Essays on regulation and risk

In this thesis, we investigate some aspects of the interplay between economic regulation and the risk of the regulated firm. In the first chapter, the main goal is to understand the implications a mainstream regulatory model (Laffont and Tirole, 1993) have on the systematic risk of the firm. We generalize the model in order to incorporate aggregate risk, and find that the optimal regulatory contract must be severely constrained in order to reproduce real-world systematic risk levels. We also consider the optimal profit-sharing mechanism, with an endogenous sharing rate, to explore the relationship between contract power and beta. We find results compatible with the available evidence that high-powered regimes impose more risk to the firm. In the second chapter, a joint work with Daniel Lima from the University of California, San Diego (UCSD), we start from the observation that regulated firms are subject to some regulatory practices that potentially affect the symmetry of the distribution of their future profits. If these practices are anticipated by investors in the stock market, the pattern of asymmetry in the empirical distribution of stock returns may differ among regulated and non-regulated companies. We review some recently proposed asymmetry measures that are robust to the empirical regularities of return data and use them to investigate whether there are meaningful differences in the distribution of asymmetry between these two groups of companies. In the third and last chapter, three different approaches to the capital asset pricing model of Kraus and Litzenberger (1976) are tested with recent Brazilian data and estimated using the generalized method of moments (GMM) as a unifying procedure. We find that ex-post stock returns generally exhibit statistically significant coskewness with the market portfolio, and hence are sensitive to squared market returns. However, while the theoretical ground for the preference for skewness is well established and fairly intuitive, we did not find supporting evidence that investors require a premium for supporting this risk factor in Brazil.

Estilo e agrupamento de fundos : um estudo aplicado aos fundos multimercados brasileiros

Uma classificação adequada de fundos é importante para que o investidor possa organizar a informação disponível de tal modo que possa tomar decisões de aplicação de seus recursos de forma eficiente. No Brasil, existem dois sistemas de classificação amplamente utilizados, o CVM e o ANBIMA, porem ambos possuem categorias com fronteiras subjetivas, isto é, possuem um elevado grau de arbitrariedade na definição de suas categorias, este fato prejudica uma alocação eficiente por parte do investidor. Fundos multimercado são fundos que possuem política de investimento que envolvem vários fatores de risco sem concentração em nenhum fator especial, diferentemente das outras classes de fundos do mercado brasileiro. Sob este aspecto, uma categorização adequada dos fundos multimercados traria inúmeros benefícios tais como a redução do custo de análise, a maior facilidade no processo de tomada de decisão de investimento, uma diversificação mais eficiente, clareza na comparação de desempenho e o melhor entendimento dos riscos incorridos dentre outros benefícios. O presente trabalho tem como objetivo, utilizando-se da já consagrada técnica de análise de estilo de Sharpe (1992), decompor a exposição de cada fundo em seus principais fatores de risco, após isto, utilizar-se da análise de cluster para agrupar os fundos de forma coerente a suas exposições, tentando assim fazer um classificação mais eficiente; isto seria um contraponto a classificação mais utilizada pelo mercado brasileiro, a classificação Anbima, que se baseia no regulamento do fundo, isto é, no que o fundo “pode” investir, e não no que o fundo efetivamente investe.

Nonparametric tail risk, macroeconomics and stock returns: predictability and risk premia

This paper proposes a new novel to calculate tail risks incorporating risk-neutral information without dependence on options data. Proceeding via a non parametric approach we derive a stochastic discount factor that correctly price a chosen panel of stocks returns. With the assumption that states probabilities are homogeneous we back out the risk neutral distribution and calculate five primitive tail risk measures, all extracted from this risk neutral probability. The final measure is than set as the first principal component of the preliminary measures. Using six Fama-French size and book to market portfolios to calculate our tail risk, we find that it has significant predictive power when forecasting market returns one month ahead, aggregate U.S. consumption and GDP one quarter ahead and also macroeconomic activity indexes. Conditional Fama-Macbeth two-pass cross-sectional regressions reveal that our factor present a positive risk premium when controlling for traditional factors.