New Economics Papers
on Risk Management
Issue of 2010‒06‒11
nine papers chosen by

  1. Risk Management of Precious Metals By Shawkat Hammoudeh; Farooq Malik; Michael McAleer
  2. An economic capital model integrating credit and interest rate risk in the banking book By Alessandri, Piergiorgio; Drehmann, Mathias
  3. The Impact of Credit Risk and Implied Volatility on Stock Returns By Florian Steiger
  4. A Loan Portfolio Model Subject to Random Liabilities and Systemic Jump Risk By Luis H. R. Alvarez; Jani Sainio
  5. Multi-Factor Bottom-Up Model for Pricing Credit Derivatives By Tsui, L. K.
  6. MBS ratings and the mortgage credit boom By Adam Ashcraft; Paul Goldsmith-Pinkham; James Vickery
  7. Recent progress in random metric theory and its applications to conditional risk measures By Tiexin Guo
  8. Multistage Stochastic Portfolio Optimisation in Deregulated Electricity Markets Using Linear Decision Rules By Paula Rocha; Akwum Daniel Kuhn
  9. Trade credit contracts By Klapper, Leora; Laeven, Luc; Rajan, Raghuram

  1. By: Shawkat Hammoudeh; Farooq Malik; Michael McAleer (University of Canterbury)
    Abstract: This paper examines volatility and correlation dynamics in price returns of gold, silver, platinum and palladium, and explores the corresponding risk management implications for market risk and hedging. Value-at-Risk (VaR) is used to analyze the downside market risk associated with investments in precious metals, and to design optimal risk management strategies. We compute the VaR for major precious metals using the calibrated RiskMetrics, different GARCH models, and the semi-parametric Filtered Historical Simulation approach. Different risk management strategies are suggested, and the best approach for estimating VaR based on conditional and unconditional statistical tests is documented. The economic importance of the results is highlighted by assessing the daily capital charges from the estimated VaRs. The risk-minimizing portfolio weights and dynamic hedge ratios between different metal groups are also analyzed.
    Keywords: Precious metals; conditional volatility; risk management; value-at-risk
    JEL: G1
    Date: 2010–05–01
  2. By: Alessandri, Piergiorgio (Bank of England); Drehmann, Mathias (Bank for International Settlements)
    Abstract: Banks often measure credit and interest rate risk separately and then add the two risk measures to determine their overall economic capital. This approach misses complex interactions between the two risks. We develop a framework where credit and interest rate risks are analysed jointly. We focus on a traditional banking book where all positions are held to maturity and subject to book value accounting. Our simulations show that interactions between risks matter, and that their implications depend on the structure of the balance sheet and on the repricing characteristics of assets and liabilities. The analysis suggests that a joint analysis of risks can deliver substantially different results relative to a piece-wise approach: risk integration is challenging but feasible and worthwhile.
    Keywords: Economic capital; risk management; credit risk; interest rate risk; asset and liability management
    JEL: C13 E47 G21
    Date: 2010–06–01
  3. By: Florian Steiger
    Abstract: This paper examines the possibility of using derivative-implied risk premia to explain stock returns. The rapid development of derivative markets has led to the possibility of trading various kinds of risks, such as credit and interest rate risk, separately from each other. This paper uses credit default swaps and equity options to determine risk premia which are then used to form portfolios that are regressed against the returns of stock portfolios. It turns out that both, credit risk and implied volatility, have high explanatory power in regard to stock returns. Especially the returns of distressed stocks are highly dependent on credit risk fluctuations. This finding leads to practical implications, such as cross-hedging opportunities between equity and credit instruments and potentially allows forecasting stock returns based on movements in the credit market.
    Date: 2010–05
  4. By: Luis H. R. Alvarez; Jani Sainio
    Abstract: We extend the Vasi\v{c}ek loan portfolio model to a setting where liabilities fluctuate randomly and asset values may be subject to systemic jump risk. We derive the probability distribution of the percentage loss of a uniform portfolio and analyze its properties. We find that the impact of liability risk is ambiguous and depends on the correlation between the continuous aggregate factor and the asset-liability ratio as well as on the default intensity. We also find that systemic jump risk has a significant impact on the upper percentiles of the loss distribution and, therefore, on both the VaR-measure as well as on the expected shortfall.
    Date: 2010–06
  5. By: Tsui, L. K.
