Analysis of contagion from the constant conditional correlation model with Markov regime switching

Over the last decades, the analysis of the transmissions of international nancial events has become the subject of many academic studies focused on multivariate volatility models volatility. The goal of this study is to evaluate the nancial contagion between stock market returns. The econometric approach employed was originally presented by Pelletier (2006), named Regime Switching Dynamic Correlation (RSDC). This methodology involves the combination of Constant Conditional Correlation Model (CCC) proposed by Bollerslev (1990) with Markov Regime Switching Model suggested by Hamilton and Susmel (1994). A modi cation was made in the original RSDC model, the introduction of the GJR-GARCH model formulated in Glosten, Jagannathan e Runkle (1993), on the equation of the conditional univariate variances to allow asymmetric e ects in volatility be captured. The database was built with the series of daily closing stock market indices in the United States (SP500), United Kingdom (FTSE100), Brazil (IBOVESPA) and South Korea (KOSPI) for the period from 02/01/2003 to 09/20/2012. Throughout the work the methodology was compared with others most widespread in the literature, and the model RSDC with two regimes was de ned as the most appropriate for the selected sample. The set of results provide evidence for the existence of nancial contagion between markets of the four countries considering the de nition of nancial contagion from the World Bank called very restrictive. Such a conclusion should be evaluated carefully considering the wide diversity of de nitions of contagion in the literature.

A (semi-)parametric functional coefficient autoregressive conditional duration model

In this paper, we propose a class of ACD-type models that accommodates overdispersion, intermittent dynamics, multiple regimes, and sign and size asymmetries in financial durations. In particular, our functional coefficient autoregressive conditional duration (FC-ACD) model relies on a smooth-transition autoregressive specification. The motivation lies on the fact that the latter yields a universal approximation if one lets the number of regimes grows without bound. After establishing that the sufficient conditions for strict stationarity do not exclude explosive regimes, we address model identifiability as well as the existence, consistency, and asymptotic normality of the quasi-maximum likelihood (QML) estimator for the FC-ACD model with a fixed number of regimes. In addition, we also discuss how to consistently estimate using a sieve approach a semiparametric variant of the FC-ACD model that takes the number of regimes to infinity. An empirical illustration indicates that our functional coefficient model is flexible enough to model IBM price durations.

Speculative bubbles and contagion: analysis of volatility’s clusters during the DotCom bubble based on the dynamic conditional correlation model

Revendo a definição e determinação de bolhas especulativas no contexto de contágio, este estudo analisa a bolha do DotCom nos mercados acionistas americanos e europeus usando o modelo de correlação condicional dinâmica (DCC) proposto por Engle e Sheppard (2001) como uma explicação econométrica e, por outro lado, as finanças comportamentais como uma explicação psicológica. Contágio é definido, neste contexto, como a quebra estatística nos DCC’s estimados, medidos através das alterações das suas médias e medianas. Surpreendentemente, o contágio é menor durante bolhas de preços, sendo que o resultado principal indica a presença de contágio entre os diferentes índices dos dois continentes e demonstra a presença de alterações estruturais durante a crise financeira.

Speculative bubbles and contagion: analysis of volatility's clusters during the DotCom bubble based on the dynamic conditional correlation model

Reviewing the de nition and measurement of speculative bubbles in context of contagion, this paper analyses the DotCom bubble in American and European equity markets using the dynamic conditional correlation (DCC) model proposed by (Engle and Sheppard 2001) as on one hand as an econometrics explanation and on the other hand the behavioral nance as an psychological explanation. Contagion is de ned in this context as the statistical break in the computed DCCs as measured by the shifts in their means and medians. Even it is astonishing, that the contagion is lower during price bubbles, the main nding indicates the presence of contagion in the di¤erent indices among those two continents and proves the presence of structural changes during nancial crisis

Estimação conjunta das elasticidades: preço da escolha da marca e da quantidade comprada com dados escaneados

