nep-rmg New Economics Papers
on Risk Management
Issue of 2017‒05‒28
eleven papers chosen by
Stan Miles
Thompson Rivers University

  1. Data and uncertainty in extreme risks; a nonlinear expectations approach By Samuel N. Cohen
  2. Conduct Risk - distribution models with very thin Tails By Peter Mitic
  3. How is the likelihood of fire sales in a crisis affected by the interaction of various bank regulations? By Divya Kirti; Vijay Narasiman
  4. Herding boosts too-connected-to-fail risk in stock market of China By Shan Lu; Jichang Zhao; Huiwen Wang; Ruoen Ren
  5. ASYMPTOTIC MULTIVARIATE EXPECTILES By Véronique Maume-Deschamps; Didier Rullière; Khalil Said
  6. Real effects of bank capital regulations: Global evidence By Deli, Yota; Hasan, Iftekhar
  7. How Production Risk and Flexibility Affect Liquidity Management: Evidence from Electricity Generating Firms By Chen Lin; Thomas Schmid; Michael S. Weisbach
  8. Formation of a System of Prudential Supervision for Professional Participants of the Securities Market By Korishchenko, Konstantin; Morozov, Stepan
  9. Optimal Dividends in the Dual Risk Model under a Stochastic Interest Rate By Zailei Cheng
  10. Disaster recovery and the term structure of dividend strips? By Michael Hasler; Roberto Marfè
  11. CDS Rate Construction Methods by Machine Learning Techniques By Brummelhuis, Raymond; Luo, Zhongmin

