**2.2 Commodities**

In [12], volatility spills were investigated in commodity markets since 1700. They argue that some authors raised questions regarding the volatility of commodity prices been more than manufacturing ones, the secular trend since 1700 and relationship between globalisation and commodity volatilities. However, none of the scholars have addressed those questions using a long term series indeed. For poor countries [12], it was argue that volatilities for those countries should be high because those countries specialise in agriculture and mineral production. The data used in [12] is for the world and various trends are outlined during specific periods. This is to consolidate reasons that drove commodity prices during those periods. They calculated log prices for their study, and used Dickey-Fuller and Phillips-Perron tests to validate their illustrate volatilities. Prebisch-Singer hypothesis was central to their analysis. Preliminary results of [12] show that volatilities among different commodities are different. In poor countries, volatilities tend to be higher because those countries are dependent on agriculture and mineral production. Sauerbeck-Statist shows no evidence of secular patterns from 1800 onwards. Further analysis illustrates that French and American Revolutionary Wars, the Napoleonic Wars and the War of 1812 contributed to increase in volatilities. In order to test the robustness of their results [12], GARCH (1;1) model and GARCH (1;1) was used and it was confirmed that results are robust. Seasonality also played a role in driving higher volatilities.

Antonakakis and Kizys [13] investigated dynamic spills between commodity and currency markets. In [13], it is argued that precious metals (gold, silver, platinum and palladium) have been seen as safe havens during final crisis. Further, they state that inclusion of precious metals in equity portfolios decreases systematic risk of investments; therefore, diversification accrues in those investments. They research is centred on these questions; (i) how time-varying spills differ among commodity and currency markets, and (ii) what is the relationship between returns and volatilities during financial transmission. In answering those questions, Antonakakis

and Kizys [13] used the spillover index which is performed by using rolling-window forecast error variance decomposition (FEVD) by transmitters and receivers of shocks.

shocks. The latter proposition is about how institutional demand increases for all assets are positively correlated with index, especially demand for commodity

'asymmetric and investors have the same beliefs'.

*The Independence of Indexed Volatilities DOI: http://dx.doi.org/10.5772/intechopen.90240*

measure and (vi) S&P500 stock index.

The results for [14] illustrated those volatilities in futures markets do spillover into other commodities. Further, there is a trade-off between investors due to relative performance fluctuates. The latter phenomenon is consistent with what is illustrated by VIX volatility index [14]. In addition, the model information is

In [15], excess co-movements of commodity prices in developed (118 variables from Australia, Canada, France, Germany, Japan, the UK and the U.S.) and emerging markets (six variables from China, Brazil, Brazil, Taiwan, Mexico, etc.) were investigated. They argue that prior studies illustrate that financialization in the commodities markets lead to excess price volatility. One possible reason for that is that commodities especially of currency nature such as gold are characterised by spikes in prices. Central to their investigation is that (i) co-movements imply that 'demands and supplies are affected by unobserved forecast of the economic variable' and (ii) portfolio management strategies are affected by co-movements. The latter phenomenon resonates with this study. The variables that [15] are (i) the U.S. index of industrial production, (ii) consumer price index (CPI), (iii) effective \$US exchange rate, (iv) three-month Treasury bill interest rate, (v) M1 monetary

One thing which is evident in [15] is that they are dealing with a large database which has numerous variables. And in order to probably account for those variables, you need a model that accounted for such variables. For the commodity prices, they used wheat, copper, silver, soybeans, raw sugar, cotton, crude oil and

minimised. One of the ways of how to avoid arbitrariness and computational difficulties is to use principal component analysis (PCA) and stepwise regression, although stepwise is time consuming when one uses many variables. In their analysis [15] focused on filtering commodity returns using large approximate factors models. And for that [15] used (i) static factor model and (ii) ARCH-LM for illustrating spillovers and (iii) SUR model to test whether residuals are unrelated. The preliminary analysis of [15] the skewness of all commodities except of wheat is negatively skewed. Thus, wheat should have high volatilities than the rest of the commodities. And the Jarque-Berra test confirms non-normality for all commodities. The latter illustration is consistent with other studies on commodities. The correlation matrix shows that all commodities are correlated with one another except with live cattle. That is, live cattle in when compared with the seven commodities might offer diversification benefits. The results of returns show that crude oil and copper are costly correlated with variables of emerging markets. Monetary measures have more influence in emerging markets than developed countries. When they test for excess co-movement of commodity returns, results exemplify that commodity co-movements are common and influencing across all markets. Moreover, those co-movements are sampling dependent. In [15], it stated that given that the speculation is rife in commodity markets, some co-movements might be driven by speculation. The OLS model confirms the presence of endogeneity.

