nep-rmg New Economics Papers
on Risk Management
Issue of 2015‒08‒30
twenty papers chosen by

  1. Mean-risk hedging strategies in electricity markets with limited liquidity By Woll, Oliver
  2. Copula-Based Factor Model for Credit Risk Analysis By Lu, Meng-Jou; Chen, Cathy Yi-Hsuan; Härdle, Karl Wolfgang; Härdle
  3. Tail risk in hedge funds: A unique view from portfolio holdings By Agarwal, Vikas; Ruenzi, Stefan; Weigert, Florian
  4. Where the Risks Lie: A Survey on Systemic Risk By Sylvain Benoit; Jean-Edouard Colliard; Christophe Hurlin; Christophe Pérignon
  5. Model Risk and the Great Financial Crisis: The Rise of Modern Model Risk Management By Brown, Jeffrey A.; McGourty, Brad; Schuermann, Til
  6. Identifying Dependence Structure among Equities in Indian Markets using Copulas By Grover, Vaibhav
  7. Bank Capital for Operational Risk: A Tale of Fragility and Instability By Ames, Mark; Schuermann, Til; Scott, Hal S.
  8. Governance, Risk Management, and Risk-Taking in Banks By Stulz, Rene M.
  9. Risk Spillovers across the Energy and Carbon Markets and Hedging Strategies for Carbon Risk By Mehmet Balcilar; Riza Demirer; Shawkat Hammoudeh; Duc Khuong Nguyen
  10. Many a little makes a mickle: Macro portfolio stress test for small and medium-sized German banks By Busch, Ramona; Koziol, Philipp; Mitrovic, Marc
  11. Bank charter value, systemic risk and credit reporting systems: Evidence from the Asia-Pacific region By Wahyoe Soedarmono; Romora Edward Sitorus; Amine Tarazi
  12. Why FX Risk Management Is Broken – And What Boards Need to Know to Fix It By Jankensgård, Håkan; Alviniussen, Alf; Oxelheim, Lars
  13. Volatility of aggregate volatility and hedge funds returns By Agarwal, Vikas; Arisoy, Y. Eser; Naik, Narayan Y.
  14. Hedge Funds: A Dynamic Industry In Transition By Mila Getmansky; Peter A. Lee; Andrew W. Lo
  15. The Causal Effect of Option Pay on Corporate Risk Management By Bakke, Tor-Erik; Mahmudi, Hamed; Fernando, Chitru S.; Salas, Jesus M.
  16. Heterotic Risk Models By Zura Kakushadze
  17. The Benefits of Geographic Diversification in Banking By Céline Meslier; Donald P. Morgan; Katherine Samolyk; Amine Tarazi
  18. Predicting Stock Returns in the Capital Asset Pricing Model Using Quantile Regression and Belief Functions By K Autchariyapanitkul; S Chanaim; S Sriboonchitta; T Denoeux
  19. L’Analyse Globale des Risques Quantitative (AGRq) By Sebastien Delmotte; Alain Desroches
  20. Shadow Banking and Bank Capital Regulation By Guillaume Plantin

  1. By: Woll, Oliver
    Abstract: This article investigates mean risk hedging with respect to limited liquidity and studies the impact of different risk measures on the hedging strategies. For motivation and application purposes hedging in electricity markets is chosen, because the relevant hedging markets are characterized by limited liquidity. We enhance the approach in Woll and Weber (2015) to a mean-risk optimization under limited liquidity, including the risk measures absolute and relative Value and Conditional Value at Risk (VaR and CVaR). It can be shown that for position independent measures (Variance, relative VaR, relative CVaR) liquidity has no influence on the minimum risk hedging strategies, whereas for position dependent measures (absolute VaR, absolute CVaR) liquidity has an impact on the minimum risk hedging strategies. The article gives the mathematical formulations of the problems and discusses the economic relevance of the different models. In addition, we apply the analyzed concepts to the German Electricity markets.
    Keywords: optimization,electricity,liquidity,electricity trading,mean-risk-model
    JEL: C61 G11 Q40
    Date: 2015
  2. By: Lu, Meng-Jou; Chen, Cathy Yi-Hsuan; Härdle, Karl Wolfgang; Härdle
    Abstract: A standard quantitative method to access credit risk employs a factor model based on joint multi- variate normal distribution properties. By extending a one-factor Gaussian copula model to make a more accurate default forecast, this paper proposes to incorporate a state-dependent recovery rate into the con- ditional factor loading, and model them by sharing a unique common factor. The common factor governs the default rate and recovery rate simultaneously and creates their association implicitly. In accordance with Basel III, this paper shows that the tendency of default is more governed by systematic risk rather than idiosyncratic risk during a hectic period. Among the models considered, the one with random fac- tor loading and a state-dependent recovery rate turns out to be the most superior on the default prediction.
