nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒02‒22
ten papers chosen by
Stan Miles
Thompson Rivers University

  1. Sophisticated vs. Simple Systemic Risk Measures By Pankoke, David
  2. Bivariate GARCH models for single asset returns By Tomasz Skoczylas
  3. Bank Capital, Liquid Reserves, and Insolvency Risk By Hugonnier, Julien; Morellec, Erwan
  4. How important are hedge funds in a crisis? By Gropp, Reint
  5. Decision making with Conditional Value-at-Risk and spectral risk measures: The problem of comparative risk aversion By Brandtner, Mario; Kürsten, Wolfgang
  6. Pricing Derivatives in the New Framework: OIS Discounting, CVA, DVA & FVA By García Muñoz, Luis Manuel; de Lope Contreras, Fernando; Palomar Burdeus, Juan Esteban
  7. A Model of Mortgage Losses and its Applications for Macroprudential Instruments By Hott, Christian
  8. Re-Mapping Credit Ratings By Eisl, Alexander; Elendner, Hermann W.; Lingo, Manuel
  9. Nonparametric change-point analysis of volatility By Markus Bibinger; Moritz Jirak; Mathias Vetter;
  10. Corporate Policies with Temporary and Permanent Shocks By Décamps, Jean-Paul; Gryglewicz, S.; Morellec, E.; Villeneuve, Stéphane

