nep-rmg New Economics Papers
on Risk Management
Issue of 2013‒02‒16
twelve papers chosen by
Stan Miles
Thompson Rivers University

  1. Risks of large portfolios By Fan, Jianqing; Liao, Yuan; Shi, Xiaofeng
  2. Risk Management in Agricultural Banks: An Application of Endogenous Switching Model By Shen, Xuan; Hartarska, Valentina M.
  3. Efficient Importance Sampling for Rare Event Simulation with Applications By Cheng-Der Fuh; Huei-Wen Teng; Ren-Her Wang
  4. CDS spreads and systemic risk: A spatial econometric approach By Keiler, Sebastian; Eder, Armin
  5. Measuring risk with ordinal variables By Silvia Figini; Paolo Giudici
  6. Stock Return Comovement and Systemic Risk in the Turkish Banking System By Mahir Binici; Bulent Koksal; Cuneyt Orman
  7. Default risk in business groups By Elisa Luciano; Giovanna Nicodano
  8. Have Farmers and Ranchers Lost Confidence in Futures Markets? By Amosson, Stephen H.; Anderson, David P.; Bevers, Stanley J.; Hogan, Robert J., Jr.; McCorkle, Dean A.; Robinson, John R.C.; Smith, Jackie; Waller, Mark L.; Welch, Mark; Williams, Emmy
  9. Relative risk aversion and power-law distribution of macroeconomic disasters By Michał Brzeziński
  10. Credit risk predictions with Bayesian model averaging By Silvia Figini; Paolo Giudici
  11. Proposed Farm Bill Impact On The Optimal Hedge Ratios For Crops By Tran, Trang; Coble, Keith H.; Harri, Ardian; Barnett, Barry J.; Riley, John Michael
  12. Robust Hedging with Proportional Transaction Costs By Yan Dolinsky; H. Mete Soner

  1. By: Fan, Jianqing; Liao, Yuan; Shi, Xiaofeng
    Abstract: Estimating and assessing the risk of a large portfolio is an important topic in financial econometrics and risk management. The risk is often estimated by a substitution of a good estimator of the volatility matrix. However, the accuracy of such a risk estimator for large portfolios is largely unknown, and a simple inequality in the previous literature gives an infeasible upper bound for the estimation error. In addition, numerical studies illustrate that this upper bound is very crude. In this paper, we propose factor-based risk estimators under a large amount of assets, and introduce a high-confidence level upper bound (H-CLUB) to assess the accuracy of the risk estimation. The H-CLUB is constructed based on three different estimates of the volatility matrix: sample covariance, approximate factor model with known factors, and unknown factors (POET, Fan, Liao and Mincheva, 2013). For the first time in the literature, we derive the limiting distribution of the estimated risks in high dimensionality. Our numerical results demonstrate that the proposed upper bounds significantly outperform the traditional crude bounds, and provide insightful assessment of the estimation of the portfolio risks. In addition, our simulated results quantify the relative error in the risk estimation, which is usually negligible using 3-month daily data. Finally, the proposed methods are applied to an empirical study.
    Keywords: High dimensionality; approximate factor model; unknown factors; principal components; sparse matrix; thresholding; risk management; volatility
    JEL: G11 C38 G32 C58
    Date: 2013–02
  2. By: Shen, Xuan; Hartarska, Valentina M.
    Abstract: This paper is prepared for SAEA 2013 conference
    Keywords: Risk management, agricultural bank, community bank, financial derivatives, Agricultural Finance, Risk and Uncertainty,
    Date: 2013
  3. By: Cheng-Der Fuh; Huei-Wen Teng; Ren-Her Wang
    Abstract: Importance sampling has been known as a powerful tool to reduce the variance of Monte Carlo estimator for rare event simulation. Based on the criterion of minimizing the variance of Monte Carlo estimator within a parametric family, we propose a general account for finding the optimal tilting measure. To this end, when the moment generating function of the underlying distribution exists, we obtain a simple and explicit expression of the optimal alternative distribution. The proposed algorithm is quite general to cover many interesting examples, such as normal distribution, noncentral $\chi^2$ distribution, and compound Poisson processes. To illustrate the broad applicability of our method, we study value-at-risk (VaR) computation in financial risk management and bootstrap confidence regions in statistical inferences.
