Um dos objetivos deste estudo é testar modelos de precificação de ativos financeiros, especialmente o de risco downside de Ang et al. (2006), em todas as ações da Bovespa, para o período que se estende de janeiro de 1999 a julho de 2012. Para atingi-lo, aplica-se o método de regressões Fama e MacBeth (1973) com retornos um período à frente. A quase totalidade dos modelos testados é rejeitada, inclusive a existência de um eventual prêmio para o risco downside. A exceção é o modelo que inclui com o beta tradicional e o seu quadrado, o que permite rejeitar o CAPM devido a não linearidade no risco de mercado. A relação existente entre o beta e o retorno das ações seria positiva até beta igual a 0,642 e depois negativa. Outra meta desta dissertação é comparar as estimações condicionais às não condicionais do modelo CoVaR de Adrian e Brunnermeier (2011) para as 16 ações da Bovespa utilizadas por Almeida et al. (2012), que obtiveram apenas estimações não condicionais para o Brasil em um período semelhante. Os resultados daqui mostram uma baixa e não estatisticamente significante correlação com os de Almeida et al. (2012). Para este estudo, tem-se que as duas formas de calcular o CoVaR são similares para o teste de estresse, mas não para o risco sistêmico. / This research pursues as an objective to test cross-sectional returns of some asset pricing models, specially the downside risk suggested by Ang et al. (2006). To accomplish this goal, all the Brazilian Bovespa\'s stocks are used, from January 1999 to July 2012, in one month forward returns Fama-MacBeth regressions. Not only the downside risk model is rejected: almost all models, including the traditional CAPM and versions of the 3 factors Fama-French. A nonlinear CAPM (beta and beta squared) is the exception in the universe of tested models, which produces the best predictions and a positive relationship between betas and forward returns until beta equals 0,642, after this value, the relationship becomes negative. Another issue followed by this study is to compare conditional estimates of the CoVaR model of Adrian and Brunnermeier (2011) with the unconditional ones for the sixteen stock used by Almeida et al. (2012) unconditionally estimates. The results show low and not statistically significant correlation with Almeida\'s estimates. For the sample used here, comparing the conditional and the unconditional methodologies suggests a great similarity for the stress test, but not so close results for the systemic risk.

Um dos objetivos deste estudo é testar modelos de precificação de ativos financeiros, especialmente o de risco downside de Ang et al. (2006), em todas as ações da Bovespa, para o período que se estende de janeiro de 1999 a julho de 2012. Para atingi-lo, aplica-se o método de regressões Fama e MacBeth (1973) com retornos um período à frente. A quase totalidade dos modelos testados é rejeitada, inclusive a existência de um eventual prêmio para o risco downside. A exceção é o modelo que inclui com o beta tradicional e o seu quadrado, o que permite rejeitar o CAPM devido a não linearidade no risco de mercado. A relação existente entre o beta e o retorno das ações seria positiva até beta igual a 0,642 e depois negativa. Outra meta desta dissertação é comparar as estimações condicionais às não condicionais do modelo CoVaR de Adrian e Brunnermeier (2011) para as 16 ações da Bovespa utilizadas por Almeida et al. (2012), que obtiveram apenas estimações não condicionais para o Brasil em um período semelhante. Os resultados daqui mostram uma baixa e não estatisticamente significante correlação com os de Almeida et al. (2012). Para este estudo, tem-se que as duas formas de calcular o CoVaR são similares para o teste de estresse, mas não para o risco sistêmico. / This research pursues as an objective to test cross-sectional returns of some asset pricing models, specially the downside risk suggested by Ang et al. (2006). To accomplish this goal, all the Brazilian Bovespa\'s stocks are used, from January 1999 to July 2012, in one month forward returns Fama-MacBeth regressions. Not only the downside risk model is rejected: almost all models, including the traditional CAPM and versions of the 3 factors Fama-French. A nonlinear CAPM (beta and beta squared) is the exception in the universe of tested models, which produces the best predictions and a positive relationship between betas and forward returns until beta equals 0,642, after this value, the relationship becomes negative. Another issue followed by this study is to compare conditional estimates of the CoVaR model of Adrian and Brunnermeier (2011) with the unconditional ones for the sixteen stock used by Almeida et al. (2012) unconditionally estimates. The results show low and not statistically significant correlation with Almeida\'s estimates. For the sample used here, comparing the conditional and the unconditional methodologies suggests a great similarity for the stress test, but not so close results for the systemic risk.

