The intent of this thesis is to investigate how US equity funds performance differ due to their standard deviation. In order to accomplish this study, we collected daily data for 99 US equity funds for the period 2011-2020 and divided the funds into three risk classification groups based on their standard deviation for the year 2011. The collected data was used to perform an CAPM regression and to calculate returns on a three-, five- and ten-year basis. The results for the regression and the returns for the funds was later presented as average values for the different risk classification groups. We then compared the average outcomes for the three risk classifications with each other and the index S&P 500. Our result showed that the index S&P 500 outperformed the three risk classification groups average returns for every time period. We also noticed that the difference between the average returns and the index got greater by time. We did not find any big differences between our risk classifications when it comes to their performance. Our regression analysis resulted in many negative alpha values indicating that S&P 500, as many previous studies claims, outperforms actively mutual funds. The conclusion is therefore that we could not show any evidence that the there is a major different in performance between our risk groups but also that it is difficult for fund managers to outperform index.

<p>This study deals with one of the efficient market hypothesis’ anomaly. The research aims at proving the</p><p>existence of a size anomaly by answering the question: can you outperform the market by investing in</p><p>small and mid caps? It is in fact a questioning of the well-know efficient market hypothesis (EMH). We</p><p>investigate the size effect in the situation of a passive strategy with different indices (Russell Indices and</p><p>S&P Indices) from 1995 to 2005.</p><p>The introduction gives to the reader the background he needs to understand the methodology and the</p><p>approach of the issue by the authors. Key concepts are defined such as EMH, passive strategy.</p><p>The second part exposes the methodology the authors choose and the methodology of exploited indices.</p><p>The research consist on measuring the risk adjusting excess returns by comparing the market index</p><p>return (S&P 500 or Russell 3000) and the Small and Mid Caps indices (S&P Small Cap 600, S&P Mid</p><p>Cap 400, Russell Mid Cap and Russell 2000) over the period. Indeed the methodology of indices is</p><p>exposing in details to understand in which extent the study can be influence by the construction of</p><p>indices.</p><p>Then in part 3 the authors describe theories that are possible explanations for the size effect. Then it is</p><p>understandable that the size anomaly is the result of a set of factors that generate abnormal returns.</p><p>These theories help the authors to come up with a model that gives an overview of the research.</p><p>After having explained their research method and reveal their empirical findings. The authors</p><p>demonstrate that excess returns can be earned by investing in small and mid caps indices even after</p><p>controlling for risk. The risk adjusting excess returns their findings can potentially be explained by the</p><p>other factors depicted in the theoretical part. E/P ratios, Trading Costs, January effect, Overreaction are</p><p>possible reasons to explain the size anomaly. They also find an instability and/or reversal of the size</p><p>effect consistent with one of the theories. However the authors find data with non statistic significance,</p><p>so I accept the null hypothesis that the excess returns of small and mid caps indices are equal to zero.</p><p>The paper ends with a discussion about the limitations of the study and possible further researches. The</p><p>authors conclude that even if the existence of a size effect is obvious for some years and horizons of</p><p>investment, the passive strategy appears to be an unsuited method to take advantage of the small effect</p><p>since the results reject the null hypothesis. The authors clarify the fact that before investing in small and</p><p>mid caps, one has to be aware of all the factors that can influence his investment (beside risk) because</p><p>the size effect is a set of factors.</p><p>Key words: Efficient Market Hypothesis, Abnormal returns, Size effect (anomaly), Passive strategy,</p><p>Market Index, S&P indices, Russell indices</p>

