
    Keywords: Product Scope, Country Diversification, Export Credit Insurance, Global Financial Crisis
    JEL: F13 F34 D22
    Date: 2021
  13. By: Giacomo De Giorgi; Matthew Harding; Gabriel Vasconcelos
    Abstract: Data on hundreds of variables related to individual consumer finance behavior (such as credit card and loan activity) is routinely collected in many countries and plays an important role in lending decisions. We postulate that the detailed nature of this data may be used to predict outcomes in seemingly unrelated domains such as individual health. We build a series of machine learning models to demonstrate that credit report data can be used to predict individual mortality. Variable groups related to credit cards and various loans, mostly unsecured loans, are shown to carry significant predictive power. Lags of these variables are also significant thus indicating that dynamics also matters. Improved mortality predictions based on consumer finance data can have important economic implications in insurance markets but may also raise privacy concerns.
    Date: 2021–11

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