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<p>In this paper, we study the industrial production index of Sweden from Jan, 2000 to latest Feb, 2010. We find out there is a structural break at time point Dec, 2007, when the global financial crisis burst out first in U.S then spread to Europe. To model the industrial production index, one of the business cycle indicators which may behave nonlinear feature suggests utilizing a smooth transition autoregressive (STAR) model. Following the procedures given by Teräsvirta (1994), we carry out the linearity test against the STAR model, determine the delay parameter and choose between the LSTAR model and the ESTAR model. The results from the estimated model suggest the STAR model is better performing than the linear autoregressive model.</p>

In this paper, we study the industrial production index of Sweden from Jan, 2000 to latest Feb, 2010. We find out there is a structural break at time point Dec, 2007, when the global financial crisis burst out first in U.S then spread to Europe. To model the industrial production index, one of the business cycle indicators which may behave nonlinear feature suggests utilizing a smooth transition autoregressive (STAR) model. Following the procedures given by Teräsvirta (1994), we carry out the linearity test against the STAR model, determine the delay parameter and choose between the LSTAR model and the ESTAR model. The results from the estimated model suggest the STAR model is better performing than the linear autoregressive model.

[pt] Desde o século XVI, já existia a idéia de que o poder de compra deveria influenciar no valor de cada moeda. A fim de se entender as relações entre câmbio e inflação, modelos autoregressivos lineares sempre apresentaram dificuldades para superar o passeio aleatório. Possíveis fricções em operações cambiais podem dificultar a arbitragem próxima do câmbio de equilíbrio considerado pelos agentes financeiros. À medida em que se distancia do valor considerado justo, a convergência se torna mais intensa, pois os custos já não seriam uma parcela tão relevante para o lucro potencial da operação. No modelo não-linear proposto, há dois regimes diferentes: um próximo do equilíbrio (comportamento de passeio aleatório) e um comportamento longe dele ocorrendo simultaneamente, mas com pesos variáveis. A depender do nível do câmbio em relação ao equilíbrio, um regime ganha mais peso e outro perde relevância. Essa tese tem o objetivo de avaliar o caráter preditivo do movimento cambiais. O modelo não-linear ESTAR é usado para montar cestas de moedas a serem compradas e vendidas e o retorno advindo de oscilações cambiais é computado. Por fim, incorporamos os efeitos de juros ao modelo para montar portfólios de moedas a fim de simular o retorno de um investimento usando essa estratégia. Para as cestas de moedas, o modelo gerou bons retornos e baixos riscos, tanto em termos de desvio padrão quanto em termos de drawdown. Tal característica foi observada no modelo in-sample e no out-of-sample o que indica um forte caráter preditivo. Levando em conta o efeito dos juros, os portfólios com menos moedas apresentaram retornos positivos, porém essa vantagem é perdida ao se aumentar a quantidade de moedas. / [en] Since the sixteenth century, there was already the idea that purchasing power should influence the value of each currency. In order to understand the relationship between exchange rate and inflation, linear autoregressive models always presented difficulties to beat the random walk. Possible frictions in foreign exchange operations may hinder arbitrage close to the equilibrium exchange rate considered by financial agents. As the exchange rate distances itself from the value considered fair, the convergence becomes more intense, because the costs would no longer be so relevant to the potential profit of the operation. In the proposed nonlinear model, there are two different regimes: one near equilibrium (random walk behavior) and one behavior away from it occurring simultaneously, but with variable weights. For different levels of the exchange rate relative to the equilibrium, one regime gains more weight and the other loses relevance. This thesis aims to evaluate the predictive nature of the exchange rate movement. The nonlinear model ESTAR is used to create baskets of currencies to be bought and sold and the aggregate return based on exchange rate movements is computed. Finally, we consider the interest rate effects on the model to set up currencies portfolios in order to simulate the return on an investment using this strategy. For the baskets of currencies, the model generated good returns and low risks, based on both standard deviation and drawdown. This characteristic was observed in the in-sample model and in the out-of-sample model, which indicates a strong predictive power. Considering the interest effect, portfolios with fewer currencies showed positive returns, but this advantage is lost by increasing the number of currencies.

