Change in Volatility Regimes and Diversification in Emerging Stock Markets

Abstract

A multivariate Markov-switching ARCH (MVSWARCH) model in which variance/correlations for stock returns is controlled by a state-varying mechanism is introduced and used to design a state-varying US-EM (emerging market) portfolio establishment strategy. Additionally, a conventional random-variance framework, the MVGARCH (multivariate GARCH) model, in which a time-varying technique is involved is employed and subjected to comparative analysis. The empirical results are consistent with the following notions: First, as being consistent with a study conducted by Ramchand and Susmel , the US-EM market correlations are higher when the US market is more volatile. However, this study further indicates that the US-EM market correlations increase relatively more when both the US and EM markets simultaneously experience a high variance condition. Moreover, the situation of both the US and EM stock markets at a high volatility state is associated with a minimum risk reduction benefit and a maximum cross-market correlation. Second, the state-varying portfolio loadings established by the MVSWARCH model could effectively enhance asset allocation effectiveness; however, this benefit arises more as a result of risk reduction than an increase in mean returns. Copyright (c) 2009 The Authors. Journal compilation (c) 2009 Economic Society of South Africa.

Modelling stock return volatility dynamics in selected African markets

“…Common practice within the MV-SWARCH framework is to assume that the correlations (ρ xst ) in s t * = 1 and s t * = 2 are equal, and likewise for s t * = 3 and s t * = 4 (Edwards and Susmel, 2003;Li, 2009;Ramchand and Susmel, 1998). This restricts the correlation coefficient to vary with the state of the 'originator' country series (Series i) and facilitates convergence during the maximisation procedure through maintaining tractability of the system (Ramchand and Susmel, 1998).…”

“…Following the work of Hamilton and Lin (1996), MV-SWARCH models have been typically restricted to fit certain assumptions. As MV-SWARCH models tend to be computationally burdensome, it is often assumed that the individual series experience only two volatility regimes, and such studies are commonly restricted to bivariate analysis (Edwards and Susmel, 2003;Li, 2009;Ramchand and Susmel, 1998). This simplified specification thus includes four 'primitive' states (s t *) such that:…”

“…However, these results are on occasion statistically insignificant or inconclusive (Dueker, 1997), and are less clear when assessed through some purely economic loss functions (Marcucci, 2005). Multivariate models of the conditional variance-covariance of returns, such as multivariate SWARCH (MV-SWARCH) and Markov switching BEK-GARCH, have also been used in order to compute timevarying optimal portfolio weights (Li, 2009) and hedge ratios (Lee and Yoder, 2007) or to assess contagion effects (Edwards and Susmel, 2001;Ramchand and Susmel, 1998).…”

“…In addition, changes in the co-movement of stock returns across international markets during high-and low-volatility periods have major implications for diversification strategies (Li, 2009;Ramchand and Susmel, 1998).…”

Performance and internationalization effects of the use of ICT in diversified companies

“…On the other hand, since the study developed has identified previous theoretical and empirical studies, it has shown that there is a great interest to analyze the use of ICT and its impact on the different organizational factors (Li, 2009;Melville, Kraemer, & Gurbaxani, 2004). This bibliographic review has also identified the factors that lead companies to diversify their business line, recognizing in ICT a resource that can help companies to find new opportunities to expand the range of products of organizations and their impact on the performance of the diversified company (Chari et al, 2008;Hu et al, 2011;Liu & Ravichandran, 2008;Ravichandran et al, 2009;Shin, 2009aShin, , 2009b.…”