nep-rmg New Economics Papers
on Risk Management
Issue of 2017‒11‒19
twenty papers chosen by
Stan Miles
Thompson Rivers University

  1. Market Risk Management in a Post-Basel II Regulatory Environment By Branko Uroševic; Mikica Drenovak; Vladimir Rankovic; Ranko Jelic; Milos Ivanovic
  2. Efficient Simulation for Portfolio Credit Risk in Normal Mixture Copula Models By Cheng-Der Fuh; Chuan-Ju Wang
  3. Leverage and Risk Weighted Capital Requirements By Sudipto Karmakar; Leonardo Gambacorta
  4. A simple model for forecasting conditional return distributions By Stanislav Anatolyev; Jozef Barunik
  5. On the diversification benefit of reinsurance portfolios By Limani, Jeta; Bettinger, Régis; Dacorogna, Michel M
  6. How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory? By Benjamin R. Auer; Benjamin Mögel
  7. Realized volatility of CO2 futures By Thijs Benschop; Brenda López Cabrera;
  8. Taxation and Corporate Risk-Taking By Dominika Langenmayr; Rebecca Lester
  9. Bank Panics and Fire Sales, Insolvency and Illiquidity By T. R. Hurd
  10. Variance optimal hedging with application to Electricity markets By Xavier Warin
  11. An Overview on the Practice and Issues of Hedging in Islamic Finance By Oubdi, Lahsen; Raghibi, Abdessamad
  12. Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution By Catalina Bolancé; Raluca Vernic
  13. Firm-level political risk: Measurement and effects By Hassan, Tarek; Hollander, Stephan; Tahoun, Ahmed; van Lent, Laurence
  14. Financial Regulation in a Quantitative Model of the Modern Banking System By Begenau, Juliane; Landvoigt, Tim
  15. Determinants of Bank Capital in Dual Banking Systems By Mohammad Bitar; M. Kabir Hassan; William J. Hippler
  16. Farinelli and Tibiletti ratio and Stochastic Dominance By Niu, Cuizhen; Wong, Wing-Keung; Zhu, Lixing
  17. Local logit regression for recovery rate By Nithi Sopitpongstorn; Param Silvapulle; Jiti Gao
  18. Designing fan charts for GDP growth forecasts to better reflect downturn risks By David Turner
  19. Modelling and mitigation of Flash Crashes By Fry, John; Serbera, Jean-Philippe
  20. Portfolio Optimization and Model Predictive Control: A Kinetic Approach By Torsten Trimborn; Lorenzo Pareschi; Martin Frank

  1. By: Branko Uroševic; Mikica Drenovak; Vladimir Rankovic; Ranko Jelic; Milos Ivanovic
    Abstract: We propose a novel method of Mean-Capital Requirement portfolio optimization. The optimization is performed using a parallel framework for optimization based on the Nondominated Sorting Genetic Algorithm II. Capital requirements for market risk include an additional stress component introduced by the recent Basel 2.5 regulation. Our optimization with the Basel 2.5 formula in the objective function produces superior results to those of the old (Basel II) formula in stress scenarios in which the correlations of asset returns change considerably. These improvements are achieved at the expense of reduced cardinality of Pareto-optimal portfolios. This reduced cardinality (and thus portfolio diversification) in periods of relatively low market volatility may have unintended consequences for banks’ risk exposure.
    Keywords: finance, market risk, Basel 2.5, GARCH, NSGA-II
    JEL: C01
    Date: 2016
  2. By: Cheng-Der Fuh; Chuan-Ju Wang
    Abstract: This paper considers the problem of measuring the credit risk in portfolios of loans, bonds, and other instruments subject to possible default. One such performance measure of interest is the probability that the portfolio incurs large losses over a fixed time horizon. To capture the extremal dependence among obligors, we study a multi-factor model with a normal mixture copula that allows the multivariate defaults to have an asymmetric distribution. Due to the amount of the portfolio, the heterogeneous effect of obligors, and the phenomena that default events are rare and mutually dependent, it is difficult to calculate portfolio credit risk either by means of direct analysis or crude Monte Carlo simulation. That motivates this study on efficient simulation. To this end, we first propose a general account of an importance sampling algorithm based on a two-parameter exponential embedding. Note that this innovative tilting device is more suitable for the multivariate normal mixture model and is of independent interest. Next, by utilizing a fast computational method for how the rare event occurs and the proposed importance sampling method, we provide an efficient simulation algorithm to estimate the probability that the portfolio incurs large losses under the normal mixture copula. Here our proposed simulation device is based on importance sampling for a joint probability other than the conditional probability used in previous studies. Theoretical investigations and simulation studies are given to illustrate the method.
