مدیریت خطر فلزات گرانبها

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. 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.

Financial and commodity markets have been highly volatile in recent years. Volatility brings risk and opportunity to traders and investors, and should therefore be examined. There are many reasons for volatility to occur in commodity markets. Political unrest or extreme weather conditions in commodity producing countries’ can cause supply disruptions which can create volatility in commodity prices. Introduction of new financial innovations, such as futures, options, exchange-traded funds (ETFs), and use of precious metal as collateral for trading can affect precious metals volatility. Selling and buying of gold by the International Monetary Fund (IMF) and central banks can also change volatility. Changes in demand for the product of an industry that uses commodities as an input may lead to fluctuations in prices of commodities. Market participants form different expectations of profitable opportunities, perform cross-market hedging across different asset classes, process information at different speeds, and build and draw inventories at different levels. These factors contribute to volatility of commodities over time and across markets. In addition to policy makers and portfolio managers, manufacturers are also interested in this information because precious metals have important and diversified industrial use in jewellery, medicine, electronic and auto catalytic industries. Quantification of the predictable variations in precious metals’ price changes is fundamental in designing sensible risk management strategies. Value-at-risk (VaR) has become an important instrument within financial markets for quantifying and assessing the portfolio market risk associated with financial asset and commodity price movements. There is a cost of inaccurate estimation of the VaR in financial markets which affects efficiency and accuracy of risk assessments. Widespread evidence suggests that precious metals should be part of a well diversified portfolio. Since the prices of these precious metals have been very volatile, so financial market participants are interested in knowing the downside risk of holding precious metals in their portfolios. The VaR measure directly answers this important question and surprisingly there is no study on the analysis of VaR for precious metals. One of the primary purposes of the paper is to fill this void in the risk management literature. Specifically, we compute VaR for gold, silver, platinum and palladium using RiskMetrics, the GARCH model (using normal and t-distribution), and the recent Filtered Historical Simulation (FHS) approach. The out-of-sample forecast performance indicates that the GARCH with t-distribution produces a VaR with the most accurate and robust estimates of the actual VaR thresholds for all four precious metals. The unconditional coverage test of Kupiec (1995) and the conditional coverage test of Christoffersen (1998) are used to assess the performance of the various models in regards to VaR, and different risk management strategies based on the empirical results are discussed. The economic importance of the estimation results is highlighted by calculating the capital requirements using different VaR models to assess market risk exposure for all precious metals.

This paper examines the volatility dynamics in precious metals and explores the corresponding risk management implications. The conditional volatility and correlation dynamics in the price returns of gold, silver, platinum and palladium are modeled using daily data from January 1995 to November 2009. Value-at-Risk (VaR) is used to analyze the risk associated with precious metals, and to design optimal risk management strategies. We compute the VaR for all precious metals using the calibrated RiskMetrics, alternative empirical GARCH models, and the semi-parametric Filtered Historical Simulation approach. Different risk management strategies are suggested based on conditional and unconditional statistical tests. The economic importance of our results is highlighted by calculating the daily capital charges from the estimated VaRs using different methods for all precious metals. This exercise shows that portfolio managers engaged in precious metals who want to follow a conservative strategy should calculate VaR using GARCH-t as this will yield fewer violations, though with lower profitability. Our results are very timely and useful for financial market participants as the global financial markets continue to experience unprecedented volatility and the need for investment in precious metals remains high.7