New Economics Papers
on Risk Management
Issue of 2010‒04‒17
sixteen papers chosen by



  1. Exposure at Default Model for Contingent Credit Line By Bag, Pinaki
  2. Measuring systemic risk By Viral V. Acharya; Lasse H. Pedersen; Thomas Philippon; Matthew Richardson
  3. The term structure of risk premia - new evidence from the financial crisis By Tobias Berg
  4. Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory By Julia Schaumburg
  5. The Alleviation of Coordination Problems through Financial Risk Management By BOYER, Marcel; BOYER, Martin M.; GARCIA, René
  6. Improving the Management of the Crown’s Exposure to Risk By Timothy Irwin; Oscar Parkyn
  7. Outliers in Garch models and the estimation of risk measures By Aurea Grané; Helena Veiga
  8. Precautionary Measures for Credit Risk Management in Jump Models By Masahiko Egami; Kazutoshi Yamazaki
  9. Simple Fuzzy Score for Russian Public Companies Risk of Default By Sergey Ivliev
  10. Time Varying Risk Aversion: An Application to Energy Hedging By John Cotter; Jim Hanly
  11. Managing Derivative Exposure By Ulrich Kirchner
  12. Residential mortgage default: the roles of house price volatility, euphoria and the borrower's put option By Wayne R. Archer; Brent C. Smith
  13. Prudential discipline for financial firms: micro, macro, and market structures By Larry D. Wall
  14. Inequalities for the ruin probability in a controlled discrete-time risk process By Maikol Diasparra; Rosario Romera
  15. Shortfall Risk Approximations for American Options in the multidimensional Black--Scholes Model By Yan Dolinsky
  16. Relevance of Risk Capital and Margining for the Valuation of Power Plants: Cash Requirements for Credit Risk Mitigation By Lang, Joachim; Madlener, Reinhard

  1. By: Bag, Pinaki
    Abstract: In-spite of large volume of Contingent Credit Lines (CCL) in all commercial banks paucity of Exposure at Default (EAD) models, unsuitability of external data and inconsistent internal data with partial draw-down, has been a major challenge for risk managers as well as regulators for managing CCL portfolios. Current paper is an attempt to build an easy to implement, pragmatic and parsimonious yet accurate model to determine exposure distribution of a CCL portfolio. Each of the credit line in a portfolio is modeled as a portfolio of large number of option instrument which can be exercised by the borrower determining the level of usage. Using an algorithm similar to basic CreditRisk+ and Fourier Transforms we arrive at a portfolio level probability distribution of usage.
    Keywords: EAD; Basel II; Credit Risk; Contingent credit line (CCL)
    JEL: C13 G21 G20
    Date: 2010–04–01
    URL: http://d.repec.org/n?u=RePEc:pra:mprapa:20387&r=rmg
  2. By: Viral V. Acharya; Lasse H. Pedersen; Thomas Philippon; Matthew Richardson
    Abstract: We present a simple model of systemic risk and show how each financial institution’s contribution to systemic risk can be measured and priced. An institution’s contribution, denoted systemic expected shortfall (SES), is its propensity to be undercapitalized when the system as a whole is undercapitalized, which increases in its leverage, volatility, correlation, and tail-dependence. Institutions internalize their externality if they are “taxed” based on their SES. Through several examples, we demonstrate empirically the ability of components of SES to predict emerging systemic risk during the nancial crisis of 2007-2009.
    Keywords: Systemic risk ; Risk
    Date: 2010
    URL: http://d.repec.org/n?u=RePEc:fip:fedcwp:1002&r=rmg
  3. By: Tobias Berg (Technische Universität München, Department of Financial Management and Capital Markets, Arcisstr. 21, 80290 Munich, Germany.)
    Abstract: This study calibrates the term structure of risk premia before and during the 2007/2008 financial crisis using a new calibration approach based on credit default swaps. The risk premium term structure was flat before the crisis and downward sloping during the crisis. The instantaneous risk premium increased significantly during the crisis, whereas the long-run mean of the risk premium process was of the same magnitude before and during the crisis. These findings suggest that (marginal) investors have become more risk averse during the crisis. Investors were, however, well aware that risk premia will revert back to normal levels in the long run. JEL Classification: G12, G13.