    Keywords: Factor Model, Conditional Factor Loading, State-Dependent Recovery Rate
    JEL: C38 C53 F34 G11 G17
    Date: 2015–08
  3. By: Agarwal, Vikas; Ruenzi, Stefan; Weigert, Florian
    Abstract: We develop a new tail risk measure for hedge funds to examine the impact of tail risk on fund performance and to identify the sources of tail risk. We find that tail risk affects the cross-sectional variation in fund returns, and investments in both, tailsensitive stocks as well as options, drive tail risk. Moreover, managerial incentives and discretion as well as exposure to funding liquidity shocks are important determinants of tail risk. We find evidence that is consistent with funds being able to time tail risk exposure prior to the recent financial crisis.
    Keywords: Hedge Funds,Tail Risk,Portfolio Holdings,Funding Liquidity Risk
    JEL: G11 G23
    Date: 2015
  4. By: Sylvain Benoit (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS); Jean-Edouard Colliard (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - CNRS - GROUPE HEC); Christophe Hurlin (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS); Christophe Pérignon (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - CNRS - GROUPE HEC)
    Abstract: We review the extensive literature on systemic risk and connect it to the current regulatory debate. While we take stock of the achievements of this rapidly growing field, we identify a gap between two main approaches. The first one studies different sources of systemic risk in isolation, uses confidential data, and inspires targeted but complex regulatory tools. The second approach uses market data to produce global measures which are not directly connected to any particular theory, but could support a more efficient regulation. Bridging this gap will require encompassing theoretical models and improved data disclosure.
    Date: 2015–04–14
  5. By: Brown, Jeffrey A. (Oliver Wyman); McGourty, Brad (Oliver Wyman); Schuermann, Til (Oliver Wyman)
    Abstract: We trace the development of model risk management in U.S. banking against the backdrop of the growing importance of complex financial models in banks, the recognition of model risk, the emergence of model validation as a response to model risk, and the contribution of failures in model risk management to the Great Financial Crisis. We recognize that while substantial progress has been made in the management of model risk, the challenges have grown, including the increasing reliance by the regulators on models.
    JEL: G12 G21
    Date: 2015–01
  6. By: Grover, Vaibhav
    Abstract: In this study we have examined that assets returns in Indian markets do not follow an elliptical dependence structure; asymmetric tail dependence can be observed among asset returns particularly when the assets exhibit downside returns in a bearish market. We have used Elliptical, Archimedean and Canonical Vine copulas to model such dependence structure in large portfolios. Using certain goodness-of-fit tests we find that Archimedean copulas are insufficient to model the dependence among assets in a large portfolio. We have also compared copula models using an out-of-sample Value-at-Risk (VaR) calculation and comparing results to the historical data. It is observed that the Canonical Vine copulas consistently capture the variation in weekly and daily VaR values.
    Keywords: copula, vine copulas, Value-at-Risk
    JEL: G17
    Date: 2015–08–27
  7. By: Ames, Mark (Oliver Wyman); Schuermann, Til (Oliver Wyman and University of PA); Scott, Hal S. (Harvard University)
    Abstract: Operational risk is fundamentally different from all other risks taken on by a bank. It is embedded in every activity and product of an institution, and in contrast to the conventional financial risks (e.g. market, credit) is harder to measure and model, and not straight forwardly eliminated through simple adjustments like selling off a position. Operational risk tends to be about 9-13% of the total risk pie, though growing rapidly since the 2008-09 crisis. It tends to be more fat-tailed than other risks, and the data are poorer. As a result, models are fragile--small changes in the data have dramatic impacts on modeled output--and thus required operational risk capital is unstable. Yet the regulatory capital regime is, surprisingly, more rigidly model focused for this risk than for any other, at least in the U.S. We are especially concerned with the absence of incentives to invest in and improve business control processes through the granting of regulatory capital relief. We make four, not mutually exclusive policy suggestions. First, address model fragility through anchoring of key model parameters, yet allow each bank to scale capital to their data using robust methodologies. Second, relax the current tight linkage between statistical model output and required regulatory capital, incentivizing prudent risk management through joint use of scenarios and control factors in addition to data-based statistical models in setting regulatory capital. Third, provide allowance for real risk transfer through an insurance credit to capital, encouraging more effective risk sharing through future product innovation. Fourth, expand upon the standard taxonomy of event type and business line to include additional explanatory variables (such as product type, flags for litigated events, etc.) that would allow more effective inter-bank sharing and learning from experience. Until our understanding of operational risks increases, required regulatory capital should be based on methodologies that are simpler, more standardized, more stable and more robust.
