Caracterização dos retornos e das volatilidades de ativos financeiros do mercado Brasileiro : uma abordagem baseada na volatilidade realizada

The goal of this paper is twofold. First, using five of the most actively traded stocks in the Brazilian financial market, this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when the information contained in high frequency data is used to construct the realized volatilies measures, we attain the normality of the standardized returns, giving promise of improvements in Value at Risk statistics. We also describe the distributions of volatilities of the Brazilian stocks, showing that the distributions of volatilities are nearly lognormal. Second, we estimate a simple linear model to the log of realized volatilities that differs from the ones in other studies. The main difference is that we do not find evidence of long memory. The estimated model is compared with commonly used alternatives in an out-of-sample experiment.

Reality check for volatility models

Asset allocation decisions and value at risk calculations rely strongly on volatility estimates. Volatility measures such as rolling window, EWMA, GARCH and stochastic volatility are used in practice. GARCH and EWMA type models that incorporate the dynamic structure of volatility and are capable of forecasting future behavior of risk should perform better than constant, rolling window volatility models. For the same asset the model that is the ‘best’ according to some criterion can change from period to period. We use the reality check test∗ to verify if one model out-performs others over a class of re-sampled time-series data. The test is based on re-sampling the data using stationary bootstrapping. For each re-sample we check the ‘best’ model according to two criteria and analyze the distribution of the performance statistics. We compare constant volatility, EWMA and GARCH models using a quadratic utility function and a risk management measurement as comparison criteria. No model consistently out-performs the benchmark.

Dinamicity and unpredictability of emerging markets: an implementation of Goetzamnn and Jorion (1999)

This research is to be considered as an implementation of Goetzmann and Jorion (1999). In order to provide a more realistic scenario, we have implemented a Garch (1,1) approach for the residuals of returns and a multifactor model thus to better replicate the systematic risk of a market. The new simulations reveal some new aspects of emerging markets’ expected returns: the unpredictability of the emerging markets’ returns with the global factor does not depend on the year of emergence and that the unsystematic risk explains the returns of emerging markets for a much larger period of time. The results also reveal the high impact of Exchange rate, Commodities index and of the Global factor in emerging markets’ expected return.

Impacto de eventos climáticos extremos sobre o preço de ações de indústrias de interesse nacional

A ocorrência de eventos climáticos extremos, tais como aumento da temperatura, furacões, enchentes e secas, tem sido cada vez mais frequente ao redor do mundo. A literatura de finanças tem documentado esforços dirigidos à avaliação de impactos econômicos oriundos das variações climáticas, com consequências significantes na economia mundial. Entretanto, especialmente no Brasil, um dos principais mercados emergentes, pouco tem sido pesquisado, sobretudo com vistas à avaliação dos impactos de eventos climáticos no nível das empresas. Sendo assim, esta tese analisa, de forma inédita, o impacto de eventos climáticos sobre o valor de empresas pertencentes a duas indústrias de elevado interesse nacional, sob a forma de dois ensaios. Em primeiro lugar analisa-se o impacto de chuvas extremas sobre o preço de ações do setor de alimentos brasileiro. Para tanto, é conduzida a pesquisa empregando dados diários do preço de ações de seis empresas dessa indústria. A partir da localização da principal região de atuação dessas empresas, são considerados os respectivos dados diários referentes às chuvas extremas. Com o emprego da metodologia híbrida ARMA-GARCH-GPD, constatou-se que, nas empresas avaliadas, as chuvas extremas impactaram significantemente em mais da metade dos 198 dias de chuvas extremas ocorridos entre 28/02/2005 e 30/12/2014, acarretando perdas médias diárias ao redor de 1,97% no dia posterior a chuva extrema. Em termos de valor de mercado, isso representa perda média total ao redor de US$682,15 mi em um único dia. Em segundo lugar avalia-se o impacto de variáveis climáticas e localização sobre o valor das empresas do setor de energia do Brasil, a partir de dados referentes às empresas do setor elétrico brasileiro, bem como precipitação pluviométrica, temperatura e localização geográfica das empresas. A partir da análise de dados em painel estático e painel espacial, os resultados sugerem que temperatura e precipitação pluviométrica têm efeito significante sobre o valor dessas empresas. O presente estudo pode vir a contribuir no processo de estruturação e criação de um mercado de derivativos climáticos no Brasil.