This study deals with one of the efficient market hypothesis’ anomaly. The research aims at proving the existence of a size anomaly by answering the question: can you outperform the market by investing in small and mid caps? It is in fact a questioning of the well-know efficient market hypothesis (EMH). We investigate the size effect in the situation of a passive strategy with different indices (Russell Indices and S&amp;P Indices) from 1995 to 2005. The introduction gives to the reader the background he needs to understand the methodology and the approach of the issue by the authors. Key concepts are defined such as EMH, passive strategy. The second part exposes the methodology the authors choose and the methodology of exploited indices. The research consist on measuring the risk adjusting excess returns by comparing the market index return (S&amp;P 500 or Russell 3000) and the Small and Mid Caps indices (S&amp;P Small Cap 600, S&amp;P Mid Cap 400, Russell Mid Cap and Russell 2000) over the period. Indeed the methodology of indices is exposing in details to understand in which extent the study can be influence by the construction of indices. Then in part 3 the authors describe theories that are possible explanations for the size effect. Then it is understandable that the size anomaly is the result of a set of factors that generate abnormal returns. These theories help the authors to come up with a model that gives an overview of the research. After having explained their research method and reveal their empirical findings. The authors demonstrate that excess returns can be earned by investing in small and mid caps indices even after controlling for risk. The risk adjusting excess returns their findings can potentially be explained by the other factors depicted in the theoretical part. E/P ratios, Trading Costs, January effect, Overreaction are possible reasons to explain the size anomaly. They also find an instability and/or reversal of the size effect consistent with one of the theories. However the authors find data with non statistic significance, so I accept the null hypothesis that the excess returns of small and mid caps indices are equal to zero. The paper ends with a discussion about the limitations of the study and possible further researches. The authors conclude that even if the existence of a size effect is obvious for some years and horizons of investment, the passive strategy appears to be an unsuited method to take advantage of the small effect since the results reject the null hypothesis. The authors clarify the fact that before investing in small and mid caps, one has to be aware of all the factors that can influence his investment (beside risk) because the size effect is a set of factors. Key words: Efficient Market Hypothesis, Abnormal returns, Size effect (anomaly), Passive strategy, Market Index, S&amp;P indices, Russell indices

One of the most popular investment and trading strategies over the last decade, has been the currency carry trade, which allows traders and investors to buy high-yielding currencies in the Foreign Exchange spot market by borrowing, low or zero interest rate currencies in the form of pairs, such as the Australian Dollar/Japanese Yen (AUD/JPY), with the purpose of investing the proceeds afterwards into fixed-income securities.To be able to determine the causality between the returns of equity markets and the foreign exchange market, we choose to observe the sensitivity and influence of two equity indexes on several pairs involved in carry trading. The reason for studying these relationships is to further explain the causes of the uncovered interest parity puzzle, thus adding our contribution to the academic field through this thesis.To accomplish our goals, data was gathered for daily quotes of 16 different currency pairs, grouped by interest differentials, and two equity indexes, the S&amp;P 500 and FTSE All-World, along with data for the VIX volatility index, for the 2003-2012 period. The data was collected from Thomson Reuters Datastream and the selected ten year span was divided into three different periods. This was done in order to discover the differences on how equity indexes relate to typical carry trade currency pairs, depending on market developments before, during and after the world financial crisis.The tests conducted on the collected data measured the correlations, influences and sensitivity for the 16 different currency pairs with the S&amp;P 500 Index, the FTSE All-World index, and the volatility index between the years of 2003-2012. For influences and sensitivity, we performed Maximum Likelihood (ML) regressions with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) [1,1], in Eviews software.After analyzing the results, we found that, during our chosen time period, the majority of currency pair daily returns are positively correlated with the equity indexes and that the FX pairs show greater correlation with the FTSE All-World, than with the S&amp;P 500. Factors such as the interest rate of a currency and the choice of funding currency played an important role in the foreign exchange markets, during the ten year time span, for every yield group of FX pairs.Regarding the influence and sensitivity between currency pairs and the S&amp;P 500 with its VIX index, we found that our models explanatory power seems to be stronger when the interest rate differential between the currency pairs is smaller. Our regression analysis also uncovered that the characteristics of an individual currency can show noticeable effects for the relationship between its pair and the two indexes.

This paper attempts to assess the impact of price fluctuations in oil resulting from worldwide oil supply shocks on the real returns of the U.S. stock market, specifically the S&P 500, during the period of 1986 to 2011. While much past research has found an inverse relationship to exist between simply oil price increases and stock market returns, not many studies have been conducted that focus on the effects of shifts in oil supply. The model utilized, a variation of that used by Hamilton (2008), determines that changes in oil prices arising from oil supply shocks one quarter prior (t-1) and one year prior (t-4) have an effect on real stock returns. However, an F-test assessing the joint impact of the explanatory variables is unable to reject the null hypothesis that the joint effects of changes in oil prices arising from supply shocks have zero effect on the returns of the stock market.

