Pré-ipo capital infusion and underwriting in Brazil: a study on conflicts of interests in the universal banking system

Mercados financeiros e finanças corporativas

Tayloring Brazil: a system dynamics model for monetary policy feedback

The thesis introduces a system dynamics Taylor rule model of new Keynesian nature for monetary policy feedback in Brazil. The nonlinear Taylor rule for interest rate changes con-siders gaps and dynamics of GDP growth and inflation. The model closely tracks the 2004 to 2011 business cycle and outlines the endogenous feedback between the real interest rate, GDP growth and inflation. The model identifies a high degree of endogenous feedback for monetary policy and inflation, while GDP growth remains highly exposed to exogenous eco-nomic conditions. The results also show that the majority of the monetary policy moves during the sample period was related to GDP growth, despite higher coefficients of inflation parameters in the Taylor rule. This observation challenges the intuition that inflation target-ing leads to a dominance of monetary policy moves with respect to inflation. Furthermore, the results suggest that backward looking price-setting with respect to GDP growth has been the dominant driver of inflation. Moreover, simulation exercises highlight the effects of the new BCB strategy initiated in August 2011 and also consider recession and inflation avoid-ance versions of the Taylor rule. In methodological terms, the Taylor rule model highlights the advantages of system dynamics with respect to nonlinear policies and to the stock-and-flow approach. In total, the strong historical fit and some counterintuitive observations of the Taylor rule model call for an application of the model to other economies.

Price discovery in dual-class shares across multiple markets

We extend the standard price discovery analysis to estimate the information share of dual-class shares across domestic and foreign markets. By examining both common and preferred shares, we aim to extract information not only about the fundamental value of the rm, but also about the dual-class premium. In particular, our interest lies on the price discovery mechanism regulating the prices of common and preferred shares in the BM&FBovespa as well as the prices of their ADR counterparts in the NYSE and in the Arca platform. However, in the presence of contemporaneous correlation between the innovations, the standard information share measure depends heavily on the ordering we attribute to prices in the system. To remain agnostic about which are the leading share class and market, one could for instance compute some weighted average information share across all possible orderings. This is extremely inconvenient given that we are dealing with 2 share prices in Brazil, 4 share prices in the US, plus the exchange rate (and hence over 5,000 permutations!). We thus develop a novel methodology to carry out price discovery analyses that does not impose any ex-ante assumption about which share class or trading platform conveys more information about shocks in the fundamental price. As such, our procedure yields a single measure of information share, which is invariant to the ordering of the variables in the system. Simulations of a simple market microstructure model show that our information share estimator works pretty well in practice. We then employ transactions data to study price discovery in two dual-class Brazilian stocks and their ADRs. We uncover two interesting ndings. First, the foreign market is at least as informative as the home market. Second, shocks in the dual-class premium entail a permanent e ect in normal times, but transitory in periods of nancial distress. We argue that the latter is consistent with the expropriation of preferred shareholders as a class.

Price discovery using a regime-sensitive cointegration approach

This work proposes a method to examine variations in the cointegration relation between preferred and common stocks in the Brazilian stock market via Markovian regime switches. It aims on contributing for future works in "pairs trading" and, more specifically, to price discovery, given that, conditional on the state, the system is assumed stationary. This implies there exists a (conditional) moving average representation from which measures of "information share" (IS) could be extracted. For identification purposes, the Markov error correction model is estimated within a Bayesian MCMC framework. Inference and capability of detecting regime changes are shown using a Montecarlo experiment. I also highlight the necessity of modeling financial effects of high frequency data for reliable inference.