Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE credits
Investing in commodities may have important benefits for investors but only in the last few decades have they started to think more about this possibility. Furthermore, large investors are more inclined to change their own personal view. Therefore, understanding the benefits that commodities could give to an investment portfolio might alleviate investors’ concerns. Several previous studies, as Belousova and Dorfleitner (2012) suggest, that the commodities with higher benefits are precious metals and gold, in particular. The purpose of our work is to understand which possible benefits are for equity investors and if they are common for certain commodities with different physical characteristics.
The first part of our empirical work focuses on the main descriptive statistics of the return distribution (mean, variance, volatility, skewness, kurtosis and correlation) for 8 stock indices and 7 commodity futures. The main goal of this is to understand the differences among the commodities and between the commodities and the stock indices. In the second part of the empirical work, we test the safe-haven and the hedge properties of these commodities on a weekly basis for all of them with stock indices, and we do the same on a daily and monthly basis for only commodities which are negatively correlated on average with the stock indices. In the last part of our work, we combine these 7 commodities, following the principles of Bloomberg Commodity Index (BCOM), in order to create a well-balanced and well-diversified commodity index. Additionally, we create some mixed portfolios using this index and a different stock index every time. After that we look at the volatilities and the returns of these mixed portfolios with different weight combinations. Our main goals in this section are to understand the characteristics of the commodity index in comparison with stock indices and then, finding which weight combinations give the mixed portfolios the optimal risk-return trade off. Understanding which are efficient weights, can lead to conclusions about the weight that commodities should have in a portfolio according to the risk tolerance of the investors.
The research is done considering three time frequencies: daily, weekly and monthly; in line with the ones used by Baur and McDermott (2010). The sample size differs among these three different time basis. In fact, daily data started in January 2007 and the other two time frequencies data began with January 1997. All the time samples ended in March 2016.
The results of the first part show that gold is the only commodity with a volatility similar to the stock indices (it also has a higher average return) and that on the daily, weekly and monthly basis. Whereas, the other commodities are much riskier than stock indices since they have higher volatility for all the three time-frequencies analyzed.
The results of the second part suggest that only gold is both a safe-haven and hedging commodity in line with the methodology used by Baur and McDermott (2010), but only for DAX 30 on a weekly basis. Furthermore, our results also show that natural gas is strong hedge in some cases such as natural gas for STI (Singapore) on a monthly basis or gold for Nikkei 225 on daily, weekly and monthly basis. Other commodities are neither safe-haven nor hedge in any case, except for silver which is a safe-haven commodity for DAX 30 and Sensex which at its worst, 1% and 5%, declines in the market respectively.
The results of the last part of our work show that all the minimum variance mixed portfolios (the ones with the weights give the lowest risk) - made on a weekly basis - reduce the portfolio volatility and make the portfolio returns higher than the stock indices returns in 5 cases out of 8. Additionally, the results show how investors, who add a well-balanced and well-diversified commodity index to their portfolios, are able to observe several weight combinations and choose the one which suits their risk tolerance. Moreover, our results show that the optimal-weight combinations for commodity weights are lower than 0,5 only for FTSE 100 and S&P 500 (both values are 0,49) and higher than 0,62 but lower than 0,7 for DAX 30, Nikkei 225, Hang Seng, Sensex, SSEC. Furthermore, the optimal weight for STI is 0,54.
2016. , 103 p.
Stock index, commodity, futures, return, volatility, safe-haven, hedge, portfolio, diversification, correlation, optimal weights