    Date: 2013–02
  4. By: Keiler, Sebastian; Eder, Armin
    Abstract: This study applies a novel way of measuring, quantifying and modelling the systemic risk within the financial system. The magnitude of risk spill over effects is gauged by introducing a specific weighting scheme. This approach originally stems from spatial econometrics. The methodology allows for a decomposition of the credit spread into a systemic, systematic and idiosyncratic risk premium. We identify considerable risk spill overs due to the interconnectedness of the financial institutes in the sample. In stress tests, up to one fifth of the CDS spread changes are owing to financial contagion. These results also give an alternative explanation for the nonlinear relationship between a debtor's theoretical probability of default and the observed credit spreads - known as the credit spread puzzle. --
    Keywords: systemic risk,financial contagion,spatial econometrics,CDS spreads,government policy and regulation
    JEL: C21 G12 G18 G21
    Date: 2013
  5. By: Silvia Figini (Department of Economics and Management, University of Pavia); Paolo Giudici (Department of Economics and Management, University of Pavia)
    Abstract: In this paper we propose a novel approach to measure risks, when the data available are expressed in an ordinal scale. As a result we obtain a new index of risk bounded between 0 and 1, that leads to a risk ordering that is consistent with a stochastic dominance approach. The proposed measure, being non parametric, can be applied to a wide range of problems, where data are ordinal and where a point estimate of risk is needed. We also provide a method to calculate confidence intervals for the proposed risk measure, in a Bayesian non parametric framework. In order to evaluate the actual performance of what we propose, we analyse a database provided by a telecommunication company, with the final aim of measuring operational risks, starting from a self-assessment questionnaire.
    Keywords: Risk measurement, Ordinal variables, Operational risk
    Date: 2013–02
  6. By: Mahir Binici; Bulent Koksal; Cuneyt Orman
    Abstract: This paper investigates the evolution of systemic risk in the Turkish banking sector over the past two decades using comovement of banks’ stock returns as a systemic risk indicator. In addition, we explore possible determinants of systemic risk, the knowledge of which can be a useful input into effective macroprudential policymaking. Results show that the correlations between bank stock returns almost doubled in 2000s in comparison to 1990s. The correlations decreased somewhat after 2002 and increased again after the 2007-2009 financial crisis. Main determinants of systemic risk appear to be the market share of bank pairs, the amount of nonperforming loans, herding behavior of banks, and volatilities of macro variables including the exchange rate, U.S. T-bills, EMBI+, VIX, and MSCI emerging markets index.
    Keywords: Stock Returns, Comovement, Systemic Risk, Turkish Banking System
    JEL: C22 C58 G21 G32
    Date: 2013
  7. By: Elisa Luciano; Giovanna Nicodano
    Abstract: This paper analyzes how combining firms into either groups or conglomerates affects their credit standing, as measured by their de- fault probabilities, recovery rates and credit spreads. Each combina- tion offers protection against default to its affiliates, and issues debt to optimize the trade-off between tax gains and default costs. In a group, the probability of joint default turns out to be lower than that of both stand-alone firms and conglomerates. This is the bright side of credit risk in groups. The dark side is that affiliation depletes the credit worthiness of the subsidiary. Such results hold irrespective of cash- ow correlation, if affiliates are equal in size, but fade if the parent is larger.
    Keywords: credit risk, structural models, groups, mergers, parent- subsidiary.