A volatilidade é uma medida de incerteza quanto às variações dos preços de ativos. Este trabalho tem como objetivo analisar a volatilidade, através dos diversos modelos da família GARCH, de três índices de mercados financeiros: Dow Jones, IBovespa e S&P 500. Com este intuito, foram aqui utilizadas técnicas univariadas e multivariadas, bem como análises de Causalidade de Granger. Através das duas primeiras ferramentas, escolhemos o melhor modelo para cada um destes casos. Usando a terceira ferramenta, concluímos que o IBovespa é significativamente influenciado pela abertura do Dow Jones e do S&P500. Por outro lado, mostramos que a abertura do IBovespa não impacta, nem à 10% de significância, os índices Dow Jones e S&P 500. Também concluímos que a incorporação de um dos índices americanos ao modelo do IBovespa torna-o mais significativo, uma vez que o mercado acionário brasileiro é impactado pelos dois índices citados anteriormente. Desta forma, este trabalho mostra que os modelos GARCH multivariados aparentam ser mais eficazes na estimação da volatilidade de ativos financeiros do que os modelos GARCH univariados. / The volatility is a measure of the uncertainty of variations of asset prices. The main goal of this work is to analyze the volatility, by the use of several models of the GARCH family, of three financial market indexes: Dow Jones, IBovespa and S&P 500. With this purpose, we use univariate and multivariate techniques, as well as Granger Causality. Using these first two tools, we choose the best model for each one of these cases. Using the third tool, we conclude that the IBovespa is significatively influenced by the opening of the Dow Jones and the S&P 500 indexes. On the other hand, we show that the opening of the IBovespa does not impact, not even at 10% of significance, the Dow Jones and S&P 500 indexes. We also conclude that incorporation of one of these American indexes to the model involving IBovespa makes it more significant, once the Brazilian Stock Market is impacted by the two American indexes we mention before. This work shows that multivariate GARCH models seem to be more efficient in the volatility estimation of financial assets than univariate GARCH models.

This thesis deals with the analysis of the profitability and risk of selected sectors on a stock exchange. For analysis of the industry period of 5 years was selected. This period begins in January 2010 and ends in December 2014. Data for the analysis were obtained from the New York Stock Exchange. Ratings industry is based on key indicators of profitability and risk. The profitability of the sector was calculated average and total. The risk was assessed by standard deviation, variance and coefficient of variation. The next step was to evaluate the sector by pricing model of capital asset. The coefficients alpha and beta were obtained by linear regression. MS Excel software was used for calculation. The first part describes the capital market, its subjects and the stock exchanges. For assessing the shares the basic formulas for calculating profitability, risk and CAPM are described in the theoretical part. Methodology paper describes the procedure for evaluating stocks and sectors. There is described a precise procedure of calculating individual indicators. In the third section the results of the analyzed sectors are evaluated. There is described the risk assessment of the industry and the future development of the sector. In conclusion the capital market and forecast of its development are evaluated.