    Date: 2014–02
  8. By: Stulz, Rene M. (OH State University and ECGI, Brussels)
    Abstract: This paper examines how governance and risk management affect risk-taking in banks. It distinguishes between good risks, which are risks that have an ex ante private reward for the bank on a stand-alone basis, and bad risks, which do not have such a reward. A well-governed bank takes the amount of risk that maximizes shareholder wealth subject to constraints imposed by laws and regulators. In general, this involves eliminating or mitigating all bad risks to the extent that it is cost effective to do so. The role of risk management in such a bank is not to reduce the bank's total risk per se. It is to identify and measure the risks the bank is taking, aggregate these risks in a measure of the bank's total risk, enable the bank to eliminate, mitigate and avoid bad risks, and ensure that its risk level is consistent with its risk appetite. Organizing the risk management function so that it plays that role is challenging because there are limitations in measuring risk and because, while more detailed rules can prevent destructive risk-taking, they also limit the flexibility of an institution in taking advantage of opportunities that increase firm value. Limitations of risk measurement and the decentralized nature of risk-taking imply that setting appropriate incentives for risk-takers and promoting an appropriate risk culture are essential to the success of risk management in performing its function.
    JEL: G21 G32
    Date: 2014–06
  9. By: Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Famagusta, Northern Cyprus); Riza Demirer (Department of Economics & Finance Southern Illinois University); Shawkat Hammoudeh (3200 Market Street Philadelphia, PA 19104 U.S.A Author-Email: -); Duc Khuong Nguyen (-)
    Abstract: This study examines the risk spillovers between energy futures prices and Europe-based carbon futures contracts. We use a Markov regime-switching dynamic correlation, generalized autoregressive conditional heteroscedasticity (MSDCC- GARCH) model in order to capture the time variations and structural breaks in the spillovers. We further evaluate the optimal weights, hedging effectiveness, and dynamic hedging strategies for the MS-DCC-GARCH model based on both the regime dependent and regime independent optimal hedge ratios. We finally complement our analysis by examining the in- and out-of sample hedging performances for alternative strategies. Our results mainly show significant volatility and time-varying risk transmission from energy markets to carbon market. We also find that spot and futures segments of the emission markets exhibit time-varying correlations and volatile hedging effectiveness. These results have important investment and policy implications
    Keywords: Multivariate regime-switching; time-varying correlations; hedging; CO2 allowance prices
    JEL: C32 G11 G19 Q47 Q54
    Date: 2014
  10. By: Busch, Ramona; Koziol, Philipp; Mitrovic, Marc
    Abstract: We develop a macroeconomic portfolio stress test that is specifically geared towards small and medium-sized banks. We combine a credit risk stress test which simulates credit impairments via a CreditMetrics type multi-factor portfolio model with an income stress test in the form of dynamic panel data regressions. Based on a stress scenario that extends experience of the financial crisis by integrating the current low interest rate environment, we analyse the stress impact on banks' capital ratios. Our results show that savings banks and cooperative banks prove to be very resilient to macroeconomic stress, while more than 6% of our sample's credit banks "fail" the stress test, mainly due to their lack of capital. The main stress drivers prove to be credit impairments rather than other net income components.
    Keywords: Macro Stress Tests,Macroprudential Supervision,Small and Medium-sized Banks,Income Stress Test,Credit Risk
    JEL: C13 C15 G21 G33
    Date: 2015
  11. By: Wahyoe Soedarmono (Universitas Siswa Bangsa Internasional, Faculty of Business / Sampoerna School of Business); Romora Edward Sitorus (Universitas Siswa Bangsa Internasional, Faculty of Business / Sampoerna School of Business); Amine Tarazi (LAPE - Laboratoire d'Analyse et de Prospective Economique - UNILIM - Université de Limoges - IR SHS UNILIM - Institut Sciences de l'Homme et de la Société)
    Abstract: From a sample of publicly-traded banks in the Asia-Pacific region over the 1998-2012 period, we document that banks with higher charter value are able to insulate themselves from systemic risk by acquiring more capital. Nevertheless, we find that the self-disciplining role of bank charter value is more pronounced for countries with lower depth of credit information sharing. Our results also show that in countries with lower quality of private credit bureaus, higher charter value enhances capitalization, and alleviates systemic risk in banking. Overall, these findings suggest that higher bank charter value might be detrimental to systemic stability for countries where the credit reporting system is of better quality.