    JEL: G32 G33 G34
    Date: 2013
  8. By: Amosson, Stephen H.; Anderson, David P.; Bevers, Stanley J.; Hogan, Robert J., Jr.; McCorkle, Dean A.; Robinson, John R.C.; Smith, Jackie; Waller, Mark L.; Welch, Mark; Williams, Emmy
    Abstract: Agricultural producers have used futures markets to manage price risk, confident that 1) cash and futures prices move together over time, 2) cash and futures prices converge as the contract approaches expiration, and 3) funds held in margin accounts as a performance bond were secure in segregated accounts at the brokerage firm. However, recent price and basis volatility, coupled with the financial events surrounding MF Global and Peregrine may have shaken some growers’ perceptions of the risks of hedging with futures and options. In order to assess the degree to which these circumstances in the futures markets have impacted the risk management strategies of agricultural producers, we surveyed the 1,015 graduates of the Texas A&M AgriLife Master Marketer program. This group represents a sample of farmers and ranchers, merchandisers, and lenders with training and experience in using futures and options for hedging. Besides collecting information on their current risk management practices, we measured respondent views on the importance of key new sources of risk. On a 1 to 7 Likert scale with 1=disagree, 7=agree, and 4=neutral, respondents indicated some agreement (4.87) that futures price volatility and margins are a serious impediment to using futures and options. Respondents were neutral (4.06) on margin account security as a serious impediment to futures and options. The questions touched a nerve with some farmers, as noted in the comments.
    Keywords: hedging, marketing, risk management education, price volatility, counter party risk, futures markets, margin account security, segregated funds, cash/futures market convergence, Marketing, G1,
    Date: 2013–01–18
  9. By: Michał Brzeziński (Faculty of Economic Sciences, University of Warsaw)
    Abstract: The coefficient of relative risk aversion (CRRA) is notoriously difficult to estimate. Recently, Barro and Jin (On the size distribution of macroeconomic disasters, Econometrica 2011; 79(3): 434–455) have come up with a new estimation approach that fits a power-law model to the tail of distribution of macroeconomic disasters. We show that their results can be successfully replicated using a more refined power-law fitting methodology and a more comprehensive data set.
    Keywords: coefficient of relative risk aversion, power-law modelling, macroeconomic disasters, replication, robust statistics
    JEL: D81 E32 C46
    Date: 2013
  10. By: Silvia Figini (Department of Economics and Management, University of Pavia); Paolo Giudici (Department of Economics and Management, University of Pavia)
    Abstract: Model uncertainty remains a challenge to researchers in different applications. When many competing models are available for estimation, and without enough guidance from theory, model averaging represents an alternative to model selection. Despite model averaging approaches have been present in statistics for many years, only recently they are starting to receive attention in applications. The Bayesian Model Averaging (BMA) approach sometimes can be difficult in terms of applicability, mainly because of the following reasons: firstly two types of priors need to be elicited and secondly the number of models under consideration in the model space is often huge, so that the computational aspects can be prohibitive. In this paper we show how Bayesian model averaging can be usefully employed to obtain a well calibrated model, in terms of predictive accuracy for credit risk problems. In this paper we shall investigate how BMA performs in comparison with classical and Bayesian (single) selected models using two real credit risk databases.
    Date: 2013–02
  11. By: Tran, Trang; Coble, Keith H.; Harri, Ardian; Barnett, Barry J.; Riley, John Michael
    Abstract: Revenue insurance with shallow loss protection for farmers has been introduced recently. A common attribute of most shallow loss proposals is that they would be area-revenue triggered. The impact on optimal hedge ratios of combining these shallow loss insurance proposals with deep loss farm-level insurance is examined. Since crop insurance, commodity programs and forward pricing are commonly used concurrently to manage crop revenue risk, the optimal combinations of these tools are explored. Numerical analysis in the presence of yield, basis and futures price variability is used to find the futures hedge ratio which maximizes the certainty equivalent of a risk averse producer. The results generally reveal a lower optimal hedge ratio with area-insurance than with individual insurance and show that STAX and ARC tend to slightly increase optimal hedge ratios.
    Keywords: crop insurance, simulation, hedging, Agricultural Finance, Farm Management, Risk and Uncertainty,
    Date: 2013
  12. By: Yan Dolinsky; H. Mete Soner
    Abstract: Duality for robust hedging with proportional transaction costs of path dependent European options is obtained in a discrete time financial market with one risky asset. Investor's portfolio consists of a dynamically traded stock and a static position in vanilla options which can be exercised at maturity. Only stock trading is subject to proportional transaction costs. The main theorem is duality between hedging and a Monge-Kantorovich type optimization problem. In this dual transport problem the optimization is over all the probability measures which satisfy an approximate martingale condition related to consistent price systems in addition to the usual marginal constraints.
    Date: 2013–02

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