    Date: 2017–11
  3. By: Sudipto Karmakar; Leonardo Gambacorta
    Abstract: The global financial crisis has highlighted the limitations of risk-sensitive bank capital ratios. To tackle this problem, the Basel III regulatory framework has introduced a minimum leverage ratio, defined as a banks Tier 1 capital over an exposure measure, which is independent of risk assessment. Using a medium sized DSGE model that features a banking sector, financial frictions and various economic agents with differing degrees of creditworthiness, we seek to answer three questions: 1) How does the leverage ratio behave over the cycle compared with the risk-weighted asset ratio? 2) What are the costs and the benefits of introducing a leverage ratio, in terms of the levels and volatilities of some key macro variables of interest? 3) What can we learn about the interaction of the two regulatory ratios in the long run? The main answers are the following: 1) The leverage ratio acts as a backstop to the risk-sensitive capital requirement: it is a tight constraint during a boom and a soft constraint in a bust; 2) the net benefits of introducing the leverage ratio could be substantial; 3) the steady state value of the regulatory minima for the two ratios strongly depends on the riskiness and the composition of bank lending portfolios
    Keywords: Bank Capital Buffers, Regulation, Risk-Weighted Assets, Leverage
    JEL: G21 G28 G32
    Date: 2017–10
  4. By: Stanislav Anatolyev; Jozef Barunik
    Abstract: This paper presents a simple approach to forecasting conditional probability distributions of asset returns. We work with a parsimonious parametrization of ordered binary choice regression that quite precisely forecasts future conditional probability distributions of returns, using past indicator and past volatility proxy as predictors. Direct benefits of the proposed model are revealed in the empirical application to 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. The model can therefore serve as useful risk management tool for investors monitoring tail risk, or even building trading strategies based on the entire conditional return distribution. Our approach can also be useful in many other applications where conditional distribution forecasts are desired.
    Date: 2017–11
  5. By: Limani, Jeta; Bettinger, Régis; Dacorogna, Michel M
    Abstract: In this paper we compare the diversification benefit of portfolios containing excess-of-loss treaties and portfolios containing quota-share treaties, when the risk measure is the (excess) Value-at-Risk or the (excess) Expected Shortfall. In a first section we introduce the set-up under which we perform our investigations. Then we show that when the losses are continuous, independent, bounded, the cover unlimited and when the risk measure is the Expected Shortfall at a level alpha close to 1, a portfolio of n excess-of-loss treaties diversifies better than a comparable portfolio of n quota-share treaties. This result extends to the other risk measures under additional assumptions. We further provide evidence that the boundedness assumption is not crucial by deriving analytical formulas in the case of treaties with i.i.d. exponentially distributed original losses. Finally we perform the comparison in the more general setting of arbitrary continuous joint loss distributions and observe in that case that a finite cover leads to opposite results, i.e. a portfolio of n quota-share treaties diversifies better than a comparable portfolio of n excess-of-loss treaties at high quantile levels.
