The thesis evaluates relationship between probability of default of non-financial corporations and households and evolution of macroeconomic environment. This work contributes to the literature of credit risk proving importance of macroeconomic variables in determining the PDs both on aggregate level and for sector of non-financial corporations and sector of households in the Czech Republic. Evaluation of an impact of the recent financial crisis on the PDs are done by employing latent factor model and FAVAR model on monthly data of non-performing loans and other macroeconomic variables covering the period 01/2002-06/2013. Finally, an ability to forecast and fit the data of FAVAR model and one factor latent model are compared. The comparison indicates that latent factor model should be more appropriate than FAVAR model.

The trait approach to personality is one of the most influential epistemological frameworks in personality psychology and underlies the development of most objective personality inventories. The Revised NEO Personality Inventory (NEO-PI-R) is amongst the most widely used operationalisations of the FFM within personality assessment (Costa and McCrae, 1992). However, recent research (Franklin, 2009; Laher, 2010) suggests that the NEO-PI-R is not wholly applicable within the South African context; as there may be inappropriate items contained within the inventory, both linguistically and culturally. Within the United States, McCrae, Costa and Martin (2004) identified the NEO-PI-R as having specific problematic items, and developed the NEO-PI-3 as a revised “more readable” version of the NEO-PI-R. Thirty-seven items were changed from the original 240 items in the NEO-PI-R to create the NEO-PI-3. However, the modifications made to the NEO-PI-3 did not address all the issues pertinent within the NEO-PI-R from a South African perspective as evidenced in Laher’s (2010) and Franklin’s (2009) studies. This study adapted the NEO-PI-3 by changing specific items informed through Franklin (2009) and Laher’s (2010) research, as well as research conducted within this study on two samples of university lectures at the University of the Witwatersrand. Forty-nine items were changed from the original 240 NEO-PI-R items, retaining 30 items changed from the NEO-PI-3, and preferring 3 of the original NEO-PI-R items to the NEO-PI-3 items. These changes were aimed at making the NEO-PI-3 a more appropriate and applicable instrument both culturally and linguistically within the South African context. This modified inventory was then administered to 175 students at the University of the Witwatersrand to test the inventory’s validity and reliability. The reliability of this modified inventory was assessed through conducting an internal consistency analysis generating alpha coefficients indicating that the inventory was indeed reliable. The construct validity of this modified inventory was assessed through an exploratory factor analysis where five factors did emerge from the analysis; concomitant with the theoretical basis of the FFM. Based on feedback from the participants, both quantitatively and qualitatively, recommendations for future research and further problematic items are identified and discussed. In terms of the reliability of the modified version of the NEO-PI-3, internal consistency coefficients produced within the study suggested that the instrument is reliable, producing moderate to good alpha values, as well as producing evidence of good construct validity. Only 17 items emerged as still being potentially problematic within the modified version of the NEO-PI-3.

The estimation results of Brazilian risk premia are not robust in the literature. For instance, among the 133 market risk premium estimates reported on the literature, 41 are positives, 18 are negatives and the remainder are not significant. In this study, we investigate the grounds for this lack of consensus. First of all, we analyze the sensitivity of the US risk premia estimation to two relevant constraints present in the Brazilian market: the small number of assets (137 eligible stocks) and the short time-series sample available for estimation (14 years). We conclude that the second constrain, small T, has greater impact on the results. Following, we evaluate the two potential causes of problems for the risk premia estimation with small T: i) small sample bias on betas; ii) divergence between ex-post and ex-ante risk premia. Through Monte Carlo simulations, we conclude that for the T available for Brazil, the betas estimates are no longer a problem. However, it is necessary to wait until 2041 to be able to estimate ex-ante risk premia with Brazilian data. / Os resultados das estimações de prêmios de risco brasileiros não são robustos na literatura. Por exemplo, dentre 133 estimativas de prêmio de risco de mercado documentadas, 41 são positivas, 18 negativas e o restante não é significante. No presente trabalho, investigamos os motivos da falta de consenso. Primeiramente, analisamos a sensibilidade da estimação dos prêmios de risco norte-americanos a duas restrições presentes no mercado brasileiro: o baixo número de ativos (137 ações elegíveis) e a pequena quantidade de meses disponíveis para estimação (14 anos). Concluímos que a segunda restrição, T pequeno, tem maior impacto sobre os resultados. Em seguida, avaliamos as duas potenciais causas de problemas para a estimação de prêmios de risco em amostras com T pequeno: i) viés de pequenas amostras nas estimativas dos betas; e ii) divergência entre prêmio de risco ex-post e ex-ante. Através de exercícios de Monte Carlo, concluímos que para o T disponível no Brasil, a estimativa dos betas já não é mais um problema. No entanto, ainda precisamos esperar até 2041 para conseguirmos estimar corretamente os prêmios ex-ante com os dados brasileiros.

