nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒06‒05
sixteen papers chosen by
Stan Miles
Thompson Rivers University

  1. The role of valuation practices for risk identification By Boholm, Åsa; Corvellec, Hervé
  2. The Imbalance of Supply Risk and Risk Management Activities in Supply Chains: Developing Metrics to Enable Network Analysis in the Context of Supply Chain Risk Management By Pfohl, Hans-Christian; Zuber, Christian; Berbner, Ulrich
  3. The Bank Capital Regulation (BCR) Model By Hyejin Cho
  4. Extreme Risk, excess return and leverage: the LP formula By Olivier Le Marois; Julia Mikhalevsky; Raphaël Douady
  5. Downside Risk Timing by Mutual Funds By Bodnaruk, Andriy; Chokaev, Bekhan; Simonov, Andrei
  6. Mathematical Definition, Mapping, and Detection of (Anti)Fragility By Nassim Nicholas Taleb; Raphaël Douady
  7. Systemic risk of Islamic Banks By Paolo Giudici; Shatha Hashem
  8. The kiss of information theory that captures systemic risk By Peter Martey Addo; Philippe De Peretti; Hayette Gatfaoui; Jakob Runge
  9. The role of investment banking in systemic risk profiles. Evidence from a panel of EU banking sectors By Renata Karkowska
  10. Systemic risk and macro-prudential policies: A credit network-based approach By Catullo, Ermanno; Gallegati, Mauro; Palestrini, Antonio
  11. Ending over-lending: assessing systemic risk with debt to cash flow By Ramsay, Bruce A.; Sarlin, Peter
  12. Optimal Investment to Minimize the Probability of Drawdown By Bahman Angoshtari; Erhan Bayraktar; Virginia R. Young
  13. The risk management approach to monetary policy, nonlinearity and aggressiveness: the case of the US Fed By Moccero, Diego; Gnabo, Jean-Yves
  14. Portfolio Optimization within Mixture of Distributions By Rania Hentati Kaffel; Jean-Luc Prigent
  15. A Practical Approach to Financial Crisis Indicators Based on Random Matrices By Antoine Kornprobst; Raphael Douady
  16. Forecasting with VAR models: fat tails and stochastic volatility By Chiu, Ching-Wai (Jeremy); Mumtaz, Haroon; Pinter, Gabor

  1. By: Boholm, Åsa (Gothenburg Research Institute); Corvellec, Hervé (Gothenburg Research Institute)
    Abstract: This report uses a relational theory of risk within which risk is understood as a relationship between a risk object and an object at risk where the risk object threatens the value embedded in the object at risk. A case study of risk management in railway planning examined through a relational understanding of risk demonstrates how riskwork is conditioned by what is valued, how, and by whom. The report argues that riskwork originates in the versatile valuation practices that take place in organizations. Furthermore, it suggests that bringing such valuation practices under critical scrutiny opens up the possibility for a reflexive approach to risk management. Such a reflexive approach would take into account how risk identification is embedded in a particular organizational order.
    Keywords: Relationship of risk; Valuation; Risk management; Practice; Railway planning
    Date: 2015–05–28
  2. By: Pfohl, Hans-Christian; Zuber, Christian; Berbner, Ulrich
    Date: 2014
  3. By: Hyejin Cho (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS)
    Abstract: The motivation of this article is to induce the bank capital management solution for banks and regulation bodies on commercial banks. The goal of the paper is intended to mitigate the risk of a banking area and also provide the right incentive for banks to support the real economy.
