**1. Introduction**

Formation of organisation that represents countries with similar interests or likeminded goals can be traced many decades ago. Some of those organisations are continentally focused (i.e. African Union, former Organisation of African Unity in 1963 and European Union in 1958-its original roots) while other are global (i.e. United Nations in 1945). Recently, we have seen organisations that are Transatlantic-Brazil, Russia, India, China and South Africa (BRICS hereafter) countries. South Africa (SA) joined BRIC countries in 2009 through invitation by other member states while the four founding members originate from a term coined by Jim O'Neil (former Managing Partner of Goldman Sachs). While the origination of the BRIC term is influenced by the economic similarities, there are other interesting similarities about BRIC countries. The similarities of BRICS nation are (i) political structure-ruling parties stay in power for least 10 years without much challenge; although, we have recently seen the rising of opposition parties or citizens, (ii) country governance-ruling elites combine free market policies with socialism, and privatisation of government owned entities is extremely rare and (iii) economic policies-ruling parties champion economic direction and by extension economic of countries [1]. Some market commentators called that approach statism. However, statism is beyond the scope of this study. Those three traits have strong influence of the capital markets of those countries. The key question is what

relationship exists between investments and associated risks. For this article, the special focus is on volatility spills.

studies illustrate that those effects are trans-Atlantic. The reason why cash is not analysed in this article is because cash and money asset classes have been extensively researched. For example, over 60% of international trade is done on U.S. dollars and currency markets are the most liquid of all capital markets [5]. For this study, it is important to drive risk management strategies, especially when infor-

The article similar to this study is one by Liow [6]. That study analysed spillovers of four major asset classes (public real estate, general equity, currency and bond) during 2007–2009 period. Given the longer period for this study, one foresees more interesting results than ones of Liow [6]. He used regime-switching, VAR and GARCH (1;1) models. This uses models used in [6] plus the regime-switching model. Liow [6] draws data from four continents; (i) Asia emerging countries, (ii) European emerging markets, (iii) Latin American emerging countries and (iv) South Africa. Other than being emerging countries, the BRICS are similar in the sense that ruling political elites stay in power for long periods (i.e. 15 years), more, those governments have come up with organisations that are most likely to compete with established institutions, i.e. the BRICS Bank is most likely to compete with the World Bank in future. Further, there is close political will among the BRICS which is not prevalent among all emerging countries. As volatility spills are driven by financial integration, liberalisation and crises contagion [6] among other factors, the former factors are likely to be key drivers for volatility spills among the BRICS countries. So far, it seems there are no major crises contagion reported in any of the

To sum up, the results show that the indices (bonds, commodities, equities and

real estate) illustrate that volatility spills are within and in between emerging countries. The volatility movements between countries are sporadic without any specific pattern(s)-most volatility spills are within countries. Those spills are evident in both out and in-sample data. Thus, lagged data of indices have evident volatility spillovers. Consistent with prior studies, the volatility spills move between different volatility regimes. Interestingly, liquid indices have less persistent regimes than illiquid indices. That would imply that illiquid indices are suitable for investments by intraday investors such as hedge funds while liquid indices are suitable for long-term investments-a rare finding. In [7], Markov-Switching-GARCH model is used, while this study uses general Markov regime switching model. The former model is univariate and discrete in nature while the latter is 'multivariate' and continuous in nature. Hedging was effectively reduced by 64% in [8] while in this

The balance of this article is structured as follows: Section 2 is on literature review. Section 3 is on data and modelling, and Section 4 presents the analysis.

In criticising the prior studies this article divides literature review as per the four asset classes; (iii) bonds, (i) commodities, (ii) equities, and (iv) listed real estate.

In [9], it is explored volatility spills and return between equity and bond markets for Australia during the period of 1992–2006. They argue that volatility spills are important for diverse purposes; (i) asset allocation, (ii) portfolio management,

study volatility risk is appropriately modelled.

In this way, specific traits of each asset class are disentangled.

Section 5 concludes the study.

**2. Literature review**

**2.1 Bonds**

**129**

mation is asymmetric.

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

BRICS countries.

That concept is commonly known as volatility transfer hypothesis (VTH). VTH is well documented across and within different traditional asset classes (i.e. stocks, bonds and money market instruments especially cash). Fundamentally, VTH argue that as one become familiar with a firm, the volatility of that firm decreases due to decrease in information asymmetry. However some scholars argue that VTH does not hold in every situation. On the practical side, specifically in among alternative asset classes, there are virtually no studies on VTH. This is the main gap that this article fills in. In analysis, the study draws data on bonds, commodities, equities and listed real estate from the BRICS countries. The analysis is essentially empirical. Both empirical and theoretical studies offer little, if any, insight on how volatility spillovers behave and their effects in the BRICS countries. The closest study that explores this theme is [2]. In [2], multivariate general autoregressive conditional heteroscedasticity (GARCH) and disaggregated value-at-Risk (VaR) are used to study traditional asset classes. This study goes beyond traditional asset classes and uses other models such as the regime-switching models. Similarly to [2], international diversification and risk management is central to volatility spillovers in BRICS countries.

