New Economics Papers
on Risk Management
Issue of 2010‒09‒03
seven papers chosen by

  1. Risk management of precious metals By Hammoudeh, S.M.; Malik, F.; McAleer, M.J.
  2. Predictive Ability of Value-at-Risk Methods: Evidence from the Karachi Stock Exchange-100 Index By Javed Iqbal; Sara Azher; Ayesha Ijza
  3. Analyzing and Forecasting Volatility Spillovers and Asymmetries in Major Crude Oil Spot, Forward and Futures Markets By Chia-Lin Chang; Michael McAleer; Roengchai Tansuchat
  4. Capital Regulation after the Crisis: Business as Usual? By Martin Hellwig
  5. Modelling the Asymmetric Volatility in Hog Prices in Taiwan: The Impact of Joining the WTO By Chang, C-L.; Huang, B-W.; Chen, M.-G.
  6. Monte Carlo Portfolio Optimization for General Investor Risk-Return Objectives and Arbitrary Return Distributions: a Solution for Long-only Portfolios By William T. Shaw
  7. Large Shareholder Diversification And Corporate Risk- Taking By Mara Faccio; Maria-Teresa Marchica; Roberto Mura

  1. By: Hammoudeh, S.M.; Malik, F.; McAleer, M.J.
    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
    Date: 2010–07–29
  2. By: Javed Iqbal; Sara Azher; Ayesha Ijza
    Abstract: Value-at-risk (VaR) is a useful risk measure broadly used by financial institutions all over the world. VaR has been extensively used to measure systematic risk exposure in developed markets like of the US, Europe and Asia. This paper analyzes the accuracy of VaR measure for Pakistan’s emerging stock market using daily data from the Karachi Stock Exchange-100 index January 1992 to June 2008. We computed VaR by employing data on annual basis as well as for the whole 17 year period. Overall we found that VaR measures are more accurate when KSE index return volatility is estimated by GARCH (1,1) model especially at 95% confidence level. In this case the actual loss of KSE-100 index exceeds VaR in only two years 1998 and 2006. At 99% confidence level no method generally gives accurate VaR estimates. In this case ‘equally weighted moving average’, ‘exponentially weighted moving average’ and ‘GARCH’ based methods yield accurate VaR estimates in nearly half of the number of years. On average for the whole period 95% VaR is estimated to be about 2.5% of the value of KSE-100 index. That is on average in one out of 20 days KSE-100 index loses at least 2.5% of its value. We also investigate the asset pricing implication of downside risk measured by VaR and expected returns for decile portfolios sorted according to VaR of each stock. We found that portfolios with higher VaR have higher average returns. Therefore VaR as a measure of downside risk is associated with higher returns.
    Keywords: Downside risk; Emerging Markets; Value-at-Risk.
    JEL: C5 C52 G1 G10
    Date: 2010–08–18
  3. By: Chia-Lin Chang (Department of Applied Economics, National Chung Hsing University); Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University); Roengchai Tansuchat (Faculty of Economics, Maejo University)
    Abstract: Crude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at- Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas Intermediate (USA), Brent (North Sea), Dubai/Oman (Middle East), and Tapis (Asia- Pacific), which are likely to be highly correlated. This paper analyses the volatility spillover and asymmetric effects across and within the four markets, using three multivariate GARCH models, namely the constant conditional correlation (CCC), vector ARMA-GARCH (VARMA-GARCH) and vector ARMA-asymmetric GARCH (VARMA-AGARCH) models. A rolling window approach is used to forecast the 1-day ahead conditional correlations. The paper presents evidence of volatility spillovers and asymmetric effects on the conditional variances for most pairs of series. In addition, the forecast conditional correlations between pairs of crude oil returns have both positive and negative trends. Moreover, the optimal hedge ratios and optimal portfolio weights of crude oil across different assets and market portfolios are evaluated in order to provide important policy implications for risk management in crude oil markets.
    Keywords: Volatility spillovers, multivariate GARCH, conditional correlation, crude oil prices, spot returns, forward returns, futures returns
    JEL: C22 C32 G32
    Date: 2010–08
  4. By: Martin Hellwig (Max Planck Institute for Research on Collective Goods, Bonn)
