In this thesis I study two fields of empirical finance: market integration and economic forecasting. The first two chapters focus on studying regional integration of Mexican and U.S. equity markets. In the third chapter, I propose the use of the daily term structure of interest rates to forecast inflation. Each chapter is a free-standing essay that constitutes a contribution to the field of empirical finance and economic forecasting. In Chapter 1, I study the ability of multi-factor asset pricing models to explain the unconditional and conditional cross-section of expected returns in Mexico. Two sets of factors, local and foreign factors, are evaluated consistent with the hypotheses of segmentation and of integration of the international finance literature. Only one variable, the Mexican U.S. exchange rate, appears in the list of both foreign and local factors. Empirical evidence suggests that the foreign factors do a better job explaining the cross-section of returns in Mexico in both the unconditional and conditional versions of the model. This evidence provides some suggestive support for the hypothesis of integration of the Mexican stock exchange to the U.S. market. In Chapter 2, I study further the integration between Mexico and U.S. equity markets. Based on the result from chapter 1, I assume that the Fama and French factors are the mimicking portfolios of the underlying risk factors in both countries. Market integration implies the same prices of risk in both countries. I evaluate the performance of the asset pricing model under the hypothesis of segmentation (country dependent risk rewards) and integration over the 1990-2004 period. The results indicate a higher degree of integration at the end of the sample period. However, the degree of integration exhibits wide swings that are related to both local and global events. At the same time, the limitations that arise in empirical asset pricing methodologies with emerging market data are evident. The data set is short in length, has missing observations, and includes data from thinly traded securities. Finally, Chapter 3, coauthored with John Maheu and Alex Maynard, studies the ability of daily spreads at different maturities to forecast inflation. Many pricing models imply that nominal interest rates contain information on inflation expectations. This has lead to a large empirical literature that investigates the use of interest rates as predictors of future inflation. Most of these focus on the Fisher hypothesis in which the interest rate maturity matches the inflation horizon. In general, forecast improvements have been modest. Rather than use only monthly interest rates that match the maturity of inflation, this chapter advocates using the whole term structure of daily interest rates and their lagged values to forecast monthly inflation. Principle component methods are employed to combine information from interest rates across both the term structure and time series dimensions. Robust forecasting improvements are found as compared to the Fisher hypothesis and autoregressive benchmarks.

In this study the prediction capabilities of Artificial Neural Networks and typical econometric methods are compared. This is done in the domains of Finance and Economics. Initially, the Neural Networks are shown to outperform traditional econometric models in forecasting nonlinear behaviour. The comparison is extended to indicate that the accuracy of share price forecasting is not necessarily improved when applying Neural Networks rather than traditional time series analysis. Finally, Neural Networks are used to forecast the South African inflation rates, and its performance is compared to that of vector error correcting models, which apparently outperform Artificial Neural Networks. / Prof. D.J. Marais

M.Com. (Econometrics) / The main purpose of this study is the combining of forecasts with special reference to major macroeconomic series of South Africa. The study is based on econometric principles and makes use of three macro-economic variables, forecasted with four forecasting techniques. The macroeconomic variables which have been selected are the consumer price index, consumer expenditure on durable and semi-durable products and real M3 money supply. Forecasts of these variables have been generated by applying the Box-Jenkins ARIMA technique, Holt's two parameter exponential smoothing, the regression approach and mUltiplicative decomposition. Subsequently, the results of each individual forecast are combined in order to determine if forecasting errors can be minimized. Traditionally, forecasting involves the identification and application of the best forecasting model. However, in the search for this unique model, it often happens that some important independent information contained in one of the other models, is discarded. To prevent this from happening, researchers have investigated the idea of combining forecasts. A number of researchers used the results from different techniques as inputs into the combination of forecasts. In spite of the differences in their conclusions, three basic principles have been identified in the combination of forecasts, namely: i The considered forecasts should represent the widest range of forecasting techniques possible. Inferior forecasts should be identified. Predictable errors should be modelled and incorporated into a new forecast series. Finally, a method of combining the selected forecasts needs to be chosen. The best way of selecting a m ethod is probably by experimenting to find the best fit over the historical data. Having generated individual forecasts, these are combined by considering the specifications of the three combination methods. The first combination method is the combination of forecasts via weighted averages. The use of weighted averages to combine forecasts allows consideration of the relative accuracy of the individual methods and of the covariances of forecast errors among the methods. Secondly, the combination of exponential smoothing and Box-Jenkins is considered. Past errors of each of the original forecasts are used to determine the weights to attach to the two original forecasts in forming the combined forecasts. Finally, the regression approach is used to combine individual forecasts. Granger en Ramanathan (1984) have shown that weights can be obtained by regressing actual values of the variables of interest on the individual forecasts, without including a constant and with the restriction that weights add up to one. The performance of combination relative to the individual forecasts have been tested, given that the efficiency criterion is the minimization of the mean square errors. The results of both the individual and the combined forecasting methods are acceptable. Although some of the methods prove to be more accurate than others, the conclusion can be made that reliable forecasts are generated by individual and combined forecasting methods. It is up to the researcher to decide whether he wants to use an individual or combined method since the difference, if any, in the root mean square percentage errors (RMSPE) are insignificantly small.

Thesis (Ph. D.)--Florida State University, 2003. / Advisor: Dr. Peter Easton, Florida State University, College of Education, Dept. Educational Leadership and Policy Studies. Title and description from dissertation home page (viewed Mar. 02, 2003). Includes bibliographical references.

In this thesis I study two fields of empirical finance: market integration and economic forecasting. The first two chapters focus on studying regional integration of Mexican and U.S. equity markets. In the third chapter, I propose the use of the daily term structure of interest rates to forecast inflation. Each chapter is a free-standing essay that constitutes a contribution to the field of empirical finance and economic forecasting. In