Housing prices are of crucial importance in financial stability management. The severe financial crises that originated in the housing market in the US and subsequently spread throughout the world highlighted the crucial role that the housing market plays in preserving financial stability. After the severe housing market crash, many financial institutions in the US suffered from high default rates, severe liquidity shortages, and even bankruptcy. Against this background, researchers have sought to use econometric models to capture and forecast prices of homes. Available empirical research indicates that nonlinear models may be suitable for modelling price cycles. Accordingly, this thesis focuses primarily on using nonlinear models to empirically investigate cyclical patterns in housing prices. More specifically, the content of this thesis can be summarised in three essays which complement the existing literature on price modelling by using nonlinear models. The first essay contributes to the literature by testing the ability of regime switching models to capture and forecast house prices. The second essay examines the impact of banking factors on house price fluctuations. To account for house price characteristics, the regime switching model and generalised autoregressive conditionally heteroscedastic (GARCH) in-mean model have been used. The final essay investigates the effect of structural breaks on the unit root test and shows that a time-varying GARCH in-mean model can be used to estimate the housing price cycle in the UK.

¡@¡@The purpose of this research is to employ the STAR model in discussing and analyzing the relationship between stock index and macroeconomic variables in Taiwan, Japan and Korea. ¡@¡@Monthly stock market index data is analyzed over the period January 1990 to December 2000, with the sample period from January 2001 to April 2005 being used in an out-of -sample forecasting exercise. The macroeconomic variables considered in this paper include money supply, consumer price index, industrial production index, interest rate and exchange rate. ¡@¡@The empirical results of Taiwan, Japan and Korea show that LSTAR & ESTAR model improve both the in-sample fit and out-of-sample forecast of the data over both the linear model alternative.

Submitted by Jordano Vieira Rocha (jordanorocha@hotmail.com) on 2015-04-30T08:48:24Z No. of bitstreams: 1 Dissertação - Jordano Vieira Rocha.pdf: 1057882 bytes, checksum: 1ba84113f5ec0c31d9c99f3bebe4714d (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2015-04-30T13:02:56Z (GMT) No. of bitstreams: 1 Dissertação - Jordano Vieira Rocha.pdf: 1057882 bytes, checksum: 1ba84113f5ec0c31d9c99f3bebe4714d (MD5) / Made available in DSpace on 2015-04-30T17:23:54Z (GMT). No. of bitstreams: 1 Dissertação - Jordano Vieira Rocha.pdf: 1057882 bytes, checksum: 1ba84113f5ec0c31d9c99f3bebe4714d (MD5) Previous issue date: 2015-04-07 / This work assesses the forecasts of three nonlinear methods — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model, and Autometrics with Dummy Saturation — for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double differencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double differencing device, but the linear autoregressive model is more accurate than all the other methods analyzed. / Este trabalho avalia as previsões de três métodos não lineares — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model e Autometrics com Dummy Saturation — para a produção industrial mensal brasileira e testa se elas são mais precisas que aquelas de preditores naive, como o modelo autorregressivo de ordem p e o mecanismo de double differencing. Os resultados mostram que a saturação com dummies de degrau e o Logistic Smooth Transition Autoregressive Model podem ser superiores ao mecanismo de double differencing, mas o modelo linear autoregressivo é mais preciso que todos os outros métodos analisados.

In an effort to assess the predictive ability of exchange rate models when data on African countries is sampled, this paper studies nonlinear modelling and prediction of the nominal exchange rate series of the United States dollar to currencies of thirty-eight African states using the smooth transition autoregressive (STAR) model. A three step analysis is undertaken. One, it investigates nonlinearity in all nominal exchange rate series examined using a chain of credible statistical in-sample tests. Significantly, evidence of nonlinear exponential STAR (ESTAR) dynamics is detected across all series. Two, linear models are provided another chance to make it right by shuffling to data on African countries to investigate their predictive power against the tough random walk without drift model. Linear models again failed significantly. Lastly, the predictive ability of nonlinear models against both the random walk without drift and the corresponding linear models is investigated. Nonlinear models display useful forecasting gains over all contending models.

The recent empirical literature has shown that the dynamic generating mechanism of macroeconomic variables can be asymmetric. Inspiring from these empirical results, this thesis uses a class of nonlinear models called smooth transition autoregressive models to investigate possible asymmetric dynamics in inflation and nominal exchange rate series of Turkey. Estimation results imply that variables under consideration contain strong nonlinearities and these can be modeled by STAR models.

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