Coleção Insper Business and Economics Working Papers
URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/5740
Navegar
36 resultados
Resultados da Pesquisa
Working Paper Criação de Valor em Fusões e Aquisições além de Fronteiras: Uma análise empírica do caso brasileiro(2014) Garcia, Maria Pia de Siqueira; ADRIANA BRUSCATO BORTOLUZZO; Boehe, Dirk Michael; Sheng, Hsia HuaEste artigo tem como objetivo investigar se as aquisições realizadas por empresas brasileiras fora do país nos últimos 15 anos têm gerado valor a seus acionistas. Além disso, é realizada uma análise empírica dos fatores determinantes desse sucesso, com base nas teorias institucional, sociocultural e de aprendizagem organizacional. Os resultados do estudo são condizentes com a teoria e contribuem para a literatura e para a comunidade empresarial ao indicar que de fato as investidas além de fronteiras de companhias do Brasil criam valor – o qual é positivamente impactado quando a distância cultural entre os países da adquirida e adquirente é baixa ou média e quando o ambiente institucional no qual a empresa alvo se encontra é desenvolvido. Já a relação entre as experiências anteriores das brasileiras em fusões ou aquisições internacionais e o desempenho de uma nova aquisição fora do país segue o formato de U-invertido, conciliando resultados divergentes encontrados na literatura. Estes resultados enfatizam a relevância de se considerar a experiência com fusões e aquisições da empresa compradora além das características institucionais dos seus países alvo.Working Paper Competição Bancária: Comparação do Comportamento de Bancos Públicos e Privados e suas Reações à Crise de 2008(2014) Martins, Tiago Sammarco; ADRIANA BRUSCATO BORTOLUZZO; SERGIO GIOVANETTI LAZZARINIWorking Paper Modelos de Risco de Crédito de Clientes: Uma aplicação a Dados Reais(2014) Pereira, Gustavo H. A.; RINALDO ARTESModelos de behavioural scoring são geralmente utilizados para estimar a probabilidade de um cliente de uma instituição financeira que já possui um determinado produto de crédito se tornar inadimplente neste produto em um horizonte de tempo pré-fixado. Porém, frequentemente, um mesmo cliente tem diversos produtos de crédito em uma única instituiçãoo e os modelos de behavioural scoring geralmente tratam cada um deles de forma independente. Para facilitar e tornar mais eficiente o gerenciamento do risco de crédito, é interessante o desenvolvimento de modelos de customer default scoring. Esses modelos buscam estimar a probabilidade de um cliente de uma instituição financeira se tornar inadimplente em pelo menos um produto em um horizonte de tempo pré-fixado. Neste trabalho, são descritas três estratégias que podem ser utilizadas para o desenvolvimento de modelos de customer default scoring. Uma das estratégias é usualmente utilizada por instituições financeiras e as duas outras são propostas neste trabalho. As performances dessas estratégias são comparadas utilizando um banco de dados real fornecido por uma instituição financeira e um estudo de simulação de Monte Carlo.Working Paper Rational Sunspots(2016) Ascari, Guido; Banomolo, Paolo; HEDIBERT FREITAS LOPESThe instability of macroeconomic variables is usually ruled out by rational expectations. We propose a generalization of the rational expectations framework to estimate possible temporary unstable paths. Our approach yields drifting parameters and stochastic volatility. The methodology allows the data to choose between diferent possible alternatives: determinacy, indeterminacy and instability. We apply our methodology to US inflation dynamics in the '70s through the lens of a simple New Keynesian model. When unstable RE paths are allowed, the data unambiguously select them to explain the stagflation period in the '70s. Thus, our methodology suggests that US inflation dynamics in the '70s is better described by unstable rational equilibrium paths.Working Paper Dynamics in two networks based on stocks of the US stock market(2014) Sandoval Junior, LeonidasWe follow the main stocks belonging to the New York Stock Exchange and to Nasdaq from 2003 to 2012, through years of normality and of crisis, and study the dynamics of networks built on two measures expressing relations between those stocks: correlation, which is symmetric and measures how similar two stocks behave, and Transfer Entropy, which is non-symmetric and measures the influence of the time series of one stock onto another in terms of the information that the time series of one stock transmits to the time series of another stock. The two measures are used in the creation of two networks that evolve in time, revealing how the relations between stocks and industrial sectors changed in times of crisis. The two networks are also used in conjunction with a dynamic model of the spreading of volatility in order to detect which are the stocks that are most likely to spread crises, according to the model. This information may be used in the building of policies aiming to reduce the effect of financial crises.Working Paper Sequential bayesian learning for stochastic volatility with variance-gamma jumps in return(2014) Warty, Samir P.; HEDIBERT FREITAS LOPES; Polson, Nicholas G.In this work, we investigate sequential Bayesian estimation for inference of stochastic volatility with variance-gamma jumps in returns (SVVG). We develop an estimation algorithm that adapts the sequential learning auxiliary particle filter proposed by Carvalho, Johannes, Lopes, and Polson (2010) to SVVG. Simulation evidence and empirical estimation results indicate that this approach is able to filter latent variances, identify latent jumps in returns, and provide sequential learning about the static parameters of SVVG. We demonstrate comparative performance of the sequential algorithm and offline Markov Chain Monte Carlo in synthetic and real data applications.Working Paper A Tutorial on the Computation of Bayes Factor(2014) HEDIBERT FREITAS LOPESWorking Paper Shrinkage priors for linear instrumental variable models with many instruments(2014) Hahn, P. Richard; HEDIBERT FREITAS LOPESWorking Paper Parsimony Inducing Priors for Large Scale State-Space Models(2014) HEDIBERT FREITAS LOPES; McCulloch, Robert E.; Tsay, Ruey S.State-space models are commonly used in the engineering, economic, and statistical literatures. They are flexible and encompass many well-known statistical models, including random coefficient autoregressive models and dynamic factor models. Bayesian analysis of state-space models has attracted much interest in recent years. However, for large scale models, prior specification becomes a challenging issue in Bayesian inference. In this paper, we propose a flexible prior for state-space models. The proposed prior is a mixture of four commonly entertained models, yet achieves parsimony in high-dimensional systems. Here “parsimony” is represented by the idea that in a largesystem, some states may not be time-varying. Simulation and simple examples are used throughout to demonstrate the performance of the proposed prior. As an application, we consider the time-varying conditional covariance matrices of daily log returns of 94 components of the S&P 100 index, leading to a state-space model with 94×95/2=4,465 time-varying states. Our model for this large system enables us to use parallel computing.Working Paper Particle Learning for Fat-tailed Distributions(2014) HEDIBERT FREITAS LOPES; Polson, Nicholas G.It is well-known that parameter estimates and forecasts are sensitive to assumptions about the tail behavior of the error distribution. In this paper we develop an approach to sequential inference that also simultaneously estimates the tail of the accompanying error distribution. Our simulation-based approach models errors with a tν-distribution and, as new data arrives, we sequentially compute the marginal posterior distribution of the tail thickness. Our method naturally incorporates fat-tailed error distributions and can be extended to other data features such as stochastic volatility. We show that the sequential Bayes factor provides an optimal test of fat-tails versus normality. We provide an empirical and theoretical analysis of the rate of learning of tail thickness under a default Jeffreys prior. We illustrate our sequential methodology on the British pound/US dollar daily exchange rate data and on data from the 2008-2009 credit crisis using daily S&P500 returns. Our method naturally extends to multivariate and dynamic panel data.