<p><font face="F59" size="3"><font face="F59" size="3"> <p align="left">This study examines the relationship between stock returns and market segmentation. Monthly returns of stocks listed on the JSE from 1997-2007 are analysed using mostly the analytic factor and cluster analysis techniques. Evidence supporting the use of multi-index models in explaining the return generating process on the JSE is found. The results provide additional support for Van Rensburg (1997)'s hypothesis on market segmentation on the JSE.</p> </font></font></p>

<p><font face="F59" size="3"><font face="F59" size="3"> <p align="left">This study examines the relationship between stock returns and market segmentation. Monthly returns of stocks listed on the JSE from 1997-2007 are analysed using mostly the analytic factor and cluster analysis techniques. Evidence supporting the use of multi-index models in explaining the return generating process on the JSE is found. The results provide additional support for Van Rensburg (1997)'s hypothesis on market segmentation on the JSE.</p> </font></font></p>

>Magister Scientiae - MSc / This study examines the relationship between stock returns and market segmentation. Monthly returns of stocks listed on the JSE from 1997-2007 are analysed using mostly the analytic factor and cluster analysis techniques. Evidence supporting the use of multi-index models in explaining the return generating process on the JSE is found. The results provide additional support for Van Rensburg (1997)'s hypothesis on market segmentation on the JSE.

nÃo hà / O presente artigo busca uma mÃtrica refinada e confiÃvel para mensurar riscos financeiros. RiskMetrics (1994) marcou o inÃcio dessa busca e desde entÃo vÃrios pesquisadores contribuÃram com inovaÃÃes e novos modelos para essa medida e aqui se apresenta mais um passo desse caminho, ao se agregar uma modelagem multivariada. Com essa modelagem à possÃvel capturar o efeito contÃgio e a interdependÃncia financeira global. O grupo de 10 paÃses presente no estudo representa 49,9% do PIB mundial e possuem representantes de 5 continentes. O modelo de volatilidade segue sugestÃo apresentada por Cappielo, Engle e Sheppard (2006) e modelos de Value-at-Risk (VaR) seguem Matos, Cruz, Macedo e Jucà (CAEN-UFC Workingpaper). AtravÃs desse procedimento à possÃvel calcular VaR levando em consideraÃÃo o efeito contÃgio e a interdependÃncia entre os mercados ao longo do tempo. Os resultados encontrados sÃo robustos contra problemas de variÃveis omitidas, heterocedasticidade e endogeneidade, alÃm de considerar quebras estruturais. De acordo com os resultados encontrados, a interdependÃncia apresenta um papel importante dentro do processo de mensuraÃÃo de risco de mercado, apesar de atà agora ter sido esquecida pelos pesquisadores. Isso se deve, principalmente, porque a integraÃÃo financeira a nÃvel global leva ao cenÃrio de dependÃncia crescente entre os mercados financeiros e, dessa forma, aumentando o contÃgio de um impacto que ocorre em um mercado nos outros. Convidamos outros pesquisadores a rever nossa metodologia, utilizando inclusive mais informaÃÃes e incluindo outros paÃses. Acredita-se que o mundo està ano a ano se tornando mais globalizado e suas economias por consequÃncia. Nesse artigo esse efeito està sendo considerado dentro da mensuraÃÃo do risco de mercado. Incorporar esse efeito leva a modelagem, legal e interna, mais acurada, que ajuda supervisores de mercado a garantirem estabilidade de longo prazo para os mercados e possuÃrem mÃtricas mais confiÃveis dentro das instituiÃÃes sob sua tutela. AlÃm disso, à de grande valia para Ãreas de GestÃo de Risco de bancos e instituiÃÃes financeiras ao ajuda-las a compreender melhor seu perfil de risco, melhorar a comunicaÃÃo com investidores institucionais internacionais e ranquear de maneira mais eficiente seus investimentos e aplicaÃÃes. Estudos anteriores possuem um aspecto comum: Apenas levam em consideraÃÃo mudanÃas de volatilidade nos mercados domÃsticos, nÃo levando em consideraÃÃo os efeitos que outros paÃses possuem neles. No presente estudo, esse efeito se provou como importante e representativo, os modelos univariados domÃsticos falharam mais e com mais severidade que os modelos multivariados. Portanto, no presente artigo, buscou-se o desafio de dar o passo de nÃo mais modelar modelos univariados domÃsticos, mas modelos 4 multivariados globais. Acredita-se que esse avanÃo metodolÃgico ajudarà a melhor mensurar e entender o comportamento do risco de mercado atravÃs do mundo. / This paper enter into the search of a refined and trustable metric for measuring financial risk. RiskMetrics (1994) marked the start of this search and since them many researches contributed with innovations and new models for that measure, and here we find a stepforward into the search, by aggregating multivariate models, with this itâs possible to capture the effect of a worldwide contagion and financial interdependence. The group of 10 countries presents in this study represents 49,9% of world GDP and has representation across 5 continents. We follow the model of volatilities suggested in Cappielo, Engle e Sheppard (2006) and Value-at-Risk follows Matos, Cruz, Macedo e Jucà (CAEN-UFC Working paper), though this procedure itâs possible to accurate VaR model, and take in count the contagion and interdependence between markets, in long term. Our results are robust to problems with omitted variable, heteroskedasticity and endogeneity. We also take into account for structural break. According to our results, the interdependence plays an important role into financial risk measure process, although its until now usually forbidden by modelers, mostly because worldâs financial integration leads the global economies to the scenario of increasing dependence among them and contagion effect that spreads the impacts that occur into one market to the others. We invite researchers to revisit this issue in order obtain evidences using larger data and other countries as well. We claim that the world is year by year more globalized, and so are the other economies, here we add this into account for measuring financial risks. This leads to model, legal and internal, more accurate that help supervisors to guarantee the long term stability across the markets, have trustable measure of the financial institutions under their responsibility. Besides, helps the Risk Management area of banks and other financial institutions to better understand their risk profile, improve communication with institutional investors worldwide and rank effiently their investments and applications into the markets. Previous studies have a common aspect: they only consider the volatilities change across the domestic market, not tanking in consider the effect of the other countries into the domestic volatility, and this effect here is proven to be important and representative, the univariate domestic risk measure fails more and harder than the multivariate model. That being said, here we take this step, the challenge of modeling no more univariate, domestic risk measures, but a worldwide multivariate. This is a methodological innovation that helps better measure and understands the financial risks behavior across the world.