nep-rmg New Economics Papers
on Risk Management
Issue of 2013‒03‒30
twelve papers chosen by
Stan Miles
Thompson Rivers University

  1. An Enterprise Risk Management maturity model By Monda, Barbara; Giorgino, Marco
  2. The cross-section of tail risks in stock returns By Moore, Kyle; Sun, Pengfei; de Vries, Casper G.; Zhou, Chen
  3. Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach By Xisong Jin; Francisco Nadal De Simone
  4. Rationales for Corporate Risk Management - A Critical Literature Review By Monda, Barbara; Giorgino, Marco; Modolin, Ileana
  5. "Too big to fail" or "Too non-traditional to fail"?: The determinants of banks' systemic importance By Moore, Kyle; Zhou, Chen
  6. Calibrating Initial Shocks in Bank Stress Test Scenarios: An Outlier Detection Based Approach. By Darne, O.; Levy-Rueff, O.; Pop, A.
  7. Forecasting the Risk of Speculative Assets by Means of Copula Distributions By Benjamin Beckers; Helmut Herwartz; Moritz Seidel
  8. The Market for Reinsurance By M. Martin Boyer; Théodora Dupont-Courtade
  9. The drivers of downside equity tail risk By Moore, Kyle; Sun, Pengei; de Vries, Casper G.; Zhou, Chen
  10. GARCH Models for Daily Stock Returns: Impact of Estimation Frequency on Value-at-Risk and Expected Shortfall Forecasts By David Ardia; Lennart Hoogerheide
  11. Co-dependence of Extreme Events in High Frequency FX Returns By Arnold Polanski; Evarist Stoja
  12. Rules of Thumb for Banking Crises in Emerging Markets By P. Manasse; R. Savona; M. Vezzoli

  1. By: Monda, Barbara; Giorgino, Marco
    Abstract: In the recent years, Enterprise Risk Management (ERM) has emerged as a new risk management technique aimed to manage the portfolio of risks that faces an organization in a integrated, enterprise- wide manner. Unlike traditional risk management, where individual risk categories are managed from a silo-based perspective, ERM involves an holistic view of risks allowing to take into account correlations across all risk classes. The academic literature on ERM is focused on two main aspects: the analysis of the factors that influence ERM adoption and its effects on firms performances. No studies have been conducted yet to propose robust and rigorous models to evaluate the quality, or maturity, of ERM programs implemented by firms. The aim of the research described in this paper is to fill this gap in the literature. In order to build a rigorous ERM maturity model, we have run an e-mail Delphi procedure involving a panel of worldwide experts on ERM and reached their consensus on the selection of a set of ERM best practice parameters, which are used to develop a structured questionnaire to be administered to firms. Experts consensus in obtained also on the scales and the scores for each questionnaire answer option. The output of the Delphi method is a scoring model that can be used to assess the maturity of an ERM program by administering a questionnaire composed of 22 closed-end questions to firms: answers are collected and scored, and all scores are combined in a single final score, the ERM Index (ERMi). The robustness of the model has finally been tested on a small sample of firms. We foresee two different uses of the ERMi maturity model, one by scholars for further quantitative research on ERM topics, and one by practitioners, as ERMi is suitable to be used by firms for a self- assessment of their ERM programs (internal use), and by consultancy firms, auditors and rating agencies (external use). The difference with other existing maturity models is its solid scientific base, the rigour with which it has been designed and the fact that it is derived from a Delphi procedure involving leading ERM experts who reached consensus on the model detailed design.
    Keywords: Enterprise Risk Management, Maturity model, Delphi method
    JEL: G32
    Date: 2013–01
  2. By: Moore, Kyle; Sun, Pengfei; de Vries, Casper G.; Zhou, Chen
    Abstract: This paper investigates how the downside tail risk of stock returns is differentiated cross-sectionally. Stock returns follow heavy-tailed distributions with downside tail risk determined by the tail shape and scale. If safety-first investors are concerned with sufficiently large downside losses, i.e. have a sufficiently low risk tolerance, then in the equilibrium, assets traded in the same market share a homogeneous tail shape parameter. Furthermore, if tail shapes are homogeneous, the equilibrium prices of assets are differentiated by the scales.