This project focuses on risk analysis of corporate bond portfolios. We separate the total risk of the portfolio into three parts, which are market risk, credit risk and liquidity risk. The market risk component is quantified by value-at-risk (VaR) determined by change in yield to maturity of the bond portfolio. For the credit risk component, we calculate default probabilities and losses in the event of default and then compute credit VaR. Next, we define a factor called basis which is the difference between the Credit Default Swap (CDS) spread and its corresponding corporate bond yield spread (z-spread or OAS). We quantify the liquidity risk by using the basis. In addition, we also introduce a Fama-French multi-factor model to analyze factor significance to the corporate bond portfolio.

This project focuses on risk analysis of corporate bond portfolios. We divide the total risk of the portfolio into three parts, which are market risk, credit risk and liquidity risk. The market risk component is quantified by value-at-risk (VaR) which is determined by change in yield to maturity of the bond portfolio. For the credit risk component, we calculate default probabilities and losses in the event of default and then compute credit VaR. Next, we define a factor called `basis' which is the difference between the Credit Default Swap (CDS) spread and its corresponding corporate bond yield spread (z-spread or OAS). We quantify the liquidity risk by using the basis. In addition we also introduce a Fama-French multi-factor model to analyze the factor significance to the corporate bond portfolio.

Atualmente há uma quantidade considerável de informação sobre o comportamento da economia à disposição da autoridade monetária, cuja decisão é provavelmente baseada nesse grande conjunto de dados. Entretanto, grande parte das análises empíricas de política monetária é baseada em modelos de pequena escala, e o problema de variáveis omitidas pode ser relevante. Uma literatura mais recente mostrou que grandes conjuntos de séries macroeconômicas podem ser modelados usando fatores dinâmicos, que são considerados um resumo da informação contida nos dados. Neste trabalho combinamos os fatores extraídos de 178 séries de tempo com os modelos tradicionais de pequena escala para analisar a política monetária no Brasil. Os fatores estimados são usados como instrumentos em regras de Taylor forward looking e como regressores adicionais em VAR´s. A informação extraída de grandes conjuntos de dados mostrou-se bem útil na análise empírica da política monetária. / Nowadays there is a considerable amount of information on the behavior of the economy available and central bankers can be expected to base their decisions on this very large information set. Nevertheless, most of the empirical analysis of monetary policy has been based on small scale models, and omitted information can be a relevant problem. Recent time-series techniques have shown that large datasets can be modeled using dynamic factors, which are considered a summary of the information in the data. In this work we combine the factors extracted from 178 time series with more traditional small scale models to analyze monetary policy in Brazil. The estimated factors are used as instruments in forward looking Taylor rules and as additional regressors in VAR´s. The information extracted from large datasets turns out to be quite useful for the empirical analysis of monetary policy.

Psychopathy is a personality disorder characterized by antisocial deviance in the context of interpersonal and emotional detachment. The study of psychopathy in non-forensic samples is an area of growing interest, but one that is limited by the fact that most large-scale epidemiological studies, which collect a wealth of data that could further elucidate the phenotypic correlates, constructs, assessments, and etiologic mechanisms in psychopathy, typically do not include direct assessment of psychopathy construct or measurements. However, if facets of psychopathy could be predicted from other measures, such as broadband inventories of normal personality that are often administered in large-scale investigations, data from college epidemiological studies could be brought to bear light on the study of psychopathy. This study is two-fold in the investigation of psychopathy. First, the present study replicated the work of Sellbom and colleagues (2012) three-psychopathy scales derived from the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF). These scales were developed to assess psychopathy as conceptualized in the PPI-R and include Global Psychopathy (Py-T), Impulsive-Antisociality (Py-IA) and Fearless-Dominance (Py-FD). Second, the present study built upon the three-psychopathy scales by investigating psychopathy’s construct in relation to Cooke and Michie’s (2001) three-facto model. A sample of 151 participants from a Midwestern university were administrated the PPI-R and MMPI-2-RF. The MMPI-2-RF three scales and construct of psychopathy were evaluated using bivariate correlations. Results support previous studies, regarding the Py-T, Py-IA, and Py-FD scales and the three-factor model of psychopathy.

