Este trabalho tem objetivo de estudar os ciclos de crédito em cinco países da América Latina – Brasil, Chile, Colômbia, México e Peru - usando modelos com mudança de regime markoviano univariados e multivariados. Alguns dos modelos são capazes de captar períodos de crises bancárias nos países individualmente datados em Laeven e Valencia (2008, 2012) e Reinhart e Rogoff (2008), enquanto o modelo multivariado capta uma dinâmica comum nos países estudados. O ciclo que o modelo multivariado revela está de acordo com conhecidos períodos de expansão e contração da taxa de crescimento do crédito real ao setor privado conhecidos na literatura, em especial o boom da primeira metade da década de 1990 e sua desaceleração subseqüente. / This paper aims to study credit cycles in five Latin American countries in a Markov Switching Approach with univariate and multivariate models. The univariate models, for some countries, identified periods of banking crises dated in Laeven and Valencia (2008; 2012) and Reinhart and Rogoff (2008) while the multivariate model captured a common dynamic in those countries studied. The cycle revealed with this model is in accordance with known periods of expansion and contraction of the growth rate credit in Latin America, in special the early 1990’s boom and it’s subsequent slowdown.

O objetivo desta dissertação é avaliar o comportamento dos principais parâmetros da economia brasileira através da estimação de um modelo DSGE (Dynamic Stochastic General Equilibrium) de economia aberta usando métodos bayesianos e permitindo mudanças de regime markovianas de determinados parâmetros. Utilizando o modelo DSGE desenvolvido por Justiniano e Preston (2010) e o método de solução do modelo Markov Switching DSGE (MS-DSGE) proposto por Farmer et al. (2008), este trabalho encontrou superioridade nos ajustes dos dados dos modelos que incorporaram mudanças markovianas, rejeitando a hipótese de parâmetros constantes em modelos DSGE para a economia brasileira. / The goal of this dissertation is to evaluate the behaviour of the main parameters of the Brazilian economy through the estimation of a DSGE (Dynamic Stochastic General Equilibrium) model of open economy using Bayesian methods and allowing Markov switching of certain parameters. Using the DSGE model developed by Justiniano and Preston (2010) and the method of solution of the Markov Switching DSGE (MS-DSGE) model proposed by Farmer et al. (2008), this work found superiority in the settings of the data of the models that incorporated Markov switching, rejecting the hypothesis of constant parameters in DSGE models for the Brazilian economy.

A relação entre Variáveis Macroeconômicas e o Retorno de Ações é de alta importância para pesquisas econômicas e financeiras, já que, quando descoberto, um mecanismo de conhecer ou prever o impacto dessas variáveis oportuniza uma melhor performance de investidores no mercado acionário. Nesse sentido, nosso trabalho testa nove variáveis macroeconômicas (Preço de Commodities, Taxa de Desemprego, Inflação, Agregados Monetários, Taxas de juros, Relative Money Market Rate (RMM), Produção Industrial, Hiato do Produto (GAP) e Taxa de juros dos EUA) contra o retorno real do Ibovespa, empregando regressões lineares, como tradicional na literatura, e modelos de mudança de regime markoviana (MSM), para avaliar melhor o impacto e poder de previsão do retorno sob uma economia tão perturbada por planos econômicos e crises financeiras. Além disso, realizamos uma rigorosa avaliação do poder preditivo através de testes dentro e fora da amostra, incluindo avaliações dos coeficientes estimados defasados, critérios de Informação de AIC e BIC, Razões de Erro Quadrático Médio e o Erro Absoluto Médio e testes de encompassing de Diebold e Mariano (1995), de Clark e Mccracken (2001) e de Mccracken (2007), combinados aos novos valores assintóticos de Clark e Mccracken (2001,2004). Os resultados indicam que o Ibovespa possui dois regimes, e que a variável Hiato do Produto se destaca por ser a variável mais significativa e de maior poder de previsão, tanto nos modelos lineares como nos nãolineares. Além dessa, a variável RMM, também se mostrou capacitada para prever o retorno quando estimada no MSM, assim como as variáveis inflação e agregados monetários também apresentaram poder preditivo quando acompanhados da variável GAP. Entretanto, Produção industrial e taxa de juros não tiveram qualquer evidência de capacidade preditiva. Por fim, nos horizontes trimestrais e semestrais, os MSM tiveram dificuldade de encontrar os diferentes regimes, e por isso, não conseguiram se mostrar sistematicamente superiores aos modelos lineares. / The relationship between Macroeconomic Variables and stock returns is of high importance for economic and financial research because, when discovered, a mechanism to know or predict the impact of these variables allows a better performance of investors in the stock market In this sense, our research tests nine macroeconomic variables (Commodities Prices, Unemployment Rate, Inflation, Money Stock, Interest Rate, Relative Money Market Rate (RMM), Industrial Production, Output Gap (GAP) and United States Interest Rate) versus the Ibovespa Real Stock Return, with linear models, as in traditional literature, and with Markov Switching Models, to gauge the impact and the predictive power of the assumption of an economy so troubled by economic plans and financial crises. In addition, we conducted a rigorous predictive ability evaluation by testing in-sample and out-of-sample, including a lagged coefficient estimated evaluation, information criteria of Akaike and Schwarz, Mean-square Error, Absolute Mean Error and encompassing tests of Diebold e Mariano (1995), Clark e Mccracken (2001) and Mccracken (2007) combined with the new asymptotic values of Clark e Mccracken (2001,2004). The results indicated that the Ibovespa has two states and the Output Gap variable stands out for being the most significant variable and with the greatest predictive ability for both linear and nonlinear models. Besides, the RMM variable has also shown to be able to predict the stock return when estimated in the MSM. Furthermore, the inflation and money stock variable also presents predict ability when estimated models is addicted with GAP variable. Industrial production and interest rates had no evidence of predictive ability. Finally, in the quarterly and semiannual horizons, the MSM had difficulty in finding the different regimes, and therefore failed to show themselves consistently higher than the linear models.