    Keywords: credit risk, risk premia, equity premium, mean reversion, structural models of default.
    Date: 2010–03
    URL: http://d.repec.org/n?u=RePEc:ecb:ecbwps:20101165&r=rmg
  4. By: Julia Schaumburg
    Abstract: This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, where particularly few data points are available, we propose to combine nonparametric quantile regression with extreme value theory. The out-of-sample forecasting performance of our methods turns out to be clearly superior to different specifications of the Conditionally Autoregressive VaR (CAViaR) models.
    Keywords: Value at Risk, nonparametric quantile regression, risk management, extreme value theory, monotonization, CAViaR
    JEL: C14 C22 C52 C53
    Date: 2010–02
    URL: http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2010-009&r=rmg
  5. By: BOYER, Marcel; BOYER, Martin M.; GARCIA, René
    Abstract: We characterize a firm as a nexus of activities and projects with their associated cashflow distributions across states of the world and time. With specialized managers intent on maximizing firm value, we show that such a representation leads to a transformation possibility frontier between the riskiness and expected value of cashflows. A firm reacts to changes in the market prices of risks by adjusting its value maximizing portfolio of real activities. We show that financial risk management can help to alleviate the reorganization and coordination problems related to the implementation of the desired adjustments. Empirically, we show that a firm's use of financial derivatives is linked to its reactivity to variations in risk prices. We also argue that financial risk management allows a firm to maintain its value in the presence of cashflow-at-risk or value-at-risk constraints.
    Keywords: Risk Management, Firm Value, Hedging, Value at Risk
    JEL: G22 G31 G34
    Date: 2010
    URL: http://d.repec.org/n?u=RePEc:mtl:montec:06-2010&r=rmg
  6. By: Timothy Irwin; Oscar Parkyn (The Treasury)
    Abstract: The paper discusses the management of the New Zealand Crown’s exposure to financial risk. It argues that the Crown’s aggregate exposure to risk can be effectively managed only centrally, and that, despite the difficulties of measuring risk and specifying an appropriate objective, the government should do more to measure, monitor, and control the Crown’s aggregate exposure to risk. The paper goes on to present a new model for quantifying the Crown’s exposure to risk, which integrates analysis of the government’s accounting assets and liabilities with analysis of projected tax revenue and government spending. Among other results, the model suggests that the annual volatility (standard deviation) of the Crown’s comprehensive balance sheet is at present approximately $30 billion.
    Keywords: Risk management; Crown balance sheet
    JEL: G32 H11
    Date: 2009–12
    URL: http://d.repec.org/n?u=RePEc:nzt:nztwps:09/06&r=rmg
  7. By: Aurea Grané; Helena Veiga
    Abstract: In this paper we focus on the impact of additive level outliers on the calculation of risk measures, such as minimum capital risk requirements, and compare four alternatives of reducing these measures' estimation biases. The first three proposals proceed by detecting and correcting outliers before estimating these risk measures with the GARCH(1,1) model, while the fourth procedure fits a Student’s t-distributed GARCH(1,1) model directly to the data. The former group includes the proposal of Grané and Veiga (2010), a detection procedure based on wavelets with hard- or soft-thresholding filtering, and the well known method of Franses and Ghijsels (1999). The first results, based on Monte Carlo experiments, reveal that the presence of outliers can bias severely the minimum capital risk requirement estimates calculated using the GARCH(1,1) model. The message driven from the second results, both empirical and simulations, is that outlier detection and filtering generate more accurate minimum capital risk requirements than the fourth alternative. Moreover, the detection procedure based on wavelets with hard-thresholding filtering gathers a very good performance in attenuating the effects of outliers and generating accurate minimum capital risk requirements out-of-sample, even in pretty volatile periods
    Keywords: Minimum capital risk requirements, Outliers, Wavelets
    JEL: C22 C5 G13
    Date: 2010–01
    URL: http://d.repec.org/n?u=RePEc:cte:wsrepe:ws100502&r=rmg
  8. By: Masahiko Egami; Kazutoshi Yamazaki
    Abstract: Sustaining efficiency and stability by properly controlling the equity to asset ratio is one of the most important and difficult challenges in bank management. Due to unexpected and abrupt decline of asset values, a bank must closely monitor its net worth as well as market conditions, and one of its important concerns is when to raise more capital so as not to violate capital adequacy requirements. In this paper, we model the tradeoff between avoiding costs of delay and premature capital raising, and solve the corresponding optimal stopping problem. In order to model defaults in a bank's loan/credit business portfolios, we represent its net worth by appropriate Levy processes, and solve explicitly for the double exponential jump diffusion process. In particular, for the spectrally negative case, we generalize the formulation using the scale function, and obtain explicitly the optimal solutions for the exponential jump diffusion process.