    Keywords: Diversification benefit, risk measures, portfolio, excess-of-loss treaties
    JEL: C00 C02 C18
    Date: 2017–10–30
  6. By: Benjamin R. Auer; Benjamin Mögel
    Abstract: In this study, we compare the out-of-sample forecasting performance of several modern Value-at- Risk (VaR) estimators derived from extreme value theory (EVT). Specifically, in a multi-asset study covering 30 years of stock, bond, commodity and currency market data, we analyse the accuracy of the classic generalised Pareto peak over threshold approach and three recently proposed methods based on the Box-Cox transformation, L-moment estimation and the Johnson system of distributions. We find that, in their unconditional form, some of the estimators are acceptable under current regulatory assessment rules but none of them can continuously pass more advanced tests of forecasting accuracy. In their conditional forms, forecasting power is significantly increased and the Box-Cox method proves to be the most promising estimator. However, it is also important to stress that the traditional historical simulation approach, which is currently the most frequently used VaR estimator in commercial banks, can not only keep up with the EVT-based methods but occasionally even outperforms them (depending on the setting: unconditional vs. conditional). Thus, recent claims to generally replace this simple method by theoretically more advanced EVT-based methods may be premature.
    Keywords: value-at-risk, extreme value theory, historical simulation, backtest, financial crisis
    JEL: G10 G11 G17
    Date: 2016
  7. By: Thijs Benschop; Brenda López Cabrera;
    Abstract: The EU Emission Trading System (EU ETS) was created to reduce the CO2 and other greenhouse gas emissions at the lowest economic cost. In reality market participants are faced with considerable uncertainty due to price changes and require price and volatility estimates and forecasts for appropriate risk management, asset allocation and volatility trading. Although the simplest approach to estimate volatility is to use the historical standard deviation, realized volatility is a more accurate measure for volatility, since it is based on intraday data. Besides the stylized facts commonly observed in financial time series, we observe long-memory properties in the realized volatility series, which motivates the use of Heterogeneous Autoregressive (HAR) class models. Therefore, we propose to model and forecast the realized volatility of the EU ETS futures with HAR class models. The HAR models outperform benchmark models such as the standard long-memory ARFIMA model in terms of model fit, in-sample and out-of-sample forecasting. The analysis is based on intraday data (May 2007-April 2012) for futures on CO2 certificates for the second EU-ETS trading period (expiry December 2008-2012). The estimation results of the models allow to explain the volatility drivers in the market and volatility structure, according to the Heterogeneous Market Hypothesis as well as the observed asymmetries. We see that both speculators with short investment horizons as well as traders taking long-term hedging positions are active in the market. In a simulation study we test the suitability of the HAR model for option pricing and conclude that the HAR model is capable of mimicking the long-term volatility structure in the futures market and can be used for short-term and long-term option pricing.
    Keywords: EU ETS, Realized Volatility, HAR, Volatility Forecasting, Intraday Data, CO2 Emission Allowances, Emissions Markets, Asymmetry, SHAR, HARQ, MC Simulation JEL Classification: C00
    JEL: C00
    Date: 2017–08
  8. By: Dominika Langenmayr; Rebecca Lester
    Abstract: We study whether the corporate tax system provides incentives for risky firm investment. We analytically and empirically show two main findings: first, risk-taking is positively related to the length of tax loss periods because the loss rules shift some risk to the government; and second, the tax rate has a positive effect on risk-taking for firms that expect to use losses, and a weak negative effect for those that cannot. Thus, the sign of the tax effect on risky investment hinges on firm-specific expectations of future loss recovery.
    Keywords: corporate taxation, risk-taking, net operating losses
    JEL: H25 H32 G32
    Date: 2017
  9. By: T. R. Hurd
    Abstract: Banking system crises are complex events that in a short span of time can inflict extensive damage to banks themselves and to the external economy. The crisis literature has so far identified a number of distinct effects or channels that can propagate distress contagiously both directly within the banking network itself and indirectly, between the network and the external economy. These contagious effects, and the potential events that trigger these effects, can explain most aspects of past crises, and are thought to be likely to dominate future financial crises. Since the current international financial regulatory regime based on the Basel III Accord does a good job of ensuring that banks are resilient to such contagion effects taken one at a time, systemic risk theorists increasingly understand that future crises are likely to be dominated by the spillovers between distinct contagion channels. The present paper aims to provide a model for systemic risk that is comprehensive enough to include the important contagion channels identified in the literature. In such a model one can hope to understand the dangerous spillover effects that are expected to dominate future crises. To rein in the number and complexity of the modelling assumptions, two requirements are imposed, neither of which is yet well-known or established in the main stream of systemic risk research. The first, called stock-flow consistency, demands that the financial system follows a rigorous set of rules based on accounting principles. The second requirement, called Asset-Liability symmetry, implies that every proposed contagion channel has a dual channel obtained by interchanging assets and liabilities, and that these dual channel pairs have a symmetric mathematical representation.
