Submitted by PEDRO ALVES (pguedesalves@gmail.com) on 2013-09-17T14:48:59Z No. of bitstreams: 1 Dissertacao pedro guedes alves final.pdf: 359622 bytes, checksum: 4ea301abbbeba3d60296bea87411ee79 (MD5) / Approved for entry into archive by Vitor Souza (vitor.souza@fgv.br) on 2013-10-09T15:39:12Z (GMT) No. of bitstreams: 1 Dissertacao pedro guedes alves final.pdf: 359622 bytes, checksum: 4ea301abbbeba3d60296bea87411ee79 (MD5) / Made available in DSpace on 2013-10-11T13:21:08Z (GMT). No. of bitstreams: 1 Dissertacao pedro guedes alves final.pdf: 359622 bytes, checksum: 4ea301abbbeba3d60296bea87411ee79 (MD5) Previous issue date: 2012-05-31 / Este trabalho busca medir a credibilidade do Banco Central Brasileiro. Utiliza-se como medida da credibilidade, a variação do prêmio de risco de inflação em função de surpresas inflacionárias de curto prazo no índice IPCA. Primeiro evidencia-se que as expectativas inflacionárias de médio prazo são afetadas pelas surpresas inflacionárias, este efeito é causado por dois motivos, a indexação da economia e/ou a falta de credibilidade da autoridade monetária. Em seguida verifica-se que as surpresas inflacionárias também tem efeito sobre o premio de risco de inflação o que indica falta de credibilidade do banco central. / This paper seeks to measure the credibility of the Brazilian Central Bank. It uses as a measure of credibility, the change in the inflation risk premium in terms of short-term inflationary surprises in the IPCA index. At first, it is shown that the medium-term inflation expectations are affected by inflation surprises, this effect is caused by two reasons, the indexation of the economy and/or lack of credibility of the monetary authority. Then it is observed that the inflation surprises also have an effect on the inflation risk premium, which indicates a lack of credibility of the central bank.

This thesis aims to provide new insights into the importance of decomposing aggregate time series data using the Maximum Overlap Discrete Wavelet Transform. In particular, the analysis throughout this thesis involves decomposing aggregate financial time series data at hand into approximation (low-frequency) and detail (high-frequency) components. Following this, information and hidden relations can be extracted for different investment horizons, as matched with the detail components. The first study examines the ability of different GARCH models to forecast stock return volatility in eight international stock markets. The results demonstrate that de-noising the returns improves the accuracy of volatility forecasts regardless of the statistical test employed. After de-noising, the asymmetric GARCH approach tends to be preferred, although that result is not universal. Furthermore, wavelet de-noising is found to be more important at the key 99% Value-at-Risk level compared to the 95% level. The second study examines the impact of fourteen macroeconomic news announcements on the stock and bond return dynamic correlation in the U.S. from the day of the announcement up to sixteen days afterwards. Results conducted over the full sample offer very little evidence that macroeconomic news announcements affect the stock-bond return dynamic correlation. However, after controlling for the financial crisis of 2007-2008 several announcements become significant both on the announcement day and afterwards. Furthermore, the study observes that news released early in the day, i.e. before 12 pm, and in the first half of the month, exhibit a slower effect on the dynamic correlation than those released later in the month or later in the day. While several announcements exhibit significance in the 2008 crisis period, only CPI and Housing Starts show significant and consistent effects on the correlation outside the 2001, 2008 and 2011 crises periods. The final study investigates whether recent returns and the time-scaled return can predict the subsequent trading in ten stock markets. The study finds little evidence that recent returns do predict the subsequent trading, though this predictability is observed more over the long-run horizon. The study also finds a statistical relation between trading and return over the long-time investment horizons of [8-16] and [16-32] day periods. Yet, this relation is mostly a negative one, only being positive for developing countries. It also tends to be economically stronger during bull-periods.