    Date: 2010–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1004.0595&r=rmg
  9. By: Sergey Ivliev
    Abstract: The model is aimed to discriminate the 'good' and the 'bad' companies in Russian corporate sector based on their financial statements data (Russian Accounting Standards). The data sample consists of 126 Russian public companies- issuers of Ruble bonds which represent about 36% of total number of corporate bonds issuers. 25 companies have defaulted on their debt in 2008-2009 which represent around 30% of default cases. 29% companies in the sample have credit ratings assigned compared to 34% in the parent population. No SPV companies were included in the sample. The model shows in-sample Gini AR about 73% and gives a reasonable mapping to external ratings. The model can be used to calculate implied credit rating for Russian companies which many of them don't have one.
    Date: 2010–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1004.0685&r=rmg
  10. By: John Cotter (School of Business, University College Dublin); Jim Hanly (School of Accounting and Finance, Dublin Institute of Technology)
    Abstract: Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hedging market participants. The resulting estimates are applied to derive explicit risk aversion based optimal hedge strategies for both short and long hedgers. Out-of-sample results are also presented based on a unique approach that allows us to forecast risk aversion, thereby estimating hedge strategies that address the potential future needs of energy hedgers. We find that the risk aversion based hedges differ significantly from simpler OLS hedges. When implemented in-sample, risk aversion hedges for short hedgers outperform the OLS hedge ratio in a utility based comparison.
    Keywords: Energy, Hedging, Risk Management, Risk Aversion, Forecasting
    JEL: G10 G12 G15
    Date: 2010–01–01
    URL: http://d.repec.org/n?u=RePEc:ucd:wpaper:201007&r=rmg
  11. By: Ulrich Kirchner
    Abstract: We present an approach to derivative exposure management based on subjective and implied probabilities. We suggest to maximize the valuation difference subject to risk constraints and propose a class of risk measures derived from the subjective distribution. We illustrate this process with specific examples for the two and three dimensional case. In these cases the optimization can be performed graphically.
    Date: 2010–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1004.1053&r=rmg
  12. By: Wayne R. Archer; Brent C. Smith
    Abstract: House price volatility; lender and borrow perception of price trends, loan and property features; and the borrower’s put option are integrated in a model of residential mortgage default. These dimensions of the default problem have, to our knowledge, not previously been considered altogether within the same investigation framework. We rely on a sample of individual mortgage loans for twenty counties in Florida, over the period 2001 through 2008, third quarter, with housing price performance obtained from repeat sales analysis of individual transactions. The results from the analysis strongly confirm the significance of the borrower’s put as an operative factor in default. At the same time, the results provide convincing evidence that the experience in Florida is in part driven by lenders and purchasers exhibiting euphoric behavior such that in markets with higher price appreciation there is a willingness to accept recent prior performance as an indicator of future risk. This connection illustrates a familiar moral hazard in the housing market due to the limited information about future prices.