    Date: 2017–11
  10. By: Xavier Warin
    Abstract: In Electricity markets, illiquidity, transaction costs and market price characteristics prevent managers to replicate exactly contracts. A residual risk is always present and the hedging strategy depends on a risk criterion chosen. We present an algorithm to hedge a position for a mean variance criterion taking into account the transaction cost and the small depth of the market. We show its effectiveness on a typical problem coming from the field of electricity markets.
    Date: 2017–11
  11. By: Oubdi, Lahsen; Raghibi, Abdessamad
    Abstract: In terms of Islamic banking, which relies on three main pillar of prohibiting Riba, Gharar& Maysir, risk management is still not sufficiently developed. Indeed, Islamic use traditional types of risk management instruments used by conventional banks. Despite Islamic banking has a specific characteristic related essentially to the Profit & Loss Sharing (PLS) principle in Mudarabah & Musharakah contracts. Such instruments change the classic concept of risk in comparison with conventional banks adding new types of risk such as Commodity/Asset price risk and Bundled risk along with neutralizing other type of risks like the liquidity risk. Nevertheless, the rigidity of some Sharia’a scholars has impeded some financial instruments that try to match the real risk management demands by business entities in the global Islamic finance industry. Indeed, it has been widely acknowledged by many observers that the Islamic finance industry will not be able to sustainably continue on this growth trajectory, and may even regress, without a proper market risk management framework that can effectively deal with the complex risks that exist in today’s globalized economy (Chapra& Khan, 2000; Moody's, 2010).The present research article is an attempt to analyze hedging instruments from an Islamic finance perspective. It will approach different fiqhi ruling on them along with the applicability of these instruments in the reality.
    Keywords: Risk Management, Islamic Finance, Hedging, Future
    JEL: G13 G23
    Date: 2017–10–13
  12. By: Catalina Bolancé (RISKCENTER, Universitat de Barcelona); Raluca Vernic (Faculty of Mathematics and Informatics, Ovidius University of Constanta)
    Abstract: Starting from the question: “What is the accident risk of an insured?”, this paper considers a multivariate approach by taking into account three types of accident risks and the possible dependence between them. Driven by a real data set, we propose three trivariate Sarmanov distributions with generalized linear models (GLMs) for marginals and incorporate various individual characteristics of the policyholders by means of explanatory variables. Since the data set was collected over a longer time period (10 years), we also added each individual’s exposure to risk. To estimate the parameters of the three Sarmanov distributions, we analyze a pseudo-maximumlikelihood method. Finally, the three models are compared numerically with the simpler trivariate Negative Binomial GLM.
    Keywords: multivariate counting distribution, Sarmanov distribution, Negative Binomial distribution, Generalized Linear Model, ML estimation algorithm
    Date: 2017–11
  13. By: Hassan, Tarek; Hollander, Stephan; Tahoun, Ahmed; van Lent, Laurence
    Abstract: We adapt simple tools from computational linguistics to construct a new measure of political risk faced by individual US firms: the share of their quarterly earnings conference calls that they devote to political risks. We validate our measure by showing that it correctly identifies calls containing extensive conversations on risks that are political in nature, that it varies intuitively over time and across sectors, and that it correlates with the firm's actions and stock market volatility in a manner that is highly indicative of political risk. Firms exposed to political risk retrench hiring and investment and actively lobby and donate to politicians. Interestingly, we find that the incidence of political risk across firms is far more heterogeneous and volatile than previously thought. The vast majority of the variation in our measure is at the firm-level rather than at the aggregate or sector-level, in the sense that it is neither captured by time fixed effects and the interaction of sector and time fixed effects, nor by heterogeneous exposure of individual firms to aggregate political risk. The dispersion of this firm-level political risk increases significantly at times with high aggregate political risk. Decomposing our measure of political risk by topic, we find that firms that devote more time to discussing risks associated with a given political topic tend to increase lobbying on that topic, but not on other topics, in the following quarter.