    Date: 2014–09–22
  4. By: Olivier Le Marois (fluks - FLUKS); Julia Mikhalevsky (FEDERIS Gestion d'Actifs - Federis Gestion d'Actifs); Raphaël Douady (Riskdata - Financial Risk Management Software, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS)
    Abstract: The LP formula is based upon the substitution of the exogenous risk aversion hypothesis by a credit equilibrium hypothesis. This leads to a trade-off between expected blue-sky return – the expected return excluding default scenarios – and extreme risk estimated from scenarios leading to default. An empirical study on the past 90 years shows that this trade-off curve is almost identical across asset classes. In equilibrium, an asset expected blue-sky return is proportional to its contribution to extreme risk. Assuming normal returns, we obtain CAPM as a sub-case of the LP relation. This relationship makes extreme risk underestimation a strong driver of asset price bubbles.
    Date: 2014–12
  5. By: Bodnaruk, Andriy; Chokaev, Bekhan; Simonov, Andrei
    Abstract: We study whether mutual funds systematically manage downside risk of their portfolios in ways that improve their performance. We find that actively managed mutual funds on average possess positive downside risk timing ability. Funds investing in large-cap and value stocks have stronger downside risk timing skills. Managers adjust funds’ downside risk exposure in response to macroeconomic information. The economic value of downside risk timing is comparable to that of market timing.
    Keywords: downside risk; market timing; mutual funds
    JEL: G10 G11
    Date: 2015–05
  6. By: Nassim Nicholas Taleb (NYU Polytechnic School of Engineering); Raphaël Douady (Riskdata - Financial Risk Management Software, CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS)
    Abstract: We provide a mathematical definition of fragility and antifragility as negative or positive sensitivity to a semi-measure of dispersion and volatility (a variant of negative or positive "vega") and examine the link to nonlinear effects. We integrate model error (and biases) into the fragile or antifragile context. Unlike risk, which is linked to psychological notions such as subjective preferences (hence cannot apply to a coffee cup) we offer a measure that is universal and concerns any object that has a probability distribution (whether such distribution is known or, critically, unknown). We propose a detection of fragility, robustness, and antifragility using a single "fast-and-frugal", model-free, probability free heuristic that also picks up exposure to model error. The heuristic lends itself to immediate implementation, and uncovers hidden risks related to company size, forecasting problems, and bank tail exposures (it explains the forecasting biases). While simple to implement, it improves on stress testing and bypasses the cillib flaws in Value-at-Risk.
    Date: 2014–12
  7. By: Paolo Giudici (Department of Economics and Management, University of Pavia); Shatha Hashem (Omar Ibn Al-Khattab Street, Nablus, Palestine)
    Abstract: The main aim of this paper is to investigate the proposition that Islamic banking services support financial stability. We examine this proposition using network modelling for stock market returns based on graphical Gaussian distributions, aimed at capturing the contagion effects that move along countries, combined with a regression modelling approach, aimed at capturing the effect of bank- specific strategies, that depend on the degree of Islamic financial services spe- cialization levels. The integration between the two models will enable us to distinguish the systemic correlations between banks due to common idiosyn- cratic characteristics, from the systemic correlation that can be attributed to country effects that are common to all banks in a given country. Our proposed models are applied to the MENA region banking sector for the period from 2007 to 2014.
    Keywords: Camels regression, Centrality measures, Graphical Gaussian models, Islamic bank specialisation levels
    Date: 2015–05
  8. By: Peter Martey Addo (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS); Philippe De Peretti (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS); Hayette Gatfaoui (Pôle Finance Responsable - Rouen Business School - Rouen Business School); Jakob Runge (Potsdam Institute for Climate Research, Potsdam, Germany, Department of Physics - Humboldt University - Berlin)
    Abstract: We provide a new approach to understanding systemic risk by analysing complex linkages in finance and insurance sectors. The analysis is achieved by using a recently proposed method for quantifying causal coupling strength, which identifies the existence of causal dependencies between two components of a multivariate time series and assesses the strength of their association by defining a meaningful coupling strength. The measure of association is general, causal and lag-specific, reflecting a well interpretable notion of coupling strength and is pratically computable. A comprehensive analysis of the feasibility of this approach is provided via simulated and real data.
