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Downside risk management and VaR-based optimal portfolios for precious metals, oil and stocks (2013)

by on October 8, 2013

This article is one of very few studies discussing the downside risk management of precious metals market.

Commodities especially the precious metals experienced price surges in the last few years after the global financial crisis. Considering the increasing importance of precious metals as hedge and safe haven, and also their volatile nature, this paper analyzes the VaR for precious metals through different estimation techniques. They find that the RiskMetrics model is the best performer under the Basel rules.

The paper also compares three optimal portfolios based on the VaR. Portfolio 1 consists the pure precious metals. Portfolio 2 is the most diversified and comprised of six assets that include the four precious metals, Brent oil and the S&P 500 index. Portfolio 3 has three assets gold, oil and the S&P 500 index. The results show that the average daily returns of the three portfolios are very close, and they should have more gold than any of the other assets included in the portfolio. The most diversified portfolio 3 is the most efficient and the portfolio 1 which only consists of precious metals is the least efficient.

Model: Estimation models to compute VaR include Risk Metrics, Duration-based Peak Over Threshold (DPOT), conditional EVT (CEVT), APARCH models (using normal and skewed t-distributions), GARCH-based filtered historical simulation and median strategy.

Several risk performance evaluations of these techniques are also used such as unconditional coverage test, an independence test and a conditional coverage test.

Data: Daily returns based on closing spot prices for the four precious metals (gold, silver, platinum, and palladium) traded at COMEX, the oil price and the S&P 500 index for the period January 2, 1995 to July 5, 2011.

Abstract: Value-at-Risk (VaR) is used to analyze the market downside risk associated with investments in six key individual assets including four precious metals, oil and the S&P 500 index, and three diversified portfolios. Using combinations of these assets, three optimal portfolios and their efficient frontiers within a VaR framework are constructed and the returns and downside risks for these portfolios are also analyzed. One-day-ahead VaR forecasts are computed with nine risk models including calibrated RiskMetrics, asymmetric GARCH type models, the filtered Historical Simulation approach, methodologies from statistics of extremes and a risk management strategy involving combinations of models. These risk models are evaluated and compared based on the unconditional coverage, independence and conditional coverage criteria. The economic importance of the results is also highlighted by assessing the daily capital charges under the Basel Accord rule. The best approaches for estimating the VaR for the individual assets under study and for the three VaR-based optimal portfolios and efficient frontiers are discussed. The VaR-based performance measure ranks the most diversified optimal portfolio (Portfolio #2) as the most efficient and the pure precious metals (Portfolio #1) as the least efficient.

Full Citation: Shawkat Hammoudeh, Paulo Araújo Santos, Abdullah Al-Hassan, Downside risk management and VaR-based optimal portfolios for precious metals, oil and stocks, The North American Journal of Economics and Finance, Volume 25, August 2013, Pages 318-334, ISSN 1062-9408, http://dx.doi.org/10.1016/j.najef.2012.06.012.

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From → Empirical, Gold, PGMs, Silver

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