    Date: 2010
    URL: http://d.repec.org/n?u=RePEc:fip:fedrwp:10-02&r=rmg
  13. By: Larry D. Wall
    Abstract: The recent global financial crisis reflects numerous breakdowns in the prudential discipline of financial firms. This paper discusses ways to strengthen micro- and macroprudential supervision and restore credible market discipline. The discussion notes that microprudential supervisors are typically assigned a variety of goals that sometimes have conflicting policy implications. In such a setting, the structure of the regulatory agencies and the priority given to prudential goals are critical to achieving those goals. ; The analysis of macroprudential supervision emphasizes that this supervisor must be both bold and modest: bold in seeking to understand the sources and distributions of systemically important risks and modest about what a supervisor can do without imposing overly restrictive regulations. ; Finally, the paper argues that the primary responsibility for risk management must rest with firms, not government supervisors. Unfortunately, systemic risk concerns have led governments to shield the private sector from the full losses that dull their incentive to discipline risk taking. This section of the paper suggests that deposit insurance reform, special resolutions for systemically important firms, and requirements that firms plan for their own resolution and contingent capital may all have a role to play in restoring effective market discipline.
    Date: 2010
    URL: http://d.repec.org/n?u=RePEc:fip:fedawp:2010-09&r=rmg
  14. By: Maikol Diasparra; Rosario Romera
    Abstract: Ruin probabilities in a controlled discrete-time risk process with a Markov chain interest are studied. To reduce the risk there is a possibility to reinsure a part or the whole reserve. Recursive and integral equations for ruin probabilities are given. Generalized Lundberg inequalities for the ruin probabilities are derived given a constant stationary policy. The relationships between these inequalities are discussed. To illustrate these results some numerical examples are included.
    Keywords: Risk process, Ruin probability, Proportional reinsurance, Lundberg`s
    Date: 2009–06
    URL: http://d.repec.org/n?u=RePEc:cte:wsrepe:ws093513&r=rmg
  15. By: Yan Dolinsky
    Abstract: We show that shortfall risks of American options in a sequence of multinomial approximations of the multidimensional Black--Scholes (BS) market converge to the corresponding quantities for similar American options in the multidimensional BS market with path dependent payoffs. In comparison to previous papers we consider the multi assets case for which we use the weak convergence approach.
    Date: 2010–04
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1004.1574&r=rmg
  16. By: Lang, Joachim (E.ON AG, Controlling / Corporate Planning); Madlener, Reinhard (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))
    Abstract: In the electricity sector, most of the trades are still done in the OTC market without direct mitigation of credit risk. Newer discussions fuelled by the European Commission (EUCOM 2009a,b) show that there is a political will to enforce a stronger collateralization policy on all European derivatives markets, including the OTC markets. This is meant to secure the markets and prohibit arbitrage on regulatory regimes as a consequence of the financial crisis. However, collateralization does not come for free. In this study, we analyze the capital needs for margining based on commodity prices of 2007-2009 in conjunction with the clearing rules for margining of the European Commodity Clearing AG (ECC). We apply different hedging scenarios to state-of-the-art coal- and gas-fired power plants, and the sale of outright power in the German market. Based on the set-up of our analysis, we show that in absolute terms especially outright power has quite significant cash needs for trading, whereas coaland gas-fired power plants have less than half the needs of outright power. In relative terms for the fossil-fired power plants, we find that coal-fired power plants have a relative advantage in comparison to gas-fired plants. The need for risk capital per MWhth of coal-fired power plants is comparably lower. A major reason for this is the standard notation of coal in US-$ per ton: As one ton of coal contains approx. 7 MWh of thermal energy (which is the relevant unit for the calculation of the fuel consumption), the price change for coal in US-$ (or €) per MWh is only approximately one seventh compared to the notation in metric tons. This translates into comparably lower margining needs for the fuel variation margin of a coal-fired power plant vs. a gas-fired power plant, offering a variety of further research questions.
    Keywords: Credit risk mitigation; margining; collateralization; risk capital; power plant valuation; portfolio optimization
    JEL: G12 G32 L94 O16
    Date: 2010–02
    URL: http://d.repec.org/n?u=RePEc:ris:fcnwpa:2010_001&r=rmg

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