    Date: 2014–10
  9. By: Renata Karkowska (University of Warsaw, Faculty of Management)
    Abstract: The goal of this study is to identify empirically how non-traditional activities affect directly the risk profiles and profitability of the banking sector. Through a dataset that covers 2678 European banks spanning the period 1996–2011 and the methodology of panel regression, the empirical findings document that investment banks have a negative effect on systemic risk in the banking sector. To show the heterogeneity of systemic risk determinants, the study sample was divided according to the economic development of a country into two groups: advanced and developing countries. We examine the implications of banks’ activity and risk-taking that manifest themselves as spreading and growing instability in the banking system. Then we explore the implications of the interaction between banking risk and structural, macroeconomic and financial market determinants. The findings have implications for both bank risk management and regulators. This paper advances the agenda of making macroprudential policy operational.
    Keywords: systemic risk, investment banking, emerging markets, credit risk, liquidity, bank solvency, instability
    JEL: F36 G21 G32 G33
    Date: 2015–05
  10. By: Catullo, Ermanno; Gallegati, Mauro; Palestrini, Antonio
    Abstract: Assessing systemic risk and defining macro-prudential policies aiming at reducing economic system vulnerability have been at the center of the economic debate of the last years. Credit networks play a crucial role in diffusing and amplifying local shocks, following the network-based financial accelerator approach (Delli Gatti et al., 2010; Battiston et al., 2012), we constructed an agent based model reproducing an artificial credit network populated by heterogeneous firms and banks. Calibrating the model on a sample of firms and banks quoted on Japanese stock-exchange mar- kets from 1980 to 2012, we try to define both early warning indicators of crises and policy precautionary measures based on the analysis of the endogenous dynamics of credit network connectivity.
    Date: 2015
  11. By: Ramsay, Bruce A.; Sarlin, Peter
    Abstract: This paper introduces the ratio of debt to cash flow (D/CF) of nations and their economic sectors to macroprudential analysis, particularly as an indicator of systemic risk and vulnerabilities. While leverage is oftentimes linked to the vulnerability of a nation, the stock of total debt and the flow of gross savings is a less explored measure. Cash flows certainly have a well-known connection to corporations' ability to service debt. This paper investigates whether the D/CF provides a means for understanding systemic risks. For a panel of 33 nations, we explore historic D/CF trends, and apply the same procedure to economic sectors. In terms of an early-warning indicator, we show that the D/CF ratio provides a useful additional measure of vulnerability to systemic banking and sovereign crises, relative to more conventional indicators. As a conceptual framework, the assessment of financial stability is arranged for presentation within four vulnerability zones, and exemplified with a number of illustrative case studies. JEL Classification: E21, F34, G01, H63
    Keywords: debt to cash flow, early-warning indicator, systemic risk, total debt to gross savings
    Date: 2015–03
  12. By: Bahman Angoshtari; Erhan Bayraktar; Virginia R. Young
    Abstract: We determine the optimal investment strategy in a Black-Scholes financial market to minimize the so-called {\it probability of drawdown}, namely, the probability that the value of an investment portfolio reaches some fixed proportion of its maximum value to date. We assume that the portfolio is subject to a payout that is a deterministic function of its value, as might be the case for an endowment fund paying at a specified rate, for example, at a constant rate or at a rate that is proportional to the fund's value.
