This thesis examines the performance of different models of conditional betas and higher comoments in the context of the cross-section of expected stock returns, both in-sample and out-of-sample. I first examine the performance of different conditional market beta models by using monthly returns of the Fama-French 25 portfolios formed by the quintiles of size and book-to-market ratio in Chapter 3. This is a cross-sectional test of the conditional CAPM. The models examined include simple OLS regressions, the macroeconomic variables model, the state-space model, the multivariate GARCH model and the realized beta model. The results show that the state-space model performs best in-sample with significant betas and insignificant intercepts. For the out-of-sample performance, however, none of the models examined can explain returns of the 25 portfolios. Next, I examine the recently proposed realized beta model, which is based on the realized volatility literature, by using individual stocks listed in the US market in Chapter 4. I extend the realized market beta model to betas of multi-factor asset pricing models. Models tested are the CAPM, the Fama-French three-factor model and a four-factor model including the three Fama-French factors and a momentum factor. Realized betas of different models are used in the cross-section regressions along with firm-level variables such as size, book-to-market ratio and past returns. The in-sample results show that market beta is significant and additional betas of multi-factor models can reduce although not eliminate the effects of firm-level variables. The out-of-sample results show that no betas are significant. The results are robust across different markets such as NYSE, AMEX and NASDAQ. In Chapter 5, I test if realized coskewness and cokurtosis can help explain the cross-section of stock returns. I add coskewness and cokurtosis to the factor pricing models tested in Chapter 4. The results show that the coefficients of coskewness and cokurtosis have the correct sign as predicted by the higher-moment CAPM theory but only cokurtosis is significant. Cokurtosis is significant not only in-sample but also out-of-sample, suggesting cokurtosis is an important risk. However, the effects of firm-level variables remain significant after higher moments are included, indicating a rejection of higher-moment asset pricing models. The results are also robust across different markets such as NYSE, AMEX and NASDAQ. The overall results of this thesis indicate a rejection of the conditional asset pricing models. Models of systematic risks, i.e. betas and higher comoments, cannot explain the cross-section of expected stock returns.

This thesis analyzes the use of realized moments in asset pricing. The analysis is done using dataset containing log-returns for 29 of the most traded stocks and covering 10 years of data. The dataset is split into training set covering 7 years and test set covering 3 years of data. For each of the stocks a separate time series model is estimated. In evaluation of the quality of the models, metrics such as RMSE, MAD, accuracy in forecasting the sign of future returns, and returns achievable by executing trades based on the recommendations from the model are used. Even though the inclusion of realized moments does not provide significant improvements in terms of RMSE, it is found that realized skewness and kurtosis significantly contribute to explaining the returns of individual stocks as they lead to consistent improvements in identifying future positive, as well as negative, returns. Moreover, the recommendations from the models using realized moments can help us achieve significantly higher returns from trading stocks. Inclusion of the interaction terms for variance and returns, skewness and returns, and kurtosis and variance, provides additional improvement of forecasting accuracy, as well as improvements in returns achievable by executing transactions based on recommendations from the model....

O Objetivo deste trabalho é usar uma ferramenta matemática conhecida como expansão de Edgeworth em conjunto com a moderna teoria de análise de derivativos financeiros que utilizam o método de precificação neutra ao risco. Tal expansão permite obter uma função densidade de probabilidade com assimetria e curtose arbitrárias a partir de uma densidade normal. Desta forma, podemos usar esta nova distribuição como a state price density do ativo-objeto procurando corrigir o sorriso da volatilidade através da definição de funções de probabilidade com assimetrias positivas ou negativas e curtose maior de que três. Além disso esperamos também chegar a uma nova maneira de realizar o delta hedge de uma carteira de replicação de modo mais eficiente do que a de Black-Scholes. / There is a well-developed framework, the Black?Scholes theory, for the pricing of contracts based on the future prices of certain assets, called options. This theory assumes that the probability distribution of the returns of the underlying asset is a Gaussian distribution. However, it is observed in the market that this hypothesis is 2awed, leading to the introduction of a fudge factor, the so-called volatility smile. Therefore, it would be interesting to explore extensions of the Black?Scholes theory to non-Gaussian distributions. In this paper, we provide an explicit formula for the price of an option when the distributions of the returns of the underlying asset is parametrized by an Edgeworth expansion, which allows for the introduction of higher independent moments of the probability distribution, namely skewness and kurtosis. We test our formula with options in the Brazilian and American markets, showing that the volatility smile can be reduced. We also check whether our approach leads to more e6cient hedging strategies of these instruments.

Stochastic frontier analysis is an exciting method of economic production modeling that is relevant to hospitals, stock markets, manufacturing factories, and services. In this paper, we create a new model using the normal distribution and truncated skew-Laplace distribution, namely the normal-truncated skew-Laplace model. This is a generalized model of the normal-exponential case. Furthermore, we compute the true technical efficiency and estimated technical efficiency of the normal-truncated skewed-Laplace model. Also, we compare the technical efficiencies of normal-truncated skewed-Laplace model and normal-exponential model.