O objetivo desta dissertação é avaliar o comportamento dos principais parâmetros da economia brasileira através da estimação de um modelo DSGE (Dynamic Stochastic General Equilibrium) de economia aberta usando métodos bayesianos e permitindo mudanças de regime markovianas de determinados parâmetros. Utilizando o modelo DSGE desenvolvido por Justiniano e Preston (2010) e o método de solução do modelo Markov Switching DSGE (MS-DSGE) proposto por Farmer et al. (2008), este trabalho encontrou superioridade nos ajustes dos dados dos modelos que incorporaram mudanças markovianas, rejeitando a hipótese de parâmetros constantes em modelos DSGE para a economia brasileira. / The goal of this dissertation is to evaluate the behaviour of the main parameters of the Brazilian economy through the estimation of a DSGE (Dynamic Stochastic General Equilibrium) model of open economy using Bayesian methods and allowing Markov switching of certain parameters. Using the DSGE model developed by Justiniano and Preston (2010) and the method of solution of the Markov Switching DSGE (MS-DSGE) model proposed by Farmer et al. (2008), this work found superiority in the settings of the data of the models that incorporated Markov switching, rejecting the hypothesis of constant parameters in DSGE models for the Brazilian economy.

Este trabalho tem objetivo de estudar os ciclos de crédito em cinco países da América Latina – Brasil, Chile, Colômbia, México e Peru - usando modelos com mudança de regime markoviano univariados e multivariados. Alguns dos modelos são capazes de captar períodos de crises bancárias nos países individualmente datados em Laeven e Valencia (2008, 2012) e Reinhart e Rogoff (2008), enquanto o modelo multivariado capta uma dinâmica comum nos países estudados. O ciclo que o modelo multivariado revela está de acordo com conhecidos períodos de expansão e contração da taxa de crescimento do crédito real ao setor privado conhecidos na literatura, em especial o boom da primeira metade da década de 1990 e sua desaceleração subseqüente. / This paper aims to study credit cycles in five Latin American countries in a Markov Switching Approach with univariate and multivariate models. The univariate models, for some countries, identified periods of banking crises dated in Laeven and Valencia (2008; 2012) and Reinhart and Rogoff (2008) while the multivariate model captured a common dynamic in those countries studied. The cycle revealed with this model is in accordance with known periods of expansion and contraction of the growth rate credit in Latin America, in special the early 1990’s boom and it’s subsequent slowdown.