The first essay of this dissertation investigates the relationship between downside risk and returns of real estate investment trusts (REITs) and assesses the performance of real estate mutual funds (REMFs). We measure the asymmetric risk through downside and upside betas and through the measures incorporated higher moments such as coskewness and Leland's beta. We do not find significant contemporary relationship between the asymmetric risk and returns of REITs. There are only a small portion of REITs reacting to up and down market conditions differently. We find weak evidence that this asymmetric movement of REITs to market may be due to small and value components embedded in REITs. We evaluate the performance of real estate mutual funds (REMFs) from the asymmetric risk perception. According to our results, most of REMFs do not outperform the market. The downside risk helps to explain some of the abnormal returns associated with REMFs. However, the evaluation may be sensitive to the choices of the model and the market index being used. The second essay examines the liquidity of Asian REITs. We use various measures to assess the liquidity of JREITs and SREITs. The overall evidence indicates that the liquidity of JREITs is greater than that of SREITs. Comparing to non-REIT stocks, JREITs are less liquid than Japanese common stocks while there is no significant difference in liquidity between SREITs and Singaporean common stocks. There is also strong evidence that US REITs have smaller spreads and are traded more often than both JREITs and SREITs. We also find that the primary determinants of JREIT spreads are turnover and return volatility. The secondary factors that affect the spread of JREITs are life and property holdings. The dominant factors affecting SREITs' spreads are price, return volatility, and life. The significance of life suggests that there is a learning effect existed in both JREIT and SREIT markets in 2005.

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.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-06-28T19:37:30Z No. of bitstreams: 1 christianjonnatanjacobsensotoherrera.pdf: 883027 bytes, checksum: 3ee1cf348a7392e28d4ef150125ad72c (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-08-07T21:48:11Z (GMT) No. of bitstreams: 1 christianjonnatanjacobsensotoherrera.pdf: 883027 bytes, checksum: 3ee1cf348a7392e28d4ef150125ad72c (MD5) / Made available in DSpace on 2017-08-07T21:48:11Z (GMT). No. of bitstreams: 1 christianjonnatanjacobsensotoherrera.pdf: 883027 bytes, checksum: 3ee1cf348a7392e28d4ef150125ad72c (MD5) Previous issue date: 2017-03-01 / Esta dissertação propõe testar empiricamente, através de otimizações in sample, os modelos de downside risk, Sortino, Upside Pontential Ratio, Omega e Kappa, comparado-os com o tradicional CAPM, derivado a partir da fronteira de média e variância, utilizando as ações listadas no Ibovespa e Dow Jones (DJIA) para construção de carteiras de mercado para cada um dos modelos. Estas duas classes de modelos distinguem-se quanto aos pressupostos e à mensuração do risco. Enquanto o CAPM considera apenas os dois primeiros momentos da distribuição de retornos, as outras medidas levam em conta os momentos superiores. Através da distância Hansen-Jagannathan, que mede o erro de mensuração do Stochastic Discount Factor (SDF) gerado pelos modelos, observou-se grande distinção dos modelos nos dois mercados. Enquanto o CAPM performou melhor no Dow Jones, os modelos de downside risk apresentaram melhores resultados para o Ibovespa, sugerindo vantagem na utilização destes modelos em mercados com menor liquidez e maior assimetria. / This dissertation proposes empirically test the downside risk models, Sortino, Upside Pontential Ratio, Omega and Kappa, by comparing them with the traditional CAPM, derived from the mean and variance boundary, using the listed shares in the Ibovespa and Dow Jones (DJIA) for the construction of market portfolios for each of the models. These two classes of models are distinguished in terms of assumptions and risk measurement. While the CAPM considers only the first two moments of the returns distribution, the other measures take into account the higher moments of such distributions. The Hansen-Jagannathan distance, which measures the Stochastic Discount Factor (SDF) measurement error generated by the models, showed a great distinction of the models in the two markets. While the CAPM performed better in the Dow Jones, the downside risk models presented better results for the Ibovespa, suggesting an advantage in the use of such models in markets with lower liquidity and greater asymmetry.