  1. By: Samuel N. Cohen
    Abstract: Estimation of tail quantities, such as expected shortfall or Value at Risk, is a difficult problem. We show how the theory of nonlinear expectations, in particular the Data-robust expectation introduced in [4], can assist in the quantification of statistical uncertainty for these problems. However, when we are in a heavy-tailed context (in particular when our data are described by a Pareto distribution, as is common in much of extreme value theory), the theory of [4] is insufficient, and requires an additional regularization step which we introduce. By asking whether this regularization is possible, we obtain a qualitative requirement for reliable estimation of tail quantities and risk measures, in a Pareto setting.
    Date: 2017–05
  2. By: Peter Mitic
    Abstract: Regulatory requirements dictate that financial institutions must calculate risk capital (funds that must be retained to cover future losses) at least annually. Procedures for doing this have been well-established for many years, but recent developments in the treatment of conduct risk (the risk of loss due to the relationship between a financial institution and its customers) have cast doubt on 'standard' procedures. Regulations require that operational risk losses should be aggregated by originating event. The effect is that a large number of small and medium-sized losses are aggregated into a small number of very large losses, such that a risk capital calculation produces a hugely inflated result. To solve this problem, a novel distribution based on a one-parameter probability density with an exponential of a fourth power is proposed, where the parameter is to be estimated. Symbolic computation is used to derive the necessary analytical expressions with which to formulate the problem, and is followed by numeric calculations in R. Goodness-of-fit and parameter estimation are both determined by using a novel method developed specifically for use with probability distribution functions. The results compare favourably with an existing model that used a LogGamma Mixture density, for which it was necessary to limit the frequency and severity of the losses. No such limits were needed using the proposed exponential density.
    Date: 2017–05
  3. By: Divya Kirti; Vijay Narasiman
    Abstract: We present a model that describes how different types of bank regulation can interact to affect the likelihood of fire sales in a crisis. In our model, risk shifting motives drive how banks recapitalize following a negative shock, leading banks to concentrate their portfolios. Regulation affects the likelihood of fire sales by giving banks the incentive to sell certain assets and retain others. Ex-post incentives from high risk weights and the interaction of capital and liquidity requirements can make fire sales more likely. Time-varying risk weights may be an effective tool to prevent fire sales.
    Keywords: Macroprudential Policy;Capital requirements;Bank regulation, liquidity requirements, Government Policy and Regulation
    Date: 2017–03–24
  4. By: Shan Lu; Jichang Zhao; Huiwen Wang; Ruoen Ren
    Abstract: The crowd panic and its contagion play non-negligible roles at the time of the stock crash, especially for China where inexperienced investors dominate the market. However, existing models rarely consider investors in networking stocks and accordingly miss the exact knowledge of how panic contagion leads to abrupt crash. In this paper, by networking stocks of sharing common mutual funds, a new methodology of investigating the the market crash from the perspective of investor behavior is presented. It is surprisingly revealed that the herding, which origins in the mimic of seeking for high diversity across investment strategies to lower individual risk, will produce too-connected-to-fail stocks and reluctantly boosts the systemic risk of the entire market. Though too-connected stocks might be relatively stable during the crisis, they are so influential that a small downward fluctuation will cascade to trigger severe drops of massive successor stocks, implying that their disturbances might be unexpectedly amplified by the collective panic and result in the market crash. Our findings suggest that the whole picture of portfolio strategy has to be carefully supervised to reshape the stock network and highly connected stocks should be warned at early stages to avoid the market panic.
    Date: 2017–05
  5. By: Véronique Maume-Deschamps (ICJ - Institut Camille Jordan [Villeurbanne] - ECL - École Centrale de Lyon - UCBL - Université Claude Bernard Lyon 1 - UJM - Université Jean Monnet [Saint-Etienne] - INSA - Institut National des Sciences Appliquées - CNRS - Centre National de la Recherche Scientifique); Didier Rullière (SAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1); Khalil Said (Ecole d'Actuariat - Université Laval)
    Abstract: In [16], a new family of vector-valued risk measures called multivariate expectiles is introduced. In this paper, we focus on the asymptotic behavior of these measures in a multivariate regular variations context. For models with equivalent tails, we propose an estimator of these multivariate asymptotic expectiles, in the Fréchet attraction domain case, with asymptotic independence, or in the comonotonic case.
    Keywords: Risk measures, multivariate expectiles, regular variations, extreme values, tail dependence functions
    Date: 2017–04–18
  6. By: Deli, Yota; Hasan, Iftekhar
    Abstract: We examine the effect of the full set of bank capital regulations (capital stringency) on loan growth, using bank-level data for a maximum of 125 countries over the period 1998-2011. Contrary to standard theoretical considerations, we find that overall capital stringency only has a weak negative effect on loan growth. In fact, this effect is completely offset if banks hold moderately high levels of capital. Interestingly, the components of capital stringency that have the strongest negative effect on loan growth are those related to the prevention of banks to use as capital borrowed funds and assets other than cash or government securities. In contrast, compliance with Basel guidelines in using Basel- and credit-risk weights has a much less potent effect on loan growth.
    Keywords: capital regulation, loan growth, bank capital
    JEL: E60 G0 G2 O40
    Date: 2017
  7. By: Chen Lin; Thomas Schmid; Michael S. Weisbach
    Abstract: Production inflexibility together with product price uncertainty creates production risk, which is a potentially important factor for firms’ liquidity management. One industry for which production risk can be measured is the electricity producing industry. We use data on hourly electricity prices in 41 markets to measure fluctuations in output prices and information on over 60,000 power plants to approximate firms’ cost to vary output quantities. Our results suggest that higher electricity price volatility leads to increased cash holdings, but only in firms using inflexible production technologies. This effect is robust to a number of specification choices including instrumenting for volatility in electricity prices using weather forecast data. After deregulation, firms hold 20-25% more cash, suggesting that the process of deregulation increases the risk firms’ face. Production risk affects cash holdings most in financially constrained firms, and in firms that cannot easily hedge the electricity price through derivative markets. Capital market liquidity and balance sheet liquidity appear to be substitutes for one another.
    JEL: G3 G32 G35 L5
    Date: 2017–05
  8. By: Korishchenko, Konstantin (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Morozov, Stepan (Russian Presidential Academy of National Economy and Public Administration (RANEPA))
    Abstract: The purpose of this work is the development of a system of prudential supervision measures for the activities of professional participants in the securities market for the purpose of minimizing systemic risks and ensuring the stability of the financial system, enhancing the protection of investors and depositors and creating fair and transparent financial markets. In the course of the research, a study was conducted on the application of prudential supervision measures in relation to Russian banks as the main participants of the domestic financial market, the harmonization of existing prudential banking supervision measures with respect to professional participants, and a study of the world experience in carrying out these measures using the example of the regulatory bodies of Great Britain, Australia and the USA.
    Date: 2017–05
  9. By: Zailei Cheng
    Abstract: Optimal dividend strategy in dual risk model is well studied in the literatures. But to the best of our knowledge, all the previous works assumes deterministic interest rate. In this paper, we study the optimal dividends strategy in dual risk model, under a stochastic interest rate, assuming the discounting factor follows a geometric Brownian motion or exponential L\'evy process. We will show that closed form solutions can be obtained.
    Date: 2017–05
  10. By: Michael Hasler; Roberto Marfè
    Abstract: Recent empirical findings document downward-sloping term structures of equity return volatility and risk premia. An equilibrium model with rare disasters followed by recoveries helps recon- cile theory with empirical observations. Indeed, recoveries outweigh the upward-sloping effect of time-varying disaster intensity and expected growth, generating downward-sloping term structures of dividend growth risk, equity return volatility, and equity risk premia. In addition, the term structure of interest rates is upward-sloping when accounting for recoveries and downward-sloping otherwise. The model quantitatively reconciles high risk premia and a low risk-free rate with the shape of the term structures, which are at odds in other models.
    Keywords: Recovery; Rare disasters; Term structures of equity; Dividend strips; Asset pricing puzzles
    JEL: D53 D51 E21 G12 G13
    Date: 2016
  11. By: Brummelhuis, Raymond; Luo, Zhongmin
    Abstract: Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. After testing 156 classifiers from 8 most popular classifier families, we found that some classifiers achieve highly satisfactory accuracy rates. Furthermore, we have rank-ordered the performances and investigated performance variations amongst and within the 8 classifier families. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data. Its findings both confirm and contrast existing classifier performance literature. Given the typically highly correlated nature of financial data, we investigated the impact of correlation on classifier performance. The techniques used in this paper should be of interest for financial institutions seeking a CDS Proxy method, and can serve for proxy construction for other financial variables. Some directions for future research are indicated.
    Keywords: Machine Learning; Counterparty Credit Risk; CDS Proxy Construction; Classification.
    JEL: B23 C1 C38 C4 C45 C58 C6
    Date: 2017–05–12

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