The concept of emerging markets came to surface in early 1980 and constituted of only eight countries from the two continents of South America and Asia. The globalization of financial markets has since raised the importance of emerging capital markets. We take a quantitative approach to investigate the performance of emerging markets compared to developed markets. The aim of the study is to conclude if emerging markets offers investment value and if logic in portfolio theory can be used to improve the chance of creating a relatively better performing investment. Included markets in our study are Brazil, Russia, India, China, Colombia, Indonesia, Vietnam, Egypt, Turkey and South Africa. S&P 500 is our benchmark for developed market performance. Sample period is 2002-01-01 to 2011-12-31 and monthly return data, creating 120 data points on each index.   The weighting schemes used for the portfolios are min variance optimization, geographical location and high and low correlation. All investments are scored on performances in correlation to S&P 500, inflation adjusted growth, currency effect, Sharpe ratio, skewness and kurtosis. Rankings are done on the separate categories, on the individual overall ranking on only countries and one overall ranking on all investments. A brief overview of the overall ranking for all investments suggest that medium performing investments are overrepresented (12/20) and the low and high is underrepresented (3/20 and 5/20). Of note is that the min variance portfolio outperforms its components, the geographical portfolios have a wide range and the high correlated portfolio outperforms the low. The country to portfolio ratio over each grade suggests only a small skew of the results. There is no low scoring portfolio but the other two ratios are close to 50/50, suggesting that on average the portfolios create diversification benefits. Furthermore normality of returns seem to be violated and then the concept of volatility as a risk measure is significantly impaired also currency risk can be of high importance, currency effects ranged from -48% to 28.7%. Assuming non-normality seems more accurate than assuming normality; therefore we need to improve on volatility as a tool to measure risk. So one direction for further research we see a need is in the concept of volatility. The initial reason for this research came from small investors’ seemingly intuitive knowledge that emerging markets are a suitable investment option. We have concluded that they in fact are, therefore we suggest that a qualitative study is conducted to investigate this seemingly natural intuition.

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GARCH models have been widely used in finance to model volatility ever since the introduction of the ARCH model and its extension to the generalized ARCH (GARCH) model. Lately, there has been growing interest in modelling seasonal volatility, most recently with the introduction of the multiplicative seasonal GARCH models. As an application of the multiplicative seasonal GARCH model with real data, call prices from the major stock market index of India are calculated using estimated parameter values. It is shown that a multiplicative seasonal GARCH option pricing model outperforms the Black-Scholes formula and a GARCH(1,1) option pricing formula. A parametric bootstrap procedure is also employed to obtain an interval approximation of the call price. Narrower confidence intervals are obtained using the multiplicative seasonal GARCH model than the intervals provided by the GARCH(1,1) model for data that exhibits multiplicative seasonal GARCH volatility.

GARCH models have been widely used in finance to model volatility ever since the introduction of the ARCH model and its extension to the generalized ARCH (GARCH) model. Lately, there has been growing interest in modelling seasonal volatility, most recently with the introduction of the multiplicative seasonal GARCH models. As an application of the multiplicative seasonal GARCH model with real data, call prices from the major stock market index of India are calculated using estimated parameter values. It is shown that a multiplicative seasonal GARCH option pricing model outperforms the Black-Scholes formula and a GARCH(1,1) option pricing formula. A parametric bootstrap procedure is also employed to obtain an interval approximation of the call price. Narrower confidence intervals are obtained using the multiplicative seasonal GARCH model than the intervals provided by the GARCH(1,1) model for data that exhibits multiplicative seasonal GARCH volatility.

O Objetivo deste trabalho é usar uma ferramenta matemática conhecida como expansão de Edgeworth em conjunto com a moderna teoria de análise de derivativos financeiros que utilizam o método de precificação neutra ao risco. Tal expansão permite obter uma função densidade de probabilidade com assimetria e curtose arbitrárias a partir de uma densidade normal. Desta forma, podemos usar esta nova distribuição como a state price density do ativo-objeto procurando corrigir o sorriso da volatilidade através da definição de funções de probabilidade com assimetrias positivas ou negativas e curtose maior de que três. Além disso esperamos também chegar a uma nova maneira de realizar o delta hedge de uma carteira de replicação de modo mais eficiente do que a de Black-Scholes. / There is a well-developed framework, the Black?Scholes theory, for the pricing of contracts based on the future prices of certain assets, called options. This theory assumes that the probability distribution of the returns of the underlying asset is a Gaussian distribution. However, it is observed in the market that this hypothesis is 2awed, leading to the introduction of a fudge factor, the so-called volatility smile. Therefore, it would be interesting to explore extensions of the Black?Scholes theory to non-Gaussian distributions. In this paper, we provide an explicit formula for the price of an option when the distributions of the returns of the underlying asset is parametrized by an Edgeworth expansion, which allows for the introduction of higher independent moments of the probability distribution, namely skewness and kurtosis. We test our formula with options in the Brazilian and American markets, showing that the volatility smile can be reduced. We also check whether our approach leads to more e6cient hedging strategies of these instruments.

Un marché efficient permet de définir le prix d’un bien, pour des quantités échangées, qui reflète la valeur donnée par les acteurs économiques. Cette valeur est objective dans la théorie classique, ou intrinsèque. Or, la valeur intrinsèque apparaît parfois déconnectée d’une valeur sociale. Il y a donc un problème de définition de la valeur, qui s’exprime notamment sur les marchés financiers. S'agissant de titres financiers, cette valeur est ce qui est mesuré subjectivement au travers d'une échelle de valeur dont la norme est donnée par le leader du marché. Ce leader est reconnu comme le vainqueur d'une lutte, appelée agôn, et se repère par une croissance de son cours (effet agonistique), une baisse des volumes échangés (effet d'allégeance) et un kurtosis élevé (effet mimétique). Nous pouvons alors proposer des aides à la décision pour l’investisseur à partir d’une théorie agonistique de la valeur. / An efficient market gives the right price of any product, with exchanged quantities, reflecting the value given by sellers and buyers. This value is defined as objective, or intrinsic in classical economic approach. This intrinsic value may be disconnected from a social value. It means there is a problem to define the value especially on financial markets. On those financial markets, value is what has to be measured, subjectively on a value scale given by the market leader. This leader is the winner of a fight, called agôn, and its stock price is growing (agonistic effect), the quantities of stocks exchanged are dropping (allegiance effect), with a high kurtosis (mimetic effect). We could propose to any investor some decision-making aid through an agonistic value theory.