    Date: 2015–05
  13. By: Moccero, Diego; Gnabo, Jean-Yves
    Abstract: We estimate regime switching models where the strength of the response of monetary policy to macroeconomic conditions depends on the level of risk associated with the inflation outlook and risk in financial markets. Using quarterly data for the Greenspan period we find that: i) risk in the inflation outlook and volatility in financial markets are a powerful driver of monetary policy regime changes in the U.S.; ii) the response of the US Fed to the inflation outlook is invariant across policy regimes; iii) however, in periods of high economic risk, monetary policy tends to respond more aggressively to the output gap and the degree of inertia tends to be lower than in normal circumstances; and iv) the US Fed is estimated to have responded aggressively to the output gap in the late 1980s and begging of the 1990s, and in the late 1990s and early 2000s. JEL Classification: C24, C51, E52
    Keywords: aggressiveness, monetary policy, risk management, smooth-transition regression model, US Fed
    Date: 2015–05
  14. By: Rania Hentati Kaffel (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS); Jean-Luc Prigent (THEMA - Théorie économique, modélisation et applications - Université de Cergy Pontoise - CNRS)
    Abstract: The recent financial crisis has highlighted the necessity to introduce mixtures of probability distributions in order to improve the estimation of asset returns and in particular to better take account of risks. Since Pearson (1894), these mixtures have been intensively used in many scientific fields since they provide very convenient mathematical tools to examine various statistical data and to approximate many probability distributions. They are typically introduced to model the choice of probability distributions among a given parametric family. The coefficients of the mixture usually correspond to the relative frequencies of each possible parameter. In this framework, we examine the single-period portfolio choice model, which has been addressed in the partial equilibrium framework, by Brennan and Solanki (1981), Leland (1980) and Prigent (2006). We consider an investor who wants to maximize the expected utility of the value of his portfolio consisting of one risk-free asset and one risky asset. We provide and analyze the solution for log return with mixture distributions, in particular for the mixture Gaussian case. The optimal portfolio is characterized for arbitrary utility functions. Our results show that mixture of distributions can have significant implications on the portfolio management.
    Date: 2014–09–19
  15. By: Antoine Kornprobst; Raphael Douady
    Abstract: The aim of this work is to build financial crisis indicators based on market data time series. After choosing an optimal size for a rolling window, the market data is seen every trading day as a random matrix from which a covariance and correlation matrix is obtained. Our indicators deal with the spectral properties of these covariance and correlation matrices. Our basic financial intuition is that correlation and volatility are like the heartbeat of the financial market: when correlations between asset prices increase or develop abnormal patterns, when volatility starts to increase, then a crisis event might be around the corner. Our indicators will be mainly of two types. The first one is based on the Hellinger distance, computed between the distribution of the eigenvalues of the empirical covariance matrix and the distribution of the eigenvalues of a reference covariance matrix. As reference distributions we will use the theoretical Marchenko Pastur distribution and, mainly, simulated ones using a random matrix of the same size as the empirical rolling matrix and constituted of Gaussian or Student-t coefficients with some simulated correlations. The idea behind this first type of indicators is that when the empirical distribution of the spectrum of the covariance matrix is deviating from the reference in the sense of Hellinger, then a crisis may be forthcoming. The second type of indicators is based on the study of the spectral radius and the trace of the covariance and correlation matrices as a mean to directly study the volatility and correlations inside the market. The idea behind the second type of indicators is the fact that large eigenvalues are a sign of dynamic instability.
    Date: 2015–06
  16. By: Chiu, Ching-Wai (Jeremy) (Bank of England); Mumtaz, Haroon (Queen Mary University of London); Pinter, Gabor (Bank of England)
    Abstract: In this paper, we provide evidence that fat tails and stochastic volatility can be important in improving in-sample fit and out-of-sample forecasting performance. Specifically, we construct a VAR model where the orthogonalised shocks feature Student’s t distribution and time-varying variance. We estimate this model using US data on output growth, inflation, interest rates and stock returns. In terms of in-sample fit, the VAR model featuring both stochastic volatility and t-distributed disturbances outperforms restricted alternatives that feature either attributes. The VAR model with t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity, and this difference is especially stark over the recent Great Recession. Further international evidence confirms that accounting for both stochastic volatility and Student’s t-distributed disturbances may lead to improved forecast accuracy.
    Keywords: Bayesian VAR; fat-tails; stochastic volatility; Great Recession
    JEL: C11 C32 C52
    Date: 2015–05–29

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