This study tests whether contagion effects existed during the ¡§subprime mortgage crisis¡¨ among the equity markets of the US, the EU, Asia and emerging markets. The time-varying correlation coefficients are estimated by the dynamic conditional correlation (DCC) of Engle (2002), using a multivariate GJR-GARCH with AR (1) model. The empirical findings show that the conditional correlation coefficients of stock returns between the U.S. and others countries were positive and that the contagion effect exists among stock markets. Financial markets displayed contagion effects, in that the global equity markets were confronted with elevated systematic risk at the same time. Therefore, this study further examines the role of systematic risk in the equity market of each country. I used the rolling formulae, the MV-DGP, and DCC-GARCH (1, 1) models to estimate the CAPM beta and downside betas. This study found higher systematic risk (downside systematic risk) in the stock markets of the United States, Germany, France and Brazil, which had beta values nearly above one, while the Chinese stock market had the lowest systemic risk and served as a hedge for investors and fund managers. Finally, the results demonstrate that DCC-HW beta can capture some downside linkages between the market portfolios and expected stock returns, while these linkages cannot likely be captured by the CAPM beta.

This study aims to analyze the beta and risk behavior of the Swedish listed real estate stocks. Such a study will provide a clearer picture for investors and researchers about the changing nature of that behavior over time. The research method is based on descriptive statistics and CAPM beta regression analysis of the monthly returns. Correlation analysis is employed to identify diversification benefits within the sector stocks. In order to understand the behavior of beta/riskiness over time, the stationary and time-varying beta estimations are conducted using CAPM market excess-return model and rolling windows technique. In this investigation, the time period from 2003 to 2012 is analyzed. The results reveal that a) the betas of real estate stocks are asymmetric over time such that their values are higher during market upturns than in market downturns, b) the betas for the various types of real estate stocks are different, and c) there are low correlation coefficients among returns of real estate stocks, and within the various property type stock groups. While the real estate stock index as a whole is highly correlated to the market and has relatively stable betas over time, there are diversification benefits among Swedish real estate stocks. Hence, understanding the changing behaviors of beta over time of the various property type stocks can help investors optimize their market timing and cost of capital expectations according to the investment horizon. It is important to notice that a lot of capital for real estate equity investments in Sweden is allocated through non-traded private equity real estate funds. Therefore, transforming these private funds into real estate traded funds might add the data depth and the market efficiency necessary for better research validity and investment optimization. There are currently very few traded real estate securities in the Swedish market.

My dissertation investigates the financial linkages and transmission of economic shocks between the US and the smallest emerging markets (frontier markets). The first chapter sets up an empirical model that examines the impact of US market returns and conditional volatility on the returns and conditional volatilities of twenty-one frontier markets. The model is estimated via maximum likelihood; utilizes the GARCH model of errors, and is applied to daily country data from the MSCI Barra. We find limited, but statistically significant exposure of Frontier markets to shocks from the US. Our results suggest that it is not the lagged US market returns that have impact; rather it is the expected US market returns that influence frontier market returns The second chapter sets up an empirical time-varying parameter (TVP) model to explore the time-variation in the impact of mean US returns on mean Frontier market returns. The model utilizes the Kalman filter algorithm as well as the GARCH model of errors and is applied to daily country data from the MSCI Barra. The TVP model detects statistically significant time-variation in the impact of US returns and low, but statistically and quantitatively important impact of US market conditional volatility. The third chapter studies the risk-return relationship in twenty Frontier country stock markets by setting up an international version of the intertemporal capital asset pricing model. The systematic risk in this model comes from covariance of Frontier market stock index returns with world returns. Both the systematic risk and risk premium are time-varying in our model. We also incorporate own country variances as additional determinants of Frontier country returns. Our results suggest statistically significant impact of both world and own country risk in explaining Frontier country returns. Time-variation in the world risk premium is also found to be statistically significant for most Frontier market returns. However, own country risk is found to be quantitatively more important.