A lot of policy documents show that jointly BRICS account for over billions of dollars investments including listed investments-in 2012 BRICS received over \$1 billion in foreign aid. The population is highly consumptuous with a high percentage of population eligible to work for foreseeable future. In all those countries, ruling governments encourage their working force to save some of their earnings for later use in their life. Among the type of investments that potential future retiree can invest in include bonds, commodities, equities and listed real estate investments. Besides the type of investments that potential future retiree investments in, BRICS have their own special economic traits. South Africa offers one of the highly sophisticated capital markets in the world and China is the second biggest economy after the Unites States (U.S.). More, China has been moving at least 30 million people out of the poverty over the last 20 years. Given those massive investment opportunities in BRICS countries, how do investors maximise their returns and minimise their risks? One of the ways of minimising risks in the BRICS is by mitigating against volatility investment movements in the BRICS countries.

The consensus emerging from literature on asset co-movements is that asset markets are linked internationally, and volatility is transmitted from one market to another. Earlier studies of market linkages were habitually focused on developed countries however due to the financial liberalisation and trade openness of emerging economies, research has also focused on investigating cross-border links in emerging economies from developed countries. Emerging markets have increasingly played an important role in financial markets and were not spared from the impact of the global financial crisis. A better understanding of how emerging markets respond to exogenous shocks can assist investors and portfolio managers better understand if there are any diversification possibilities.

This article explores volatility spillovers in the BRICS countries based on alternative investment strategies. That is, alternative investment strategies involve investment in bonds, commodities, equities and real estate. For this study, seems real estate is listed because on one hand, the relationship between listed real estate and unlisted real estate is a mixed bag [3] and other the other hand, real estate is seen as a proxy for macroeconomic risks [4]. The macroeconomic risk proxy is also evident in other industries such as commodities. Moreover, diversification plays part in influencing commodity prices. Listed real estate is either real estate investment trusts (REITs) or real estate operating companies (REOCs). Further, those

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

studies illustrate that those effects are trans-Atlantic. The reason why cash is not analysed in this article is because cash and money asset classes have been extensively researched. For example, over 60% of international trade is done on U.S. dollars and currency markets are the most liquid of all capital markets [5]. For this study, it is important to drive risk management strategies, especially when information is asymmetric.

The article similar to this study is one by Liow [6]. That study analysed spillovers of four major asset classes (public real estate, general equity, currency and bond) during 2007–2009 period. Given the longer period for this study, one foresees more interesting results than ones of Liow [6]. He used regime-switching, VAR and GARCH (1;1) models. This uses models used in [6] plus the regime-switching model. Liow [6] draws data from four continents; (i) Asia emerging countries, (ii) European emerging markets, (iii) Latin American emerging countries and (iv) South Africa. Other than being emerging countries, the BRICS are similar in the sense that ruling political elites stay in power for long periods (i.e. 15 years), more, those governments have come up with organisations that are most likely to compete with established institutions, i.e. the BRICS Bank is most likely to compete with the World Bank in future. Further, there is close political will among the BRICS which is not prevalent among all emerging countries. As volatility spills are driven by financial integration, liberalisation and crises contagion [6] among other factors, the former factors are likely to be key drivers for volatility spills among the BRICS countries. So far, it seems there are no major crises contagion reported in any of the BRICS countries.

To sum up, the results show that the indices (bonds, commodities, equities and real estate) illustrate that volatility spills are within and in between emerging countries. The volatility movements between countries are sporadic without any specific pattern(s)-most volatility spills are within countries. Those spills are evident in both out and in-sample data. Thus, lagged data of indices have evident volatility spillovers. Consistent with prior studies, the volatility spills move between different volatility regimes. Interestingly, liquid indices have less persistent regimes than illiquid indices. That would imply that illiquid indices are suitable for investments by intraday investors such as hedge funds while liquid indices are suitable for long-term investments-a rare finding. In [7], Markov-Switching-GARCH model is used, while this study uses general Markov regime switching model. The former model is univariate and discrete in nature while the latter is 'multivariate' and continuous in nature. Hedging was effectively reduced by 64% in [8] while in this study volatility risk is appropriately modelled.

The balance of this article is structured as follows: Section 2 is on literature review. Section 3 is on data and modelling, and Section 4 presents the analysis. Section 5 concludes the study.