O objetivo geral desta tese é estimar a elasticidade-preço da demanda de forma conjunta, decomposta em elasticidade-preço da escolha da marca e da quantidade comprada, e discutir as implicações desta decomposição para uma categoria específica de produto. Para isto foram usados dados escaneados de uma amostra de domicílios no contexto varejista brasileiro. Oito hipóteses foram testadas por meio de dois modelos. O primeiro refere-se à decisão de escolha da marca, em que foi empregado o modelo logit condicional baseado na maximização da utilidade do domicílio. O segundo envolveu equações de demanda, obtidas pelo modelo clássico de regressão linear. Ambos foram especificados de forma que se pudesse testar a dependência das duas decisões de compra. No que diz respeito à validação, o modelo de escolha da marca demonstrou uma satisfatória capacidade de previsão, comparativamente aos modelos analisados na literatura. Implicações gerenciais incluem específicas decisões e ações de preço para as marcas, já que a natureza da decomposição das elasticidades-preço varia entre marcas.

Dynamic hedging in Markov regimes

This dissertation proposes a bivariate markov switching dynamic conditional correlation model for estimating the optimal hedge ratio between spot and futures contracts. It considers the cointegration between series and allows to capture the leverage efect in return equation. The model is applied using daily data of future and spot prices of Bovespa Index and R$/US$ exchange rate. The results in terms of variance reduction and utility show that the bivariate markov switching model outperforms the strategies based ordinary least squares and error correction models.

Does humor work in advertising of pharmaceutical products?

This thesis aims to evaluate whether humorous television commercials (TVCs) work for non-prescription drugs, known as “over-the-counter” (OTC). The construct humor in advertising is controversial since it involves complex and broad typology, and depends on the audience characteristics. Several studies within different product categories indicated that some consumer goods are better suited for humorous TVCs, while others, such as OTC drugs, may not take advantage from it. Paradoxically, drug announcers spend billions of dollars worldwide in humorous OTC ads. An experiment with real consumers was designed as between-and-within-subjects, to test three hypotheses. Sixty women were exposed to pairs of humorous and non-humorous TVCs, for each of the three drug categories (analgesics, vitamins, and laxatives). We used fictional brand names and real ads, and measured four dependant variables: attitude toward the advertising (AAD), attitude toward the brand (ABR), purchase intention (PI), and brand choice (BC), after subjects being exposed to manipulations of two independent variables: humorous vs. non-humorous TV commercials, for the drug categories. Conditional logit model confirmed that humor does not help to persuade respondents, whose choices, attitudes, and purchase intention were less favorable with humorous TVCs, in comparison to non-humorous executions. Future research is presented regarding marketing for pharmaceutical products.

Análise de contágio a partir do modelo de correlação condicional constante com mudança de regime Markoviana

Nas últimas décadas, a análise dos padrões de propagação internacional de eventos financeiros se tornou o tema de grande parte dos estudos acadêmicos focados em modelos de volatilidade multivariados. Diante deste contexto, objetivo central do presente estudo é avaliar o fenômeno de contágio financeiro entre retornos de índices de Bolsas de Valores de diferentes países a partir de uma abordagem econométrica, apresentada originalmente em Pelletier (2006), sobre a denominação de Regime Switching Dynamic Correlation (RSDC). Tal metodologia envolve a combinação do Modelo de Correlação Condicional Constante (CCC) proposto por Bollerslev (1990) com o Modelo de Mudança de Regime de Markov sugerido por Hamilton e Susmel (1994). Foi feita uma modificação no modelo original RSDC, a introdução do modelo GJR-GARCH formulado em Glosten, Jagannathan e Runkle (1993), na equação das variâncias condicionais individuais das séries para permitir capturar os efeitos assimétricos na volatilidade. A base de dados foi construída com as séries diárias de fechamento dos índices das Bolsas de Valores dos Estados Unidos (SP500), Reino Unido (FTSE100), Brasil (IBOVESPA) e Coréia do Sul (KOSPI) para o período de 02/01/2003 até 20/09/2012. Ao longo do trabalho a metodologia utilizada foi confrontada com outras mais difundidos na literatura, e o modelo RSDC com dois regimes foi definido como o mais apropriado para a amostra selecionada. O conjunto de resultados encontrados fornecem evidências a favor da existência de contágio financeiro entre os mercados dos quatro países considerando a definição de contágio financeiro do Banco Mundial denominada de “muito restritiva”. Tal conclusão deve ser avaliada com cautela considerando a extensa diversidade de definições de contágio existentes na literatura.