The aim of this study was to examine the association between personality and risk taking in a sample of 461 older adults from the Charlotte County Healthy Aging Study (CCHAS). The personality factors of openness to experience, extraversion, neuroticism, agreeableness and conscientiousness were measured with the NEO Five Factor Inventory. Risk-taking was measured with an 8-item questionnaire and a single-item question that assessed subjects' participation in sensation seeking behaviors. Spearman correlation coefficients, hierarchical linear regression and hierarchical logistic regression were used to assess the association. As consistent with past research, high scores on openness to experience (beta = 0.16, P<.0001) and low scores on neuroticism (β = -0.14, P<.01) and agreeableness (β = -0.16, P<.01) were associated with the total score of the 8-item risk taking questionnaire. The single-item risk question was also associated with openness [OR = 1.09; 95% CI: 1.05-1.13], neuroticism [OR = 0.94; 95% CI: 0.90-0.97] and agreeableness [OR = 0.95; 95% CI: 0.92-0.99]. After stratifying by gender, only openness was still significantly associated with risk-taking. Interaction terms including gender and personality factors were added to the models to test if gender was an effect modifier. Although personality differences existed between men and women, none of the interaction terms were statistically significant.

The predominant model of general personality structure is arguably the Five Factor Model (FFM), consisting of the five broad domains of neuroticism, extraversion, openness, agreeableness, and conscientiousness. The FFM of personality disorder (FFMPD) has proposed maladaptive variants at both poles of the FFM. The purpose of the current study was to identify a subset of FFMPD scales, utilizing factor analysis, that illustrate, and provide a potential measure of, the bipolarity present in the FFMPD. All of the FFMPD scales were administered to 443 community participants recruited from Amazon Mechanical Turk. Bipolarity was evident in a series of factor analyses of subsets of FFMPD scales, with the exception of openness. The current study also demonstrated that the presence of bipolarity is impaired by a number of concerns, including the presence of non-diametric scales, bloated specific factors, general factor of personality disorder, and occupation of interstitial space.

In this dissertation, I study the performance of asset-pricing models in explaining the cross section of expected stock returns. The finance literature has uncovered several potential failings of the Capital Asset Pricing Model (CAPM). I investigate the ability of additional risk factors, which are not considered by the CAPM, to explain these problems. In particular, I examine intertemporal risk and long-run risk in the cross section of returns. In addition, I develop a firm-level test to refine and reassess the cross-sectional evidence against the CAPM. In the first chapter, I test the cross-sectional implications of the Intertemporal CAPM (ICAPM) of Merton (1973) and Campbell (1993, 1996) using a new firm-level approach. I find that the ICAPM performs well in explaining returns. Consistent with theoretical predictions, investors require a large positive premium for taking on market risk and zero-beta assets earn the risk-free rate. Moreover, investors accept lower returns on assets that hedge against adverse shifts in the investment opportunity set. The ICAPM explains more cross-sectional variation in average returns than either the CAPM or Fama-French (1993) model. I also investigate whether the SMB and HML factors of the Fama-French model proxy for intertemporal risk and find little evidence in favor of this conjecture. In the second chapter, we propose an intertemporal asset-pricing model that simultaneously resolves the puzzling negative relations between expected stock return and analysts' forecast dispersion, idiosyncratic volatility, and credit risk. All three effects emerge in a long-run risk economy accommodating a formal cross section of firms characterized by mean-reverting expected dividend growth. Higher cash flow duration firms exhibit higher exposure to economic growth shocks while they are less sensitive to firm-specific news. Such firms command higher risk premiums but exhibit lower measures of idiosyncratic risk. Empirical evidence broadly supports our model's predictions, as higher dispersion, idiosyncratic volatility, and credit risk firms display lower exposure to long-run risk along with higher firm-specific risk. Lastly, in the third chapter, we examine asset-pricing anomalies at the firm level. Portfolio-level tests linking CAPM alphas to a large number of firm characteristics suggest that the CAPM fails across multiple dimensions. There are, however, concerns that underlying firm-level associations may be distorted at the portfolio level. In this paper we use a hierarchical Bayes approach to model conditional firm-level alphas as a function of firm characteristics. Our empirical results indicate that much of the portfolio-based evidence against the CAPM is overstated. Anomalies are primarily confined to small stocks, few characteristics are robustly associated with CAPM alphas out of sample, and most firm characteristics do not contain unique information about abnormal returns.