    Keywords: firm-level; Lobbying; Political uncertainty; quantification
    JEL: D80 E22 G18 G38 H32
    Date: 2017–11
  14. By: Begenau, Juliane (Harvard University); Landvoigt, Tim (University of TX)
    Abstract: How does the shadow banking system respond to changes in the capital regulation of commercial banks? We propose a tractable, quantitative general equilibrium model with regulated and unregulated banks to study the unintended consequences of regulatory policy. Tightening the capital requirement from the status quo creates a safer banking system despite more shadow banking activity. A reduction in aggregate liquidity provision decreases the funding costs of all banks, raising profits and reducing risk-taking incentives. Calibrating the model to data on financial institutions in the U.S., we find the optimal capital requirement is around 15%.
    Date: 2017–04
  15. By: Mohammad Bitar; M. Kabir Hassan; William J. Hippler
    Abstract: We report new evidence on the bank and country-level determinants of Islamic bank capital ratios in 28 countries between 1999 and 2013. Overall, we find that smaller, more profitable, and highly liquid Islamic banks are more highly capitalized. Additionally, improvements in the economic and financial environments and market discipline within a country correspond with higher Islamic bank capitalization. The results shed light on the impact that Sharia’a law restrictions have on Islamic banking capitalization. Our findings are most robust to banks that choose to hold capital well in excess of that required by regulators, consistent with traditional capital structure theory. Our results highlight the role that stable economic and political systems play in improving bank capitalization and reducing financial sector risk. By reducing political instability and corruption, improving legal systems, and encouraging access to capital markets, policymakers may incentivize mangers to make financing decisions that increase the capitalization of the Islamic banking industry in developing countries.
    Keywords: bank capitalization, Islamic banking, institutional environment, political distress, market discipline, democracy
    JEL: G24 G32 K22
    Date: 2017–10
  16. By: Niu, Cuizhen; Wong, Wing-Keung; Zhu, Lixing
    Abstract: Farinelli and Tibiletti (F-T) ratio, a general risk-reward performance measurement ratio, is popular due to its simplicity and yet generality that both Omega ratio and upside potential ratio are its special cases. The F-T ratios are ratios of average gains to average losses with respect to a target, each raised by a power index, p and q. In this paper, we establish the consistency of F-T ratios with any nonnegative values p and q with respect to first-order stochastic dominance. Second-order stochastic dominance does not lead to F-T ratios with any nonnegative values p and q, but can lead to F-T dominance with any p
    Keywords: First-order Stochastic Dominance, High-order Stochastic Dominance, Upside Potential Ratio, Farinelli and Tibiletti ratio, Risk Measures.
    JEL: C0 D81 G10
    Date: 2017–11–16
  17. By: Nithi Sopitpongstorn; Param Silvapulle; Jiti Gao
    Abstract: We propose a flexible and robust nonparametric local logit regression for modelling and predicting defaulted loans' recovery rates that lie in [0,1]. Applying the model to the widely studied Moody's recovery dataset and estimating it by a data-driven method, the local logit regression uncovers the underlying nonlinear relationship between the recovery and covariates, which include loan/borrower characteristics and economic conditions. We find some significant nonlinear marginal and interaction effects of conditioning variables on recoveries of defaulted loans. The presence of such nonlinear economic effects enriches the local logit model specification that supports the improved recovery prediction. This paper is the first to study a nonparametric regression model that not only generates unbiased and improved recovery predictions of defaulted loans relative to the parametric counterpart, it also facilitates reliable inference on marginal and interaction effects of loan/borrower characteristics and economic conditions. Moreover, incorporating these nonlinear marginal and interaction effects, we improve the specification of parametric regression for fractional response variable, which we call "calibrated" model, the predictive performance of which is comparable to that of local logit model. This calibrated parametric model will be attractive to applied researchers and industry professionals working in the risk management area and unfamiliar with nonparametric machinery.
