nep-rmg New Economics Papers
on Risk Management
Issue of 2011‒11‒07
five papers chosen by
Stan Miles
Thompson Rivers University

  1. Financial Network Systemic Risk Contributions By Nikolaus Hautsch; Julia Schaumburg; Melanie Schienle
  2. Market- and Book-Based Models of Probability of Default for Developing Macroprudential Policy Tools By Xisong Jin; Francisco Nadal de Simone
  3. Counterparty credit risk management in industrial corporates By Langkamp, Christian
  4. A Market-based Approach to Sector Risk Determinants and Transmission in the Euro Area By Martín Saldías
  5. Fat Tails Quantified and Resolved: A New Distribution to Reveal and Characterize the Risk and Opportunity Inherent in Leptokurtic Data By Lawrence R. Thorne

  1. By: Nikolaus Hautsch; Julia Schaumburg; Melanie Schienle
    Abstract: We propose the systemic risk beta as a measure for financial companies’ contribution to systemic risk given network interdependence between firms’ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the systemic risk beta as the time-varying marginal effect of a firm’s Value-at-risk (VaR) on the system’s VaR. Suitable statistical inference reveals a multitude of relevant risk spillover channels and determines companies’ systemic importance in the U.S. financial system. Our approach can be used to monitor companies’ systemic importance allowing for a transparent macroprudential regulation.
    Keywords: Systemic risk contribution, systemic risk network, Value at Risk, network topology, two-step quantile regression, time-varying parameters
    JEL: G18 G32 G38 C21 C51 C63
    Date: 2011–10
  2. By: Xisong Jin; Francisco Nadal de Simone
    Abstract: The recent financial crisis raised awareness of the need for a framework for conducting macroprudential policy. Identifying as early as possible and addressing the buildup of endogenous imbalances, exogenous shocks, and contagion from financial markets, market infrastructures, and financial institutions are key elements of a sound macroprudential framework. This paper contributes to this literature by estimating several models of default probability, two of which relax two key assumptions of the Merton model: the assumption of constant asset volatility and the assumption of a single debt maturity. The study uses market and banks? balance sheet data. It finds that systemic risk in Luxembourg banks, while mildly correlated with that of European banking groups, did not increase as dramatically as it did for the European banking groups during the heights of the financial crisis. In addition, it finds that systemic risk has declined during the second half of 2010, both for the banking groups as well as for the Luxembourg banks. Finally, this study illustrates how models of default probability can be used for event-study purposes, for simulation exercises, and for ranking default probabilities during a period of distress according to banks? business lines. As such, this study is a stepping stone toward developing an operational framework to produce quantitative judgments on systemic risk and financial stability in Luxembourg.
    Keywords: financial stability; credit risk; structured products; default probability, GARCH
    JEL: C30 E44 G1
    Date: 2011–10
  3. By: Langkamp, Christian
    Abstract: Ever since the financial crisis of the banking system of 2008 - 2010 the paradigm that deposits or other exposures towards major banks are safe has been fundamentally questioned. This put industrial corporates, who to support their business usually need to manage significant cash holdings or incur counterparty credit risk via derivatives, in the situation to develop or extend their resources for counterparty credit risk management. This paper provides a comprehensive overview over the practical issues into the subject benefitting largely from the findings of an interview series conducted with the respective heads of counterparty and customer credit risk management in the time period April - September 2011 of 25 large european enterprises with a large subset being members of the German DAX Index.
    Keywords: Financial Risk Management; Credit Risk; Counterparty Credit Risk; CCR Management; Organisation; Financial Controlling; Financial Institutions; Banks
    JEL: G32 G24 G21 L60
    Date: 2011–10–27
  4. By: Martín Saldías
    Abstract: In a panel data framework applied to Portfolio Distance-to-Default series of corporate sectors in the euro area, this paper evaluates systemic and idiosyncratic determinants of default risk and examines how distress is transferred in and between the financial and corporate sectors since the early days of the euro. This approach takes into account observed and unobserved common factors and the presence of different degrees of cross-section dependence in the form of economic proximity. This paper contributes to the financial stability literature with a contingent claims approach to a sector-based analysis with a less dominant macro focus while being compatible with existing stress-testing methodologies in the literature. A disaggregated analysis of the different corporate and financial sectors allows for a more detailed assessment of specificities in terms of risk pro le, i.e. heterogeneity of business models, risk exposures and interaction with the rest of the macro environment.
    JEL: G13 C31 C33
    Date: 2011
  5. By: Lawrence R. Thorne
    Abstract: I report a new statistical distribution formulated to confront the infamous, long-standing, computational/modeling challenge presented by highly skewed and/or leptokurtic ("fat- or heavy-tailed") data. The distribution is straightforward, flexible and effective. Even when working with far fewer data points than are routinely required, it models non-Gaussian data samples, from peak center through far tails, within the context of a single probability density function (PDF) that is valid over an extremely broad range of dispersions and probability densities. The distribution is a precision tool to characterize the great risk and the great opportunity inherent in fat-tailed data.
    Date: 2011–10

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