An evolving possibilistic fuzzy modeling approach for value-at-risk estimation

Market risk exposure plays a key role for nancial institutions risk management. A possible measure for this exposure is to evaluate losses likely to incurwhen the price of the portfolio's assets declines using Value-at-Risk (VaR) estimates, one of the most prominent measure of nancial downside market risk. This paper suggests an evolving possibilistic fuzzy modeling approach for VaR estimation. The approach is based on an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling, which employs memberships and typicalities to update clusters and creates new clusters based on a statistical control distance-based criteria. ePFM also uses an utility measure to evaluate the quality of the current cluster structure. Computational experiments consider data of the main global equity market indexes of United States, London, Germany, Spain and Brazil from January 2000 to December 2012 for VaR estimation using ePFM, traditional VaR benchmarks such as Historical Simulation, GARCH, EWMA, and Extreme Value Theory and state of the art evolving approaches. The results show that ePFM is a potential candidate for VaR modeling, with better performance than alternative approaches.

Impacto de chuvas extremas em ações da indústria de alimentos

A análise dos efeitos decorrentes de chuvas extremas sobre o retorno das ações de algumas das principais empresas do setor alimentício brasileiro é de fundamental relevância, por ser um setor com capacidade expressiva para impulsionar o crescimento econômico do Brasil. Diante disso, neste trabalho é analisado o impacto sobre o retorno financeiro diário de seus empresas do setor alimentício brasileiro decorrente de precipitação pluviométrica extrema. Para tal, os retornos diários foram ajustados pelo modelo AR(1)-GARCH(1,1), onde suas inovações foram modeladas pela Teoria dos Valores Extremos. A partir daí o valor em risco (VaR) foi estimado para analisar o impacto das chuvas extremas sobre as ações em estudo. Os resultados encontrados indicam que, em cinco das seis empresas analisadas, pelo menos mais da metade dos impactos pluviométricos extremos apresentaram-se significantes.

Options listing and the volatility of the underlying asset: a study on the derivative market function

Os mercados de derivativos são vistos com muita desconfiança por inúmeras pessoas. O trabalho analisa o efeito da introdução de opções sobre ações no mercado brasileiro buscando identificar uma outra justificativa para a existência destes mercados: a alteração no nível de risco dos ativos objetos destas opções. A evidência empírica encontrada neste mercado está de acordo com os resultados obtidos em outros mercados - a introdução de opções é benéfica para o investidor posto que reduz a volatilidade do ativo objeto. Existe também uma tênue indicação de que a volatilidade se torna mais estocástica com a introdução das opções.

Análise comparativa e teste empírico da validade dos modelos CAPM tradicional e condicional: o caso das ações da Petrobrás

ALVES, Janaína da Silva. Análise comparativa e teste empírico da validade dos modelos CAPM tradicional e condicional: o caso das ações da Petrobrás. Revista Ciências Administrativas, Fotaleza, v. 13, n. 1, p.147-157, ago. 2007.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Eficiência e razão de hedge: uma análise dos mercados futuro brasileiros de boi, café, etanol, milho e soja