A volatilidade é uma medida de incerteza quanto às variações dos preços de ativos. Este trabalho tem como objetivo analisar a volatilidade, através dos diversos modelos da família GARCH, de três índices de mercados financeiros: Dow Jones, IBovespa e S&P 500. Com este intuito, foram aqui utilizadas técnicas univariadas e multivariadas, bem como análises de Causalidade de Granger. Através das duas primeiras ferramentas, escolhemos o melhor modelo para cada um destes casos. Usando a terceira ferramenta, concluímos que o IBovespa é significativamente influenciado pela abertura do Dow Jones e do S&P500. Por outro lado, mostramos que a abertura do IBovespa não impacta, nem à 10% de significância, os índices Dow Jones e S&P 500. Também concluímos que a incorporação de um dos índices americanos ao modelo do IBovespa torna-o mais significativo, uma vez que o mercado acionário brasileiro é impactado pelos dois índices citados anteriormente. Desta forma, este trabalho mostra que os modelos GARCH multivariados aparentam ser mais eficazes na estimação da volatilidade de ativos financeiros do que os modelos GARCH univariados. / The volatility is a measure of the uncertainty of variations of asset prices. The main goal of this work is to analyze the volatility, by the use of several models of the GARCH family, of three financial market indexes: Dow Jones, IBovespa and S&P 500. With this purpose, we use univariate and multivariate techniques, as well as Granger Causality. Using these first two tools, we choose the best model for each one of these cases. Using the third tool, we conclude that the IBovespa is significatively influenced by the opening of the Dow Jones and the S&P 500 indexes. On the other hand, we show that the opening of the IBovespa does not impact, not even at 10% of significance, the Dow Jones and S&P 500 indexes. We also conclude that incorporation of one of these American indexes to the model involving IBovespa makes it more significant, once the Brazilian Stock Market is impacted by the two American indexes we mention before. This work shows that multivariate GARCH models seem to be more efficient in the volatility estimation of financial assets than univariate GARCH models.

A volatilidade é uma medida de incerteza quanto às variações dos preços de ativos. Este trabalho tem como objetivo analisar a volatilidade, através dos diversos modelos da família GARCH, de três índices de mercados financeiros: Dow Jones, IBovespa e S&P 500. Com este intuito, foram aqui utilizadas técnicas univariadas e multivariadas, bem como análises de Causalidade de Granger. Através das duas primeiras ferramentas, escolhemos o melhor modelo para cada um destes casos. Usando a terceira ferramenta, concluímos que o IBovespa é significativamente influenciado pela abertura do Dow Jones e do S&P500. Por outro lado, mostramos que a abertura do IBovespa não impacta, nem à 10% de significância, os índices Dow Jones e S&P 500. Também concluímos que a incorporação de um dos índices americanos ao modelo do IBovespa torna-o mais significativo, uma vez que o mercado acionário brasileiro é impactado pelos dois índices citados anteriormente. Desta forma, este trabalho mostra que os modelos GARCH multivariados aparentam ser mais eficazes na estimação da volatilidade de ativos financeiros do que os modelos GARCH univariados. / The volatility is a measure of the uncertainty of variations of asset prices. The main goal of this work is to analyze the volatility, by the use of several models of the GARCH family, of three financial market indexes: Dow Jones, IBovespa and S&P 500. With this purpose, we use univariate and multivariate techniques, as well as Granger Causality. Using these first two tools, we choose the best model for each one of these cases. Using the third tool, we conclude that the IBovespa is significatively influenced by the opening of the Dow Jones and the S&P 500 indexes. On the other hand, we show that the opening of the IBovespa does not impact, not even at 10% of significance, the Dow Jones and S&P 500 indexes. We also conclude that incorporation of one of these American indexes to the model involving IBovespa makes it more significant, once the Brazilian Stock Market is impacted by the two American indexes we mention before. This work shows that multivariate GARCH models seem to be more efficient in the volatility estimation of financial assets than univariate GARCH models.

Implied volatility obtained from market option prices is widely regarded as an efficient predictor of future realised volatility. Implied volatility can be thought of as market's expectation of future realised volatility. We distinguish between volatility-changing events with respect to expectations - scheduled events (such as information releases) and unscheduled events. We propose a method of testing the information content of option-implied risk-neutral moments prior to volatility-changing events. Using the method introduced by Bakshi, Kapadia & Madan (2003) we extract implied volatility, skewness and kurtosis from S&P 500 options market prices and apply the proposed method in four case studies. Two are concerned with scheduled events - United Kingdom European Union membership referendum, 2016 and United States presidential election, 2016, two are concerned with unscheduled events - flash crash of August 24, 2015 and flash crash of October 15, 2014. Implied volatility indicates a rise in future realised volatility prior to both scheduled events. We find a significant rise in implied kurtosis during the last three days prior to the presidential election of 2016. Prior to unscheduled events, we find no evidence of implied moments indicating a rise in future realised volatility.