    Keywords: Heavy-tail distribution, safety-first utility, asset pricing
    JEL: G11 G12
    Date: 2013–02–19
  3. By: Xisong Jin; Francisco Nadal De Simone
    Abstract: This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology of Segoviano, and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks? default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in three forms: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks and; (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust outof-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.
    Keywords: financial stability; procyclicality, macroprudential policy; credit risk; early warning indicators; default probability, non-linearities, generalized dynamic factor model; dynamic copulas; GARCH
    JEL: C30 E44 G1
    Date: 2013–01
  4. By: Monda, Barbara; Giorgino, Marco; Modolin, Ileana
    Abstract: This paper describes theoretical motivations for corporate risk management activities and empirical evidence provided by different scholars on such rationales. These theoretical considerations can be extended also to the new risk management practices such as enterprise risk management. Based on modern financial theory’s assumption that markets are perfectly efficient, organizations should not implement risk management practices since they cannot contribute to add firm value. However, in the presence of market imperfections, risk management, stabilizing firm’s earnings, can benefit companies in the following manners: reducing transaction costs especially the expected costs of bankruptcy, lowering corporate taxes, aligning financing and investment policies and reducing costs associated with agency problems and asymmetric information.
    Keywords: Risk Management, Hedging, Market imperfections
    JEL: G32
    Date: 2013–02
  5. By: Moore, Kyle; Zhou, Chen
    Abstract: This paper empirically analyzes the determinants of banks' systemic importance. In constructing a measure on the systemic importance of financial institutions we find that size is a leading determinant. This confirms the usual "Too big to fail'' argument. Nevertheless, banks with size above a sufficiently high level have equal systemic importance. In addition to size, we find that the extent to which banks engage in non-traditional banking activities is also positively related to banks' systemic importance. Therefore, in addition to ``Too big to fail", systemically important financial institutions can also be identified by a "Too non-traditional to fail" principle.
    Keywords: Too-big-to-fail, systemic importance, systemic risk, non-traditional banking, extreme value theory
    JEL: G01 G2 G28
    Date: 2013–02–23
  6. By: Darne, O.; Levy-Rueff, O.; Pop, A.
    Abstract: We propose a rigorous and flexible methodological framework to select and calibrate initial shocks to be used in bank stress test scenarios based on statistical techniques for detecting outliers in time series of risk factors. Our approach allows us to characterize not only the magnitude, but also the persistence of the initial shock. The stress testing exercises regularly conducted by supervisors distinguish between two types of shocks, transitory and permanent. One of the main advantages of our framework, particularly relevant as regards the calibration of transitory shocks, is that it allows considering various reverting patterns for the stressed variables and informs the choice of the appropriate stress horizon. We illustrate the proposed methodology by implementing outlier detection algorithms to several time series of (macro)economic and financial variables typically used in bank stress testing.
    Keywords: Stress testing; Stress scenarios; Financial crises; Macroprudential regulation.
    JEL: G28 G32 G20 C15
    Date: 2013
  7. By: Benjamin Beckers; Helmut Herwartz; Moritz Seidel
    Abstract: The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of financial returns and port-folio risk. In this paper, we propose an adjustment of GARCH implied conditional value-at-risk and expected shortfall forecasts that exploits the predictive content of uncorrelated, yet dependent model innovations. The adjustment is motivated by non-Gaussian characteristics of model residuals, and is implemented in a semiparametric fashion by means of conditional moments of simulated bivariate standardized copula distributions. We conduct in-sample forecasting comparisons for a set of 18 stock market indices. In total, four competing copula-GARCH models are contrasted against each other on the basis of their one-step ahead forecasting performance. With regard to forecast unbiasedness and precision, especially the Frank-GARCH models provide most conservative risk forecasts and out-perform all rival models.
