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


**Table 5.** *Markovtransition-in*

 *sample: 2012–2017.*

countries with exception of Brazil exemplify the same pattern. One possible reason is that listed real estate mimics similar movements. So far, the diagnostic assessments illustrate that there is some relationship between indexed volatilities of equities (real estate). This might imply that volatilities of indices move together

The indexed commodities volatilities of Brazil, China, India and South Africa exemplify similar movements. Brazil and South Africa are some of the main producers of minerals while China and India are some of the main consumers of mineral products. The bonds volatilities show different all countries show different movements. The indexed volatilities for in-sample period seem to be spiky than ones of out-sample period. It can be inferred from [32] that volatilities flatten out in the long-run because of diversification benefits which are more prevalent in the long-run. Broadly, the graphs of in-sample regimes are similar to ones of outsample. Just like in the out-sample analysis for indexed volatilities, Markov transitions are calculated in order to deepen the insights on how indexed volatilities

Panel 17 of **Table 5** illustrates that non-original regimes are dependent for indexed volatilities; however, the original regimes are not necessarily trend setters.

To sum up, this study illustrates that; firstly, there are spillovers that happen across, in-between and within bonds, commodities, equities and real estate indices. Secondly, sometimes the illiquid indices contribute more to volatility spillovers than liquid indices. Thirdly, expected durations of illiquid indices have shorter time spans than liquid indices. Fourth, in most cases, the volatility spillovers patterns during the out-sample period are similar to ones emanating during the in-sample period. Finally, periodical movement patterns vary across, in-between and within

The implications from this study as follows. Firstly, similar governmental formations should be encouraged throughout the world provided that there economic benefits associated with those formations. Secondly, investing in different indices should be encouraged-diversification pays. Thirdly, there are risk management strategies that one can design based on volatility spillovers across, in-between and within bonds, commodities, equities and real estate indices. Fourth, the BRICS formation has indirectly influenced how capital markets (i.e. bonds, commodities, equities and real estate indices) behave. Finally, there are numerous investment

strategies that investment managers can build based on volatility spills.

bonds, commodities, equities and real estate indices.

One of the reasons that might explain that pattern is that during 2012–2017 period most equities market experience bull phase. The expected durations for all equities volatilities are fairly short with exception of the Russian market. Panel 18 illustrates the same pattern as panel 17 except in the case of South Africa. Surprisingly, excepted durations of real estate are far shorter than ones of equities. The patterns of regimes in panels 19 and 20 show similar patterns as in **Table 5**. The interesting part is that excepted durations for Russia-excepted durations of Russia are fairly long. Normally, currencies markets lead movements in stock markets, followed by equities, then bonds and final the real estate. Based on the latter principle, Russian commodities Markov transitions are longer because of long excepted duration of Russian bond index which was preceded by equities volatilities. Similar, real estate volatilities follow the same pattern. The Russian commodities volatilities are higher because Russian is major player in the commodities

during bullish periods than bearish periods.

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

during in-sample period behave.

market in the world.

**5. Conclusion**

**151**

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

countries with exception of Brazil exemplify the same pattern. One possible reason is that listed real estate mimics similar movements. So far, the diagnostic assessments illustrate that there is some relationship between indexed volatilities of equities (real estate). This might imply that volatilities of indices move together during bullish periods than bearish periods.

The indexed commodities volatilities of Brazil, China, India and South Africa exemplify similar movements. Brazil and South Africa are some of the main producers of minerals while China and India are some of the main consumers of mineral products. The bonds volatilities show different all countries show different movements. The indexed volatilities for in-sample period seem to be spiky than ones of out-sample period. It can be inferred from [32] that volatilities flatten out in the long-run because of diversification benefits which are more prevalent in the long-run. Broadly, the graphs of in-sample regimes are similar to ones of outsample. Just like in the out-sample analysis for indexed volatilities, Markov transitions are calculated in order to deepen the insights on how indexed volatilities during in-sample period behave.

Panel 17 of **Table 5** illustrates that non-original regimes are dependent for indexed volatilities; however, the original regimes are not necessarily trend setters. One of the reasons that might explain that pattern is that during 2012–2017 period most equities market experience bull phase. The expected durations for all equities volatilities are fairly short with exception of the Russian market. Panel 18 illustrates the same pattern as panel 17 except in the case of South Africa. Surprisingly, excepted durations of real estate are far shorter than ones of equities. The patterns of regimes in panels 19 and 20 show similar patterns as in **Table 5**. The interesting part is that excepted durations for Russia-excepted durations of Russia are fairly long. Normally, currencies markets lead movements in stock markets, followed by equities, then bonds and final the real estate. Based on the latter principle, Russian commodities Markov transitions are longer because of long excepted duration of Russian bond index which was preceded by equities volatilities. Similar, real estate volatilities follow the same pattern. The Russian commodities volatilities are higher because Russian is major player in the commodities market in the world.