    Abstract: In this note we continue the study of the stress event model, a simple and intuitive dynamic model for credit risky portfolios, proposed by Duffie and Singleton (1999). The model is a bottom-up version of the multi-factor portfolio credit model proposed by Longstaff and Rajan (2008). By a novel identification of independence conditions, we are able to decompose the loss distribution into a series expansion which not only provides a clear picture of the characteristics of the loss distribution but also suggests a fast and accurate approximation for it. Our approach has three important features: (i) it is able to match the standard CDS index tranche prices and the underlying CDS spreads, (ii) the computational speed of the loss distribution is very fast, comparable to that of the Gaussian copula, (iii) the computational cost for additional factors is mild, allowing for more flexibility for calibrations and opening the possibility of studying multi-factor default dependence of a portfolio via a bottom-up approach. We demonstrate the tractability and efficiency of our approach by calibrating it to investment grade CDS index tranches.
    Keywords: credit derivatives; CDO; bottom-up approach; multi-name; intensity-based; risk and portfolio.
    JEL: C02
    Date: 2010–05–18
  6. By: Adam Ashcraft; Paul Goldsmith-Pinkham; James Vickery
    Abstract: We study credit ratings on subprime and Alt-A mortgage-backed-securities (MBS) deals issued between 2001 and 2007, the period leading up to the subprime crisis. The fraction of highly rated securities in each deal is decreasing in mortgage credit risk (measured either ex ante or ex post), suggesting that ratings contain useful information for investors. However, we also find evidence of significant time variation in risk-adjusted credit ratings, including a progressive decline in standards around the MBS market peak between the start of 2005 and mid-2007. Conditional on initial ratings, we observe underperformance (high mortgage defaults and losses and large rating downgrades) among deals with observably higher risk mortgages based on a simple ex ante model and deals with a high fraction of opaque low-documentation loans. These findings hold over the entire sample period, not just for deal cohorts most affected by the crisis.
    Keywords: Credit ratings ; Mortgages ; Mortgage-backed securities ; Subprime mortgage ; Financial crises ; Financial risk management
    Date: 2010
  7. By: Tiexin Guo
    Abstract: The purpose of this paper is to give a selective survey on recent progress in random metric theory and its applications to conditional risk measures.
    Date: 2010–06
  8. By: Paula Rocha; Akwum Daniel Kuhn
    Abstract: The deregulation of electricity markets increases the financial risk faced by retailers who procure electric energy on the spot market to meet their customers’ electricity demand. To hedge against this exposure, retailers often hold a portfolio of electricity derivative contracts. In this paper, we propose a multistage stochastic mean-variance optimisation model for the management of such a portfolio. To reduce computational complexity, we perform two approximations: stage-aggregation and linear decision rules (LDR). The LDR approach consists of restricting the set of decision rules to those affine in the history of the random parameters. When applied to mean-variance optimisation models, it leads to convex quadratic programs. Since their size grows typically only polynomially with the number of periods, they can be efficiently solved. Our numerical experiments illustrate the value of adaptivity inherent in the LDR method and its potential for enabling scalability to problems with many periods.
    Keywords: OR in energy, electricity portfolio management, stochastic programming, risk management, linear decision rules
    Date: 2010–06–03
  9. By: Klapper, Leora; Laeven, Luc; Rajan, Raghuram
    Abstract: This paper provides new evidence on the unique role of trade credit and contracting terms as a way for both sellers and buyers to mange business risk. The authors use a novel and unique dataset on almost 30,000 supplier contracts for 56 large buyers and more than 24,000 suppliers in Europe and North America. The sample of buyers and suppliers includes firms of varying size, investment grade, and sectors. The paper finds evidence in support of four important, and not mutually exclusive, reasons for trade credit:1) as a method of financing; 2) as a means of price discrimination; 3) as a bond assuring buyers of product quality; and 4) as a screening mechanism to gauge buyer default risk. In particular, the analysis finds that the largest and most creditworthy buyers receive contracts with the longest maturities, as measured by net days, from smaller, investment grade suppliers. In comparison, early payment discounts seem to be used as a risk management tool to limit the potential nonpayment risk of trade credit. Early payment discounts are generally offered to smaller, non-investment grade buyers. The results suggest that contract terms are jointly determined by supplier and buyer characteristics.
    Keywords: Debt Markets,Access to Finance,Bankruptcy and Resolution of Financial Distress,Markets and Market Access,Investment and Investment Climate
    Date: 2010–06–01

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