La méthode d'optimisation d'un portefeuille issue de la minimisation du DownSide Risk a été mise au point pour suppléer les carences de la méthode classique de Markowitz dont l'hypothèse de la normalité de la distribution des rendements se trouve défaillante très souvent. Dans cette thèse, nous proposons d'introduire des estimateurs non paramétriques de la moyenne ou de la médiane conditionnelle pour remplacer les rendements observés d'un portefeuille ou des actifs constituant un portefeuille dans le cas du DownSide Risk. Ces estimateurs nous permettent d'obtenir des frontières efficientes lisses et facilement interprétables. Nous développons des algorithmes itératifs pour résoudre les différents problèmes d'optimisation permettant d'obtenir des portefeuilles optimaux. Nous proposons aussi une nouvelle mesure de risque dit risque conditionnel qui tient compte des anticipations des valeurs futures des différents rendements. Pour le définir nous avons fait appel aux prédicteurs non paramétriques basés sur l'estimation de la moyenne conditionnelle. Enfin, nous avons testé et validé toutes nos méthodes sur des données issues de différents marchés et nous avons montré leur performance et leur efficacité comparées aux méthodes classiques / The DownSide Risk (DSR) model for portfolio optimization allows to overcome the drawbacks of the classical Mean-Variance model concerning the asymmetry of returns and the risk perception of investors. This optimization model deals with a positive definite matrix that is endogenous with respect to the portfolio weights and hence leads to a non standard optimization problem. To bypass this hurdle, we developed a new recursive minimization procedure that ensures the convergence to the solution and gives a smooth portfolio efficient frontier. Our method consists in replacing all the returns by their nonparametric estimators counterpart using kernel mean or median regressions. This technique provides an effect similar to the case where an infinite number of observations is available. We also develop a new portfolio optimization model where the risks are measured through conditional variance or semivariance. This strategy allows us to take advantage from returns prediction which are obtained by nonparametric univariate methods. The prediction step uses kernel estimation of the conditional mean. Data from different markets are used to test and validate the proposed approaches, and results indicate better overall performance

No description available.

Risk management in portfolio construction is a widely discussed topic and the tradeoff between risk and return is always considered before an investment is made. Modern portfolio theory is a mathematical framework which describes how a rational investor can use diversification to optimize a portfolio, which suggests using variance to measure financial risk. However, since variance is a symmetrical metric, the framework fails to correctly account for the loss aversion preferences most investors exhibit. Therefore, the use of downside risk measures were proposed, which only measures the variance of the portfolio below a certain threshold, usually set to zero or the risk-free rate. This thesis empirically investigates the differences in performance between the two risk measures when used to solve a real world portfolio optimization problem. Backtests using the different measures on all major Nordic equity markets are performed to highlight the dynamics between the frameworks, and when one should be preferred over the other. It is concluded that the optimization frameworks indeed provides a useful tool for investors to construct great performing portfolios. However, even though the downside risk framework is more mathematically rigorous, implementing this risk measure instead of variance seems to be of less importance for the actual results. / Riskhantering för aktieportföljer är mycket centralt och en avvägning mellan risk och avkastning görs alltid innan en investering. Modern Portföljteori är ett matematiskt ramverk som beskriver hur en rationell investerare kan använda diversifiering för att optimera en portfölj. Centralt för detta är att använda portföljens varians för att mäta risk. Dock, eftersom varians är ett symmetriskt mått lyckas inte detta ramverk korrekt ta hänsyn till den förlustaversion som de flesta investerare upplever. Därför har det föreslagits att istället använda olika mått på nedsiderisk (downside risk), som endast tar hänsyn till portföljens varians under en viss avkastningsgräns, oftast satt till noll eller den riskfria räntan. Denna studie undersöker skillnaderna i prestation mellan dessa två riskmått när de används för att lösa ett verkligt portföljoptimeringsproblem. Backtests med riskmåtten har genomförts på de olika nordiska aktiemarknaderna för att visa på likheter och skillnader mellan de olika riskmåtten, samt när det enda är att föredra framför det andra. Slutsatsen är att ramverken ger investerare ett användbart verktyg för att smidigt optimera portföljer. Däremot verkar den faktiska skillnaden mellan de två riskmåtten vara av mindre betydelse för portföljernas prestation. Detta trots att downside risk är mer matematiskt rigoröst.