<p>The cross-sectional variation of stock returns used to be described by the Capital Asset Pricing Model until the early 90‟s. Anomalies, such as, book-to-market effect and small firm effect undermined CAPM‟s ability to explain stock returns and Fama & French (1992) have shown that simple firm attributes, like, firm size and book-to-market value can explain the returns far better than Beta. Following Fama & French many other researchers examine the explanatory powers of CAPM and other asset pricing models. However, most of those studies use US data. There are some researches done in different countries than US, however more out-of-sample studies need to be conducted.</p><p>To our knowledge there are very few studies using the Swedish data and this thesis contributes to that small pool of studies. Moreover, the studies testing the CAPM use the unconditional version of the model. There are some papers suggesting the use of a conditional CAPM that would exhibit better explanatory powers than the unconditional CAPM. Different ways of conditioning the CAPM have been proposed, but one that we think is the least complex and possible to make use of in the business world is the dual-beta model. This conditional CAPM assumes a different relationship between beta and stock returns during the up markets and down markets. Furthermore, the model has not thoroughly been tested outside the US. Our study is the first to use the dual-beta model in Sweden. In addition, the momentum effect has lately been given some attention and Fama & French‟s (1993) three factor model has not been able to explain the abnormal returns related to that anomaly. We test the Fama & French three factor model, CAPM and Carhart‟s four factor model‟s explanatory abilities of the momentum effect using Swedish stock returns. Ultimately, our aim is to find the best model that describes stock return cross-section on the Stockholm Stock Exchange.</p><p>We use returns of all the non-financial firms listed on Stockholm Stock Exchange between September, 1997 and April, 2010. The number of companies included in our time sample is 366. The results of our tests indicate that the small firm effect, book-to-market effect and the momentum effect are not present on the Stockholm Stock Exchange. Consequently, the CAPM emerges as the one model that explains stock return cross-section better than the other models suggesting that Beta is still a proper measure of risk. Furthermore, the conditional version of CAPM describes the stock return variation far better than the unconditional CAPM. This implies using different Betas to estimate risk during up market conditions and down market conditions.</p>

The cross-sectional variation of stock returns used to be described by the Capital Asset Pricing Model until the early 90‟s. Anomalies, such as, book-to-market effect and small firm effect undermined CAPM‟s ability to explain stock returns and Fama &amp; French (1992) have shown that simple firm attributes, like, firm size and book-to-market value can explain the returns far better than Beta. Following Fama &amp; French many other researchers examine the explanatory powers of CAPM and other asset pricing models. However, most of those studies use US data. There are some researches done in different countries than US, however more out-of-sample studies need to be conducted. To our knowledge there are very few studies using the Swedish data and this thesis contributes to that small pool of studies. Moreover, the studies testing the CAPM use the unconditional version of the model. There are some papers suggesting the use of a conditional CAPM that would exhibit better explanatory powers than the unconditional CAPM. Different ways of conditioning the CAPM have been proposed, but one that we think is the least complex and possible to make use of in the business world is the dual-beta model. This conditional CAPM assumes a different relationship between beta and stock returns during the up markets and down markets. Furthermore, the model has not thoroughly been tested outside the US. Our study is the first to use the dual-beta model in Sweden. In addition, the momentum effect has lately been given some attention and Fama &amp; French‟s (1993) three factor model has not been able to explain the abnormal returns related to that anomaly. We test the Fama &amp; French three factor model, CAPM and Carhart‟s four factor model‟s explanatory abilities of the momentum effect using Swedish stock returns. Ultimately, our aim is to find the best model that describes stock return cross-section on the Stockholm Stock Exchange. We use returns of all the non-financial firms listed on Stockholm Stock Exchange between September, 1997 and April, 2010. The number of companies included in our time sample is 366. The results of our tests indicate that the small firm effect, book-to-market effect and the momentum effect are not present on the Stockholm Stock Exchange. Consequently, the CAPM emerges as the one model that explains stock return cross-section better than the other models suggesting that Beta is still a proper measure of risk. Furthermore, the conditional version of CAPM describes the stock return variation far better than the unconditional CAPM. This implies using different Betas to estimate risk during up market conditions and down market conditions.

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