The data analysis of the metal markets has recently attracted a lot of attention, mainly because the prices of precious metal are relatively more volatile than its historical trend. A robust estimate of extreme loss is vital, especially for mining companies to mitigate risk and uncertainty in metal price fluctuations. This paper examines the Value-at-Risk and statistical properties in daily price return of precious metals, which include gold, silver, platinum, and palladium, from January 3, 2008 to November 27, 2018. The conditional variance is modeled by different univariate GARCH-type models (GARCH and EGARCH). The estimated model suggests that the two models both worked effectively with the metal price returns and volatility clustering in those metal returns are very clear. In the second part, backtesting approach is applied to evaluate the effectiveness of the models. In comparison of VaRs for the four precious metals return, gold has the highest and most steady VaR, then is platinum and silver, while palladium has the lowest and most volatile VaR. The backtesting result confirms that our approach is an adequate method in improving risk management assessments.
MSc in Finance Project-Simon Fraser University
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