Consumer sentiment has the ability to provide researchers with many avenues to test existing Finance and Economic theories. Chapter 1 introduces the issues that I seek to explore within the area of Behavioral Finance. Chapter 2 utilizes thirty years of consumer sentiment data to explore extant economic theories and hypotheses. In particular, I study the Prospect Theory and the Life Cycle Investment Hypothesis. In addition, I also study how changes in consumer sentiment can foretell future stock returns for firms in different industries and of different sizes. By studying how individuals of different ages display optimism and pessimism through consumer sentiment surveys, I am able to contribute to the literature by shedding additional light on just how the important age is with respect to a person's economic outlook. One particular phenomenon that I discuss in this chapter is downside risk. I will provide further support to the existing literature which shows that gains and losses are not viewed equally by individuals. To account for this discrepancy, this paper models the time series relationship between consumer sentiment and stock returns using asymmetric response models. Chapter 3 builds upon the previous chapter's findings by using consumer sentiment to explore if this index can forecast housing market variables such as changes in home sales and home prices. Given the recent financial market turmoil that stemmed from the U.S. housing market debacle, this chapter is timely. Using widely cited housing indices, I explore regional differences in the U.S. housing market and how the sentiment of local consumers can possibly affect their housing markets. I also include analyses in which the age of the consumer is accounted for to see if evidence of the Life Cycle Investment Hypothesis emerges. This theory postulates that younger individuals are more likely to demand housing as a financial asset and if this were true, I hypothesize that changes in younger individuals' sentiment would have more forecasting power with respect to future housing sales and price changes. Lastly, I conclude this dissertation with Chapter 4 which includes additional discussions of the issues studied.

This thesis investigates different aspects of the impact of extreme downside risk on stock returns. We first investigate the impact at market level, where the return of the stock market index is expected to be positively correlated to its tail risk. More specifically, we incorporate Markov switching mechanism into the framework of Bali et al. (2009) to analyse the relationship between risk and returns under different market regimes. Interestingly, although highly significant in calm periods, the tail risk-return relationship cannot be captured during turbulent times. This is puzzling since this is the time when the distress risk is most prominent. We show that this pattern persists under different modifications of the framework, including expanding the set of state variables and accounting for the non-iid feature of return process. We suggest that this result is due to the leverage and volatility feedback effects. To better filter out these effects, we propose a simple but effective modification to the risk measures which reinstates the positive extreme risk-return relationship under any state of market volatility. The success of our method provides insights into how extreme downside risk is factored into expected returns. In the second investigation, this thesis explores the impact of extreme downside risk on returns in a security level analysis. We demonstrate that a stock with higher tail risk exposure tends to experience higher average returns. Motivated by the limitations of systematic extreme downside risk measures in the literature, we propose two groups of new ‘co-tail-risk’ measures constructed from two different approaches. The first group is the natural development of canonical downside beta and comoment measures, while the second group is based on the sensitivity of stock returns on innovations in market systematic crash risk. We utilise our new measures to investigate the asset pricing implication of extreme downside risk and show that they can capture a significant positive relationship between this risk and expected stock return. Moreover, our second group of ‘co-tail-risk’ measures show a highly consistent performance even in extreme settings such as low tail threshold and monthly sample estimation. The ability of this measure to generate a number of observations given limited return data solves one of the most challenging problems in tail risk literature. In the last investigation, this thesis examines the influence of extreme downside risk on portfolio optimisation. It is motivated by the evidence in Chapter 4 regarding the size pattern of the extreme downside risk impact on stock returns where the impact is larger for small stocks. Accordingly, portfolio optimisation practice that focuses on tail risk should be more effective when applied to small stocks. In comparing the performance of mean-Expected Tail Loss against that of mean-variance across size groups of Fama and French’s (1993) sorted portfolios, we confirm this conjecture. Moreover, we further investigate the performance of different switching approaches between mean-variance and mean-Expected Tail Loss to utilise the suitability of these optimisation methods for specific market conditions. However, our results reject the use of any switching method. We demonstrate the reason switching could not enhance performance is due to the invalidity of the argument regarding the suitability of any optimisation method for a specific market regime.