    Abstract: The paper discusses the reform of capital regulation of banks in the wake of the financial crisis of 2007/2009. Whereas the Basel Committee on Banking Supervision seems to go for marginal changes here and there, the paper calls for a thorough overhaul, moving away from risk calibration and raising capital requirements very substantially. The argument is based on the observation that the current system of risk-calibrated capital requirements, in particular under the model-based approach, played a key role in allowing banks to be undercapitalized prior to the crisis, with strong systemic effects for deleveraging multipliers and for the functioning of interbank markets. The argument is also based on the observation that the current system has no theoretical foundation, its objectives are ill-specified, and its effects have not been thought through, either for the individual bank or for the system as a whole. Objections to substantial increases in capital requirements rest on arguments that run counter to economic logic or are themselves evidence of moral hazard and a need for regulation.
    Keywords: financial crisis, Basel Accord, banking regulation, capital requirements, modelbased approach, systemic risk
    JEL: G21 G28
    Date: 2010–08
  5. By: Chang, C-L.; Huang, B-W.; Chen, M.-G.
    Abstract: Prices in the hog industry in Taiwan are determined according to an auction system. There are significant differences in hog prices before, during and after joining the World Trade Organization (WTO). The paper models growth rates and volatility in daily hog prices in Taiwan from 23 March 1999 to 30 June 2007, which enables an analysis of the effects of joining the WTO. The empirical results have significant implications for risk management and policy in the agricultural industry. The three sub-samples for the periods before, during and after joining the WTO display significantly different volatility persistence of symmetry, asymmetry and leverage, respectively.
    Keywords: hog prices;joining the WTO;conditional volatility models;asymmetry;leverage;moment conditions
    Date: 2010–07–28
  6. By: William T. Shaw
    Abstract: We develop the idea of using Monte Carlo sampling of random portfolios to solve portfolio investment problems. In this first paper we explore the need for more general optimization tools, and consider the means by which constrained random portfolios may be generated. A practical scheme for the long-only fully-invested problem is developed and tested for the classic QP application. The advantage of Monte Carlo methods is that they may be extended to risk functions that are more complicated functions of the return distribution, and that the underlying return distribution may be computed without the classical Gaussian limitations. The optimization of quadratic risk-return functions, VaR, CVaR, may be handled in a similar manner to variability ratios such as Sortino and Omega, or mathematical constructions such as expected utility and its behavioural finance extensions. Robustification is also possible. Grid computing technology is an excellent platform for the development of such computations due to the intrinsically parallel nature of the computation, coupled to the requirement to transmit only small packets of data over the grid. We give some examples deploying GridMathematica, in which various investor risk preferences are optimized with differing multivariate distributions. Good comparisons with established results in Mean-Variance and CVaR optimization are obtained when ``edge-vertex-biased'' sampling methods are employed to create random portfolios. We also give an application to Omega optimization.
    Date: 2010–08
  7. By: Mara Faccio; Maria-Teresa Marchica; Roberto Mura
    Abstract: Using new data for the universe of firms covered in Amadeus, we reconstruct the portfolios of shareholders who hold equity stakes in private and publicly-traded European firms. We find great heterogeneity in the degree of portfolio diversification across large shareholders. Exploiting this heterogeneity, we document that firms controlled by diversified large shareholders undertake riskier investments than firms controlled by non-diversified large shareholders. The impact of large shareholder diversification on corporate risk-taking is both economically and statistically significant. Our results have important implications at the policy level because they identify one channel through which policy changes aimed at improving capital market development can improve economic welfare.
    Keywords: Risk-taking choices; Large shareholders; Portfolio diversification
    JEL: G11 G15 G31
    Date: 2010–07

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