Simulation-based smoothing and filtering in factor stochastic volatility models : two econometric applications

In this article we use factor models to describe a certain class of covariance structure for financiaI time series models. More specifical1y, we concentrate on situations where the factor variances are modeled by a multivariate stochastic volatility structure. We build on previous work by allowing the factor loadings, in the factor mo deI structure, to have a time-varying structure and to capture changes in asset weights over time motivated by applications with multi pIe time series of daily exchange rates. We explore and discuss potential extensions to the models exposed here in the prediction area. This discussion leads to open issues on real time implementation and natural model comparisons.

Bayesian analysis of extreme events with threshold estimation

The aim of this paper is to analyze extremal events using Generalized Pareto Distributions (GPD), considering explicitly the uncertainty about the threshold. Current practice empirically determines this quantity and proceeds by estimating the GPD parameters based on data beyond it, discarding all the information available be10w the threshold. We introduce a mixture model that combines a parametric form for the center and a GPD for the tail of the distributions and uses all observations for inference about the unknown parameters from both distributions, the threshold inc1uded. Prior distribution for the parameters are indirectly obtained through experts quantiles elicitation. Posterior inference is available through Markov Chain Monte Carlo (MCMC) methods. Simulations are carried out in order to analyze the performance of our proposed mode1 under a wide range of scenarios. Those scenarios approximate realistic situations found in the literature. We also apply the proposed model to a real dataset, Nasdaq 100, an index of the financiai market that presents many extreme events. Important issues such as predictive analysis and model selection are considered along with possible modeling extensions.

Trust-based trade

There is substantially more trade within national borders than across borders. An important explanation for this fact is the weak enforcement of international contracts. We develop a model in which agents build reputations to overcome this institutional failure. The model describes the interplay between institutional quality, reputations and the dynamics of international trade. It also rationalizes several empirical regularities. We find that history matters for trade volumes, but that its effects vary with the institutional setting of the country. The same is true for the efticacy of trade liberalization programs. Moreover, while stricter enforcement of contracts enhances trade in the short run, it makes it harder for individual traders to develop good reputations. We show that this indirect negative effect may produce an "institutional trap": for sufliciently low initial levels of contract enforcement, a small tightening in enforcement reduces future trade fiows. We find also that search frictions aggravate the problems created by weak enforceability of contracts, even if they impose no direct cost on agents. The model allows extensions in several directions. We outline two of them, indicating how one could study transnational networks and the effects of firm heterogeneity within our structure.

Line extension influence on brand image: a study on the alcoholic beverage market in Brazil

The present study has the objective of understanding the influence of line extensions on the image of vodka brands. The research was performed by organizing various focus groups with vodka consumers in São Paulo. These focus groups allowed exploring and analyzing how the last line extensions of vodka brands have modified the image the consumers had of the brand. Three hypotheses were distinguished as an outcome of the research: (1) The influence of a line extension on brand image depends heavily on the initial image the consumers have of the brand. For a vodka brand with an average or bad image, launching a line extension with a perceived average or bad quality does not modify the brand image. On the contrary, for a vodka brand with a positive initial brand image, launching a line extension with perceived high quality led to a positive change in the brand image. (2) For vodka brands, a vertical line extension recognized as having high authenticity provokes a transfer of attributes from the extended product to the brand. (3) Among Keller’s (1993) dimensions of brand image, non-product related attributes and especially packaging are the one that are the most influenced by line extensions of vodka brands.

Microfounded forecasting

Our focus is on information in expectation surveys that can now be built on thousands (or millions) of respondents on an almost continuous-time basis (big data) and in continuous macroeconomic surveys with a limited number of respondents. We show that, under standard microeconomic and econometric techniques, survey forecasts are an affine function of the conditional expectation of the target variable. This is true whether or not the survey respondent knows the data-generating process (DGP) of the target variable or the econometrician knows the respondents individual loss function. If the econometrician has a mean-squared-error risk function, we show that asymptotically efficient forecasts of the target variable can be built using Hansens (Econometrica, 1982) generalized method of moments in a panel-data context, when N and T diverge or when T diverges with N xed. Sequential asymptotic results are obtained using Phillips and Moon s (Econometrica, 1999) framework. Possible extensions are also discussed.

Assessment of value creation in private equity: the acquisition of Burger King by 3G Capital

This thesis elaborates the creation of value in private equity and in particular analyzes value creation in 3G Capital’s acquisition of Burger King. In this sense, a specific model is applied that composes value creation into several drivers, in order to answer the question of how value creation can be addressed in private equity investments. Although previous research by Achleitner et al. (2010) introduced a specific model that addresses value creation in private equity, the respective model was neither applied to an individual company, nor linked to indirect drivers that explain the dynamics and rationales for the creation of value. In turn this paper applies the quantitative model to an ongoing private equity investment and thereby provides different extensions to turn the model into a better forecasting model for ongoing investments, instead of only analyzing a deal that has already been divested from an ex post perspective. The chosen research approach is a case study about the Burger King buyout that first includes an extensive review about the current status of academic literature, second a quantitative calculation and qualitative interpretation of different direct value drivers, third a qualitative breakdown of indirect drivers, and lastly a recapitulating discussion about value creation and value drivers. Presenting a very successful private equity investment and elaborately demonstrating the dynamics and mechanisms that drive value creation in this case, provides important implications for other private equity firms as well as public firms in order to develop their proprietary approach towards value creation.