The Black Monday of October 1987, the U.S. born global financial crisis of 2008 and 2009, as well as the European debt crisis that occurred in late 2009 are known as the some of the few financial crisis in the past three decades that have resulted in the volatility of financial markets and further resulted in wide spread international

live cattle. Further, arbitrariness and computational difficulties should be

storage.

**2.3 Equities**

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The weekly data in [13] is made up of the spot prices of the four precious metals, crude oil spot prices, euro (EUR/USD), Japanese yen (JPY/USD), British pound (GBP/USD) and the Swiss franc (CHF/USD) spot exchange rate, each versus U.S. dollar. They use weekly daily in order to synchronise data and error elimination [13]. The period of the data is from January the 6th, 1987 to July the 22nd, 2014, totalling 1438 observations. The usage of the four precious metals is well documented by numerous studies. The preliminary analysis of data illustrate that volatilities increased dramatically especially from 2000/2001 period for the precious metals and oil, while currencies volatility decreased from 2000/2001 onwards. Moreover, preliminary analysis shows that spot prices are positively skewed with exception of GBP/USD and CHF/USD. The absolute returns (volatility) for all parameters are positively skewed. And the Jarque-Berra tests confirm nonnormality of distributions. Further analysis includes using vector autoregressive (VAR) model to illustrate return transmission across all the parameters. One of the advantages of VAR model is that it can cater for many variables.

The results of the VAR model illustrate volatility spills across all variables. Total spillovers index indicates 42.41% average contribution. Most transmission was from gold, followed by silver and then platinum. Crude oil had lowest transmissions. On the other hand, crude oil's demand is linked to four commodities as for production of those metals, crude oil is used. One of notable thing about [13] is that negative skewness has higher probabilities. Normally, the opposite should be true because positive skewness constituent more risk than a negative one. For all variables, the curves are positively skewed and leptokurtic. The latter statement would imply that prices spreads are significantly probably due to high volatilities. According [13], volatilities in commodity and currency markets are likely to occur during less volatile episodes. For robustness test, they used h-step-ahead forecast error variance decompositions and alternative rolling windows, and robustness tests confirmed that results main qualitatively similar.

Basak and Pavlova [14] modelled financialization for commodities markets. Prior studies have documented index and non-index commodities; however, the theory of financialization which is far-reaching implications had limited synthetisation [14]. The latter point is central to study of [14]. The main variables that were analysed in the study are (i) commodity supply shocks, (ii) commodity demand shocks, and (iii) (endogenous) changes in wealth shares of the two investor classes. The theoretical model that they built is a closed form. Fundamentally, in [14], it was argued that value assets pay off more in high-index states. In building the model, they assumed that the model follow Brownian motion (BM). The model included a parameter that signal arrival of news, supply news of uncorrelated commodities, model distinguish between index and non-index commodities, and the inventors were accounted for; (i) normal investors and (ii) institutional investors. Moreover, equilibrium effects of financialization of commodities were accounted for. Centrally to the last statement, instead of the model behaving like a trading model, it behaved like one for normal investor. Other equilibrium issues included (i) equilibrium commodity futures prices shaped on corollary, (ii) futures volatilities and correlations, and (iii) economy with demand shocks. Further, the illustrated commodity prices and inventories. For the commodity prices and inventories, they (i) incorporating storage where additional economic agents (i.e. consumers and firms) were added, (ii) equilibrium commodity prices and inventories. The second proposition is on how the discount factor is affected by institutional inventors. And finally, (iii) cross-commodity spillovers and the import of income