    Keywords: Loss given default, credit risk, nonlinearity, kernel estimation, defaulted debt, simulation.
    JEL: C14 C53 G02 G32
    Date: 2017
  18. By: David Turner (OECD)
    Abstract: Forecasts of GDP growth are typically over-optimistic for horizons beyond the current year, particularly because they fail to predict the occurrence or severity of future downturns. Macroeconomic forecasters have also long been under pressure to convey the uncertainty surrounding their forecasts, particularly since the financial crisis. The current paper proposes a method to address both these issues simultaneously by constructing fan charts which are parameterised on the basis of the historical forecasting track record, but distinguish between a "safe" regime and a "downturn-risk" regime. To identify the two regimes, use is made of recent OECD work on early warning indicators of a prospective downturn, relating to housing market or credit developments. Thus, when an early warning indicator is “flashing", the associated fan chart is not only wider to reflect increased uncertainty, but is also skewed to reflect greater downside risks using a two-piece normal distribution of the form used by central banks to provide fan charts around inflation forecasts. Conversely, in a safe regime, when the early warning indicators are not flashing, as well as being symmetric, the fan chart is narrower both relative to the downturn-risk regime and relative to what the fan chart would be if the dispersion was calculated with respect to the entire forecast track record with no distinction between regimes. The method is illustrated by reference to OECD GDP forecasts for the major seven economies made just prior to the global financial crisis, with fan charts calibrated using the track record of forecasts published in the OECD Economic Outlook. Fan charts which take account of early warning indicators in this way are much better at encapsulating the outturns associated with a downturn than a symmetrical fan chart calibrated indiscriminately on all forecast errors.
    Keywords: downturn, economic forecasts, Fan charts, risk, uncertainty
    JEL: E17 E58 E65 E66 G01
    Date: 2017–11–17
  19. By: Fry, John; Serbera, Jean-Philippe
    Abstract: The algorithmic trading revolution has had a dramatic effect upon markets. Trading has become faster, and in some ways more efficient, though potentially at the cost higher volatility and increased uncertainty. Stories of predatory trading and flash crashes constitute a new financial reality. Worryingly, highly capitalised stocks may be particularly vulnerable to flash crashes. Amid fears of high-risk technology failures in the global financial system we develop a model for flash crashes. Though associated with extreme forms of illiquidity and market concentration flash crashes appear to be unpredictable in advance. Several measures may mitigate flash crash risk such as reducing the market impact of individual trades and limiting the profitability of high-frequency and predatory trading strategies.
    Keywords: Flash Crashes; Flash Rallies; Econophysics; Regulation
    JEL: C1 F3 G1 K2
    Date: 2017–09–12
  20. By: Torsten Trimborn; Lorenzo Pareschi; Martin Frank
    Abstract: In this paper, we introduce a large system of interacting financial agents in which each agent is faced with the decision of how to allocate his capital between a risky stock or a risk-less bond. The investment decision of investors, derived through an optimization, drives the stock price. The model has been inspired by the econophysical Levy-Levy-Solomon model (Economics Letters, 45). The goal of this work is to gain insights into the stock price and wealth distribution. We especially want to discover the causes for the appearance of power-laws in financial data. We follow a kinetic approach similar to (D. Maldarella, L. Pareschi, Physica A, 391) and derive the mean field limit of our microscopic agent dynamics. The novelty in our approach is that the financial agents apply model predictive control (MPC) to approximate and solve the optimization of their utility function. Interestingly, the MPC approach gives a mathematical connection between the two opponent economic concepts of modeling financial agents to be rational or boundedly rational. We derive a moment model which is able to replicate the most prominent features of the financial markets: oscillatory price behavior, booms and crashes. Due to our kinetic approach, we can study the wealth and price distribution on a mesoscopic level. The wealth distribution is characterized by a lognormal law. For the stock price distribution, we can either observe a lognormal behavior in the case of long-term investors or a power-law in the case of high-frequency trader. Furthermore, the stock return data exhibits a fat-tail, which is a well known characteristic of real financial data.
    Date: 2017–11

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