  1. By: Pankoke, David
    Abstract: This paper evaluates whether sophisticated or simple systemic risk measures are more suitable in identifying which institutions contribute to systemic risk. In this investigation, DCoVaR, Marginal Expected Shortfall (MES), SRISK and Granger-Causality Networks are considered as sophisticated systemic risk measures. Market capitalization, total debt, leverage, the stock market returns of an institution, and the correlation between the stock market returns of an institution and the market, are considered as simple systemic risk measures. Systemic relevance is approximated by the receipt of financial support during the financial crisis and the classification, as a systemically important institution, by national or international regulators. The analyses are performed for all companies included in the S&P 500 composite index. The findings suggest that simple systemic risk measures have more explanatory power than sophisticated risk measures. In particular, total debt is found to be the most suitable indicator to detect institutions which contribute to systemic risk, according to the explanatory power and model fit. The most suitable sophisticated risk measure seems to be SRISK.
    Keywords: Systemic Risk, DCoVaR, Marginal Expected Shortfall, SRISK, Granger-Causality Networks
    Date: 2014–12
  2. By: Tomasz Skoczylas (Faculty of Economic Sciences, University of Warsaw)
    Abstract: In this paper an alternative approach to modelling and forecasting single asset returns volatility is presented. A new, bivariate, flexible framework, which may be considered as a development of single-equation ARCH-type models, is proposed. This approach focuses on joint distribution of returns and observed volatility, measured by Garman-Klass variance estimator, and it enables to examine simultaneous dependencies between them. Proposed models are compared with benchmark GARCH and range-based GARCH (RGARCH) models in terms of prediction accuracy. All models are estimated with maximum likelihood method, using time series of EUR/PLN spot rate quotations and WIG20 index. Results are very encouraging especially for foreasting Value-at-Risk. Bivariate models achieved lesser rates of VaR exception, as well as lower coverage tests statistics, without being more conservative than its single-equation counterparts, as their forecasts errors measures are rather similar.
    Keywords: bivariate volatility models, joint distribution, range-based volatility estimators, Garman-Klass estimator, observed volatility, volatility modelling, GARCH, leverage, Value-at-Risk, volatility forecasting
    JEL: C13 C32 C53 C58 G10 G17
    Date: 2015
  3. By: Hugonnier, Julien; Morellec, Erwan
    Abstract: We develop a dynamic model to assess the effects of liquidity and leverage requirements on banks' insolvency risk. The model features endogenous capital structure, liquid asset holdings, payout, and default decisions. In the model, banks face taxation, flotation costs of securities, and default costs. They are financed with equity, insured deposits, and risky debt. Using the model, we show that liquidity requirements have no long-run effects on default risk but may increase it in the short-run; leverage requirements reduce default risk but may significantly reduce bank value; mispriced deposit insurance fuels default risk while depositor preference in default decreases it.
    Keywords: banks; capital structure; insolvency risk; liquidity buffers; regulation
    JEL: G21 G28 G32 G33
    Date: 2015–02
  4. By: Gropp, Reint
    Abstract: Before the 2007-09 crisis, standard risk measurement methods substantially underestimated the threat to the financial system. One reason was that these methods didn't account for how closely commercial banks, investment banks, hedge funds, and insurance companies were linked. As financial conditions worsened in one type of institution, the effects spread to others. A new method that more accurately accounts for these spillover effects suggests that hedge funds may have been central in generating systemic risk during the crisis.
    Keywords: systemic risk analysis,statistical risk measurement,spillover effects
    Date: 2014
  5. By: Brandtner, Mario; Kürsten, Wolfgang
    Abstract: We analyze spectral risk measures with respect to comparative risk aversion following Arrow (1965) and Pratt (1964) on the one hand, and Ross (1981) on the other hand. The implications for two standard financial decision problems, namely the willingness to pay for insurance and portfolio selection, are studied. Within the framework of Arrow and Pratt, we show that the widely-applied spectral Arrow-Pratt-measure is not a consistent measure of Arrow-Pratt-risk aversion. A decision maker with a greater spectral Arrow-Pratt-measure may only be willing to pay less for insurance or to invest more in the risky asset than a decision maker with a smaller spectral Arrow-Pratt-measure. We further show how a proper measure of Arrow-Pratt-risk aversion should look like instead. Within the framework of Ross, we show that the popular subclasses of Conditional Value-at-Risk, and exponential and power spectral risk measures cannot be completely ordered with respect to Ross-risk aversion. As a consequence, these subclasses also exhibit counter-intuitive comparative static results. In the insurance problem, the willingness to pay for insurance may be decreasing with increasing risk parameter. In the portfolio selection problem, the investment in the risky asset may be increasing with increasing risk parameter. These shortcomings have to be considered before spectral risk measures can be applied for the purpose of optimal decision making and regulatory issues.
    JEL: D81 G11 G21
    Date: 2014
  6. By: García Muñoz, Luis Manuel; de Lope Contreras, Fernando; Palomar Burdeus, Juan Esteban
    Abstract: As a byproduct of the 2007-2008 credit crunch, derivatives pricing and risk management are experiencing a dramatic transformation. Assumptions that were widely accepted not long ago, like absence of counterparty credit risk and the existence of a unique risk free curve available for every derivatives hedger in the derivatives replication process, are no longer accepted. Financial institutions are changing the way in which counterparty credit risk and funding risk are managed. We find ourselves in a world with multiple discounting curves for any given currency and with different adjustments to apply to the price of financial derivatives that seem difficult to hedge. The target of this book is to make a deep review of how these effects impact the derivatives valuation theory.
    Keywords: Derivatives pricing, Collateral, OIS Discounting, CVA, DVA, FVA, Counterparty Credit Risk, Funding Risk
    JEL: G10 G12 G13
    Date: 2015–02–01
  7. By: Hott, Christian
    Abstract: We develop a theoretical model of mortgage loss rates that evaluates their main underlying risk factors. Following the model, loss rates are positively influenced by the house price level, the loan-to-value of mortgages, interest rates, and the unemployment rate. They are negatively influenced by the growth of house prices and the income level. The calibration of the model for the US and Switzerland demonstrates that it is able to describe the overall development of actual mortgage loss rates. In addition, we show potential applications of the model for different macroprudential instruments: stress tests, countercyclical buffer, and setting risk weights for mortgages with different loan-to-value and loan-to-income ratios.
    JEL: E51 G21 G28
    Date: 2014
  8. By: Eisl, Alexander; Elendner, Hermann W.; Lingo, Manuel
    Abstract: Rating agencies report ordinal ratings in discrete classes. We question the market’s implicit assumption that agencies define their classes on identical scales, e.g., that AAA by Standard & Poor’s is equivalent to Aaa by Moody’s. To this end, we develop a non-parametric method to estimate the relation between rating scales for pairs of raters. For every rating class of one rater this, scale relation identifies the extent to which it corresponds to any rating class of another rater, and hence enables a rating-class specific re-mapping of one agency’s ratings to another’s. Our method is based purely on ordinal co-ratings to obviate error-prone estimation of default probabilities and the disputable assumptions involved in treating ratings as metric data. It estimates all rating classes’ relations from a pair of raters jointly, and thus exploits the information content from ordinality. We find evidence against the presumption of identical scales for the three major rating agencies Fitch, Moody’s and Standard & Poor’s, provide the relations of their rating classes and illustrate the importance of correcting for scale relations in benchmarking.
    Keywords: credit rating; rating agencies; rating scales; comparison of ratings
    JEL: C14 G24
    Date: 2015
  9. By: Markus Bibinger; Moritz Jirak; Mathias Vetter;
    Abstract: This work develops change-point methods for statistics of high-frequency data. The main interest is the volatility of an Itˆo semi-martingale, which is discretely observed over a fixed time horizon. We construct a minimax-optimal test to discriminate different smoothness classes of the underlying stochastic volatility process. In a high-frequency framework we prove weak convergence of the test statistic under the hypothesis to an extreme value distribution. As a key example, under extremely mild smoothness assumptions on the stochastic volatility we thereby derive a consistent test for volatility jumps. A simulation study demonstrates the practical value in finite-sample applications.
    Keywords: high-frequency data, nonparametric change-point test, minimax-optimal test, stochastic volatility, volatility jumps
    JEL: C12 C14
    Date: 2015–02
  10. By: Décamps, Jean-Paul; Gryglewicz, S.; Morellec, E.; Villeneuve, Stéphane
    Abstract: We develop a dynamic model of investment, cash holdings, financing, and risk management policies in which firms face financing frictions and are subject to permanent and temporary cash ow shocks. In this model, target cash holdings depend on the long-term prospects of the firm, implying that the payout policy of the firm, its financing policy, and its cashow sensitivity of cash display a more realistic behavior than in prior models with financing frictions. In addition, risk management policies are richer and depend on the nature of cash ow shocks and potential collateral constraints. Lastly, the timing of investment and the firms initial asset mix both reect financing frictions and the joint effects of permanent and temporary shocks.
    Keywords: Corporate policies; permanent vs. temporary shocks; financing frictions.
    Date: 2015–01

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