    Date: 2015–07–01
  12. By: Jankensgård, Håkan (Department of Business Administration); Alviniussen, Alf (European Banking Authority); Oxelheim, Lars (Research Institute of Industrial Economics (IFN))
    Abstract: In this paper we challenge the role of Foreign Exchange Risk Management (FXRM) in corporate management. We believe it is fair to characterize FXRM, on the whole, as a legacy activity rather than something that reflects a realistic cost-benefit analysis at the enterprise-level. The Board of Directors, as the designated guardians of the interests of shareholders, has a key role in setting the firm on a path towards a cost-efficient and centralized FXRM that preserves the firm’s transparency and predictability towards the investor community. A policy conclusion from our analysis is that responsibility for FX policy should shift from the traditional Finance/Treasury orientation to a group risk function (e.g. a Chief Risk Officer) supported by a risk committee dedicated to integrated risk management.
    Keywords: Foreign exchange; Risk management; Transparency; Risk committee; Integrated risk management
    JEL: G30 G32
    Date: 2015–08–21
  13. By: Agarwal, Vikas; Arisoy, Y. Eser; Naik, Narayan Y.
    Abstract: This paper investigates empirically whether uncertainty about volatility of the market portfolio can explain the performance of hedge funds both in the cross-section and over time. We measure uncertainty about volatility of the market portfolio via volatility of aggregate volatility (VOV) and construct an investable version of this measure by computing monthly returns on lookback straddles on the VIX index. We find that VOV exposure is a significant determinant of hedge fund returns at the overall index level, at different strategy levels, and at an individual fund level. After controlling for a large set of fund characteristics, we document a robust and significant negative risk premium for VOV exposure in the cross-section of hedge fund returns. We further show that strategies with less negative VOV betas outperform their counterparts during the financial crisis period when uncertainty was at its highest. On the contrary, strategies with more negative VOV betas generate superior returns when uncertainty in the market is less. Finally, we demonstrate that VOV exposure-return relationship of hedge funds is distinct from that of mutual funds and is consistent with the dynamic trading of hedge funds and risk-taking incentives arising from performance-based compensation of hedge funds.
    Keywords: uncertainty,volatility of volatility,hedge funds,performance
    JEL: G10 G11 C13
    Date: 2015
  14. By: Mila Getmansky; Peter A. Lee; Andrew W. Lo
    Abstract: The hedge-fund industry has grown rapidly over the past two decades, offering investors unique investment opportunities that often reflect more complex risk exposures than those of traditional investments. In this article we present a selective review of the recent academic literature on hedge funds as well as updated empirical results for this industry. Our review is written from several distinct perspectives: the investor's, the portfolio manager's, the regulator's, and the academic's. Each of these perspectives offers a different set of insights into the financial system, and the combination provides surprisingly rich implications for the Efficient Markets Hypothesis, investment management, systemic risk, financial regulation, and other aspects of financial theory and practice.
    JEL: G01 G11 G12 G20 G23 G24
    Date: 2015–08
  15. By: Bakke, Tor-Erik (University of OK); Mahmudi, Hamed (University of OK); Fernando, Chitru S. (University of OK); Salas, Jesus M. (Lehigh University)
    Abstract: This study provides strong evidence of a causal effect of risk-taking incentives provided by option compensation on corporate risk management. We utilize the passage of FAS 123R, which required firms to expense options, to investigate how CEO option compensation affects the hedging behavior of oil and gas firms. Firms that did not expense options before FAS 123R significantly reduced option pay, which resulted in a large increase in their hedging intensity compared to firms that did not use options or expensed their options voluntarily prior to FAS 123R.
    JEL: G30 G32 G38 G39
    Date: 2015–06
  16. By: Zura Kakushadze
    Abstract: We give a complete algorithm and source code for constructing what we refer to as heterotic risk models (for equities), which combine: i) granularity of an industry classification; ii) diagonality of the principal component factor covariance matrix for any sub-cluster of stocks; and iii) dramatic reduction of the factor covariance matrix size in the Russian-doll risk model construction. This appears to prove a powerful approach for constructing out-of-sample stable short-lookback risk models. Thus, for intraday mean-reversion alphas based on overnight returns, Sharpe ratio optimization using our heterotic risk models sizably improves the performance characteristics compared to weighted regressions based on principal components or industry classification. We also give source code for: a) building statistical risk models; and ii) Sharpe ratio optimization with homogeneous linear constraints and position bounds.