This dissertation investigates the dynamics of mean and volatility spillovers from the U.S. and three large (regional) Asia-Pacific stock markets to ten small (local) ones from June 2008 to May 2013. After a brief introduction to the main purposes and contributions of my research in Chapter 1, I examine the impact of lagged American and regional returns on the local markets in Chapter 2. By building up a univariate autoregressive model and treating lagged U.S. and regional returns as exogenous variables, I find that the local markets have statistically significant exposure to lagged returns of their own and the U.S. market only. The empirical results suggest that lagged American returns have exerted considerable mean spillover impact upon most of the local markets, whereas the large Asia-Pacific markets involved in this study have few such impacts. I study the linkage between the U.S. market and each of the regional markets in Chapter 3 by employing two specifications of the bivariate GARCH process—the BEKK and general dynamic covariance (DC) models—to capture common features of equity return data. Based on the results of carefully constructed diagnostic tests, the BEKK model is demonstrated to be more appropriate for the U.S.–China and U.S.–Japan cases, and the dynamic covariance model for the U.S.–Australia case. In Chapter 4, I discuss time-varying correlation of a local market with the U.S. market and with each regional market by proposing three Markov-switching shock spillover models. A comparison of model performance is drawn based on a series of model selection criteria. In fourteen cases, the local market is found to be more sensitive to regional shocks. Disturbances from two regional markets account for a higher proportion of local variance than those of U.S. origin. I conclude that the regional center, although having little mean spillover effect upon the local markets, has become increasingly influential in volatility transmission. Possible extended studies in the future as well as main findings in the preceding chapters are summarized in Chapter 5.

The aim of this thesis is to estimate monthly probability that the Czech economy is in a recession. For this purpose, I construct indexes of coincident and leading variables from multiple time series by Maximum Likelihood. Changes in coincident index are preceded by changes in the leading index by almost one year for peaks and about one month for troughs on average. To assess the probability of recession, I estimate multiple mixture models for growth rates of coincident index focusing on Markov-Switching specification for the latent business cycle process. I found that the two-state Markov-Switching AR (1) is superior to other models based on information criteria. Lagged values of leading index further improve the model fit but the model provides less clear signals of recessions compared to models based solely on coincident index.

An exact solution for the valuation of the options of the European style can be obtained using the Black-Scholes model. However, some of the limitations of the Black-Scholes model are said to be inconsistent such as the constant volatility of the stock price which is not the case in real life. In this thesis, the Black-Scholes model is extended to a model where the volatility is fully stochastic and changing over time, modelled by Markov chain with three states - high, medium and low. Under this model, we price options of both types, European and American, using Monte Carlo simulation.

This paper examines the effects of monetary policy on macroeconomic variables in Botswana as a developing small macro-economy using the Markov-switching structural vector autoregressive (MS-SVAR) framework, utilising time-series data from 1994: Q1 to 2019: Q4. The study makes use of bank rate (interest rate), inflation and output gap. The first model is a structural vector autoregressive (VAR) model that takes the form employed by Rudebusch and Svensson (1999), whilst the second one makes use of the same structure but includes Markov switching in the policy rule (i.e., Markov switching SVAR). Regime-switching models can effectively describe the data generating process when considering both in-sample and out of sample evaluations compared to the linear models, which submerge the structural changes that have occurred in the economy over the years. The results from the SVAR shows that monetary policy has a symmetric impact on the output gap and inflation. Therefore, it can be noted that non-linearities in the structural model do not necessarily imply asymmetric effects of shocks. Furthermore, the MS-SVAR shows that the Central Bank of Botswana responds differently to policy shocks in different regimes. This underscores the importance of regime-switching features in providing a more accurate description of the economy.

We develop a non-linear Markov error correction approach to examine the general co-integration relation between the H- and A-prices of cross-listed Chinese stock issuers across the period January 1999 to March 2009. We unravel three important dimensions of this relation. These pertain to (i) the long-run expectation of the H- (to A-price) discount; (ii) the level of short-run co-movement in prices; and (iii) the magnitude of error corrections. Findings point to significant improvements in all three areas. Policy and corporate governance change appears to be the principal force driving the efficiency gains. Weakening informational asymmetries underlie much of the change in the markets’ relative pricing. In contrast, sentiment effects strongly underpin the contemporaneous response and error correction adjustments. Finally, the escalating Global Financial Crisis of 2008 appears to have not only bolstered the A- and H-markets’ short-term pricing dynamic but also temporarily increased the long-term H-share discount.