This research aims to investigate the Hedge Efficiency and Optimal Hedge Ratio for the future market of cattle, coffee, ethanol, corn and soybean. This paper uses the Optimal Hedge Ratio and Hedge Effectiveness through multivariate GARCH models with error correction, attempting to the possible phenomenon of Optimal Hedge Ratio differential during the crop and intercrop period. The Optimal Hedge Ratio must be bigger in the intercrop period due to the uncertainty related to a possible supply shock (LAZZARINI, 2010). Among the future contracts studied in this research, the coffee, ethanol and soybean contracts were not object of this phenomenon investigation, yet. Furthermore, the corn and ethanol contracts were not object of researches which deal with Dynamic Hedging Strategy. This paper distinguishes itself for including the GARCH model with error correction, which it was never considered when the possible Optimal Hedge Ratio differential during the crop and intercrop period were investigated. The commodities quotation were used as future price in the market future of BM&FBOVESPA and as spot market, the CEPEA index, in the period from May 2010 to June 2013 to cattle, coffee, ethanol and corn, and to August 2012 to soybean, with daily frequency. Similar results were achieved for all the commodities. There is a long term relationship among the spot market and future market, bicausality and the spot market and future market of cattle, coffee, ethanol and corn, and unicausality of the future price of soybean on spot price. The Optimal Hedge Ratio was estimated from three different strategies: linear regression by MQO, BEKK-GARCH diagonal model, and BEKK-GARCH diagonal with intercrop dummy. The MQO regression model, pointed out the Hedge inefficiency, taking into consideration that the Optimal Hedge presented was too low. The second model represents the strategy of dynamic hedge, which collected time variations in the Optimal Hedge. The last Hedge strategy did not detect Optimal Hedge Ratio differential between the crop and intercrop period, therefore, unlikely what they expected, the investor do not need increase his/her investment in the future market during the intercrop

Modelos estocásticos com heterocedasticidade: Uma abordagem Bayesiana para os retornos do Ibovespa

Current research compares the Bayesian estimates obtained for the parameters of processes of ARCH family with normal and Student's t distributions for the conditional distribution of the return series. A non-informative prior distribution was adopted and a reparameterization of models under analysis was taken into account to map parameters' space into real space. The procedure adopts a normal prior distribution for the transformed parameters. The posterior summaries were obtained by Monte Carlo Markov Chain (MCMC) simulation methods. The methodology was evaluated by a series of Bovespa Index returns and the predictive ordinate criterion was employed to select the best adjustment model to the data. Results show that, as a rule, the proposed Bayesian approach provides satisfactory estimates and that the GARCH process with Student's t distribution adjusted better to the data.

Previsão de séries temporais financeiras por meio de redes neurais dinâmicas e processos de transformação de dados: uma abordagem empírico-comparativa

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Finanza Imprenditoriale: Teoria del Portafoglio e Costo del Capitale

This thesis focuses on the limits that may prevent an entrepreneur from maximizing her value, and the benefits of diversification in reducing her cost of capital. After reviewing all relevant literature dealing with the differences between traditional corporate finance and entrepreneurial finance, we focus on the biases occurring when traditional finance techniques are applied to the entrepreneurial context. In particular, using the portfolio theory framework, we determine the degree of under-diversification of entrepreneurs. Borrowing the methodology developed by Kerins et al. (2004), we test a model for the cost of capital according to the firms' industry and the entrepreneur's wealth commitment to the firm. This model takes three market inputs (standard deviation of market returns, expected return of the market, and risk-free rate), and two firm-specific inputs (standard deviation of the firm returns and correlation between firm and market returns) as parameters, and returns an appropriate cost of capital as an output. We determine the expected market return and the risk-free rate according to the huge literature on the market risk premium. As for the market return volatility, it is estimated considering a GARCH specification for the market index returns. Furthermore, we assume that the firm-specific inputs can be obtained considering new-listed firms similar in risk to the firm we are evaluating. After we form a database including all the data needed for our analysis, we perform an empirical investigation to understand how much of the firm's total risk depends on market risk, and which explanatory variables can explain it. Our results show that cost of capital declines as the level of entrepreneur's commitment decreases. Therefore, maximizing the value for the entrepreneur depends on the fraction of entrepreneur's wealth invested in the firm and the fraction she sells to outside investors. These results are interesting both for entrepreneurs and policy makers: the former can benefit from an unbiased model for their valuation; the latter can obtain some guidelines to overcome the recent financial market crisis.