    Date: 2015–08
  17. By: Céline Meslier (LAPE - Laboratoire d'Analyse et de Prospective Economique - unilim - Université de Limoges - Institut Sciences de l'Homme et de la Société); Donald P. Morgan (Federal Reserve Bank of New-York - Federal Reserve Bank of New-York); Katherine Samolyk (Consumer Financial Protection Bureau - Consumer Financial Protection Bureau); Amine Tarazi (LAPE - Laboratoire d'Analyse et de Prospective Economique - unilim - Université de Limoges - Institut Sciences de l'Homme et de la Société)
    Abstract: We estimate the benefits of intrastate and interstate geographic diversification for bank risk and return, and assess whether such benefits could be shaped by differences in bank size and disparities in economic conditions within states or across U.S. states. For small banks, only intrastate diversification is beneficial in terms of risk-adjusted returns but for very large institutions both intrastate and interstate expansions are rewarding. However, in all cases the relationship is hump-shaped for both intrastate and interstate diversification indicating limits for banks of all size. Moreover, we also find geographic expansion to reduce bank risk. Our results indicate that both small banks and very large banks could still benefit in terms of risk-adjusted returns from further geographic diversification. Disparities in economic conditions as measured by the dispersion in unemployment rates either across counties or states impact the benefits of diversification. At initially low levels of intrastate diversification, expanding in new markets allows small banks to further reduce their risk in the presence of higher economic disparities. However, when they get more diversified, this effect is reduced.
    Date: 2015–05–26
  18. By: K Autchariyapanitkul (Faculty of Economics, Chiang Mai University); S Chanaim (Faculty of Economics, Chiang Mai University); S Sriboonchitta (Faculty of Economics, Chiang Mai University); T Denoeux (Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne] - Université de Technologie de Compiègne - CNRS, Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques" - Université de Technologie de Compiègne - CNRS)
    Abstract: We consider an inference method for prediction based on belief functions in quantile regression with an asymmetric Laplace distribution. We apply this method to the capital asset pricing model to estimate the beta coefficient and measure volatility under various market conditions at given quantiles. Likelihood-based belief functions are constructed from historical data of the securities in the S&P500 market. The results give us evidence on the systematic risk, in the form of a consonant belief function specified from the asymmetric Laplace distribution likelihood function given recorded data. Finally, we use the method to forecast the return of an individual stock.
    Date: 2014–09–26
  19. By: Sebastien Delmotte (MAD-Environnement - MAD-Environnement); Alain Desroches (LGI - Laboratoire Génie Industriel - EA 2606 - Ecole Centrale Paris)
    Abstract: Après un rappel sur les concepts fondamentaux en management des risques, les auteurs présentent la méthode d’Analyse Globale des Risques Quantitative (AGRq), variante quantitative de la méthode AGR et fondée sur la représentation probabiliste des risques dans les processus d’évaluation, de décision et de financement. AGR est le nom donné par son auteur à la méthode APR réactualisée [2] qui, après plusieurs évolutions importantes du processus initial, permet de couvrir un périmètre d’analyse et de gestion plus vaste tant au niveau des risques structurels que fonctionnels de toute nature pendant tout le cycle de vie d‘un système, de l’étude de sa faisabilité jusqu’à celle de son démantèlement. Les invariants du processus d’analyse de l’AGR et de l’AGRq sont détaillés en mettant l’accent sur les spécificités de l’approche quantitative, illustrée par la présentation des formats des référentiels d’évaluation et de décision, des supports de l’analyse et de ses sorties.
    Abstract: After a reminder about the fundamental concepts in risk management, the authors present the Quantitative Global Risk Analysis method (GRAq), a quantitative variant of GRA method based on the probabilistic representation of risk, decision and funding assessment processes. GRA is the name given by its author to the up-to-date PRA method which after several changes of the initial process covers a broader perimeter of analysis and management both at the level of structural and functional risks of any kind throughout the system life cycle, from the study of its feasibility to its dismantling. The invariants of GRA and GRAq are detailed with emphasis on the specificities of the quantitative approach.
    Date: 2014–10–21
  20. By: Guillaume Plantin (ECON - Département d'économie - Sciences Po)
    Abstract: Banks are subject to capital requirements because their privately optimal leverage is higher than the socially optimal one. This is in turn because banks fail to internalize all costs that their insolvency creates for agents who use their money-like liabilities to settle transactions. If banks can bypass capital regulation in an opaque shadow banking sector, it may be optimal to relax capital requirements so that liquidity dries up in the shadow banking sector. Tightening capital requirements may spur a surge in shadow banking activity that leads to an overall larger risk on the money-like liabilities of the formal and shadow banking institutions.
    Date: 2015–01

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.