    Keywords: copula distributions, expected shortfall, GARCH, model selection, non-Gaussian innovations, risk forecasting, value-at-risk
    JEL: C22 C51 C52 C53 G32
    Date: 2013
  8. By: M. Martin Boyer; Théodora Dupont-Courtade
    Abstract: Using a unique proprietary dataset of primary insurers and reinsurers, we analyze the structure of the reinsurance market. The dataset, which spans six years, contains the quotes for different reinsurance layers, for different clients, for different treaties, and for different lines of business. This is the first study that documents the actual structure of the global reinsurance market using actual quotes, and not only the wining quote, for a large number of layers of a large number of reinsurance treaties. <P>
    Keywords: Reinsurance, reinsurance tranches, risk management, market structure,
    JEL: G34 G22
    Date: 2013–03–01
  9. By: Moore, Kyle; Sun, Pengei; de Vries, Casper G.; Zhou, Chen
    Abstract: We analyze the cross-sectional differences in the tail risk of equity returns and identify the drivers of tail risk. We provide two statistical procedures to test the hypothesis of cross-sectional downside tail shape homogeneity. An empirical study of 230 US non-financial firms shows that between 2008 and 2011 the cross-sectional tail shape is homogeneous across equity returns. The heterogeneity in tail risk over this period can be entirely attributed to differences in scale. The differences in scales are driven by the following firm characteristics: market beta, size, book-to-market ratio, leverage and bid-ask spread.
    Keywords: Extreme Value Theory, Hypothesis Testing, Tail Index, Tail Risk
    JEL: C12 G11 G12
    Date: 2013–02–28
  10. By: David Ardia (University Lavalle, Quebec, Canada); Lennart Hoogerheide (VU University Amsterdam)
    Abstract: We analyze the impact of the estimation frequency - updating parameter estimates on a daily, weekly, monthly or quarterly basis - for commonly used GARCH models in a large-scale study, using more than twelve years (2000-2012) of daily returns for constituents of the S&P 500 index. We assess the implication for one-day ahead 95% and 99% Value-at-Risk (VaR) forecasts with the test for correct conditional coverage of Christoffersen (1998) and for Expected Shortfall (ES) forecasts with the block-bootstrap test of ES violations of Jalal and Rockinger (2008). Using the false discovery rate methodology of Storey (2002) to estimate the percentage of stocks for which the model yields correct VaR and ES forecasts, we reach the following conclusions. First, updating the parameter estimates of the GARCH equation on a daily frequency improves only marginally the performance of the model, compared with weekly, monthly or even quarterly updates. The 90% confidence bands overlap, reflecting that the performance is not significantly different. Second, the asymmetric GARCH model with non-parametric kernel density estimate performs well; it yields correct VaR and ES forecasts for an estimated 90% to 95% of the S&P 500 constituents. Third, specifying a Student-<I>t</I> (or Gaussian) innovations' density yields substantially and significantly worse forecasts, especially for ES. In sum, the somewhat more advanced model with infrequently updated parameter estimates yields much better VaR and ES forecasts than simpler models with daily updated parameter estimates.
    Keywords: GARCH; Value-at-Risk; Expected Shortfall; equity; frequency; false discovery rate
    JEL: C12 C22 C58 G17 G32
    Date: 2013–03–21
  11. By: Arnold Polanski (University of East Anglia); Evarist Stoja (University of Bristol)
    Abstract: In this paper, we investigate extreme events in high frequency, multivariate FX returns within a purposely built framework. We generalize univariate tests and concepts to multidimensional settings and employ these novel techniques for parametric and nonparametric analysis. In particular, we investigate and quantify the co-dependence of cross-sectional and intertemporal extreme events. We find evidence of the cubic law of extreme returns, their increasing and asymmetric dependence and of the scaling property of extreme risk in joint symmetric tails.
    Date: 2013–03
  12. By: P. Manasse; R. Savona; M. Vezzoli
    Abstract: This paper employs a recent statistical algorithm (CRAGGING) in order to build an early warning model for banking crises in emerging markets. We perturb our data set many times and create “artificial” samples from which we estimated our model, so that, by construction, it is flexible enough to be applied to new data for out-of-sample prediction. We find that, out of a large number (540) of candidate explanatory variables, from macroeconomic to balance sheet indicators of the countries’ financial sector, we can accurately predict banking crises by just a handful of variables. Using data over the period from 1980 to 2010, the model identifies two basic types of banking crises in emerging markets: a “Latin American type”, resulting from the combination of a (past) credit boom, a flight from domestic assets, and high levels of interest rates on deposits; and an “Asian type”, which is characterized by an investment boom financed by banks’ foreign debt. We compare our model to other models obtained using more traditional techniques, a Stepwise Logit, a Classification Tree, and an “Average” model, and we find that our model strongly dominates the others in terms of out-of-sample predictive power.
    JEL: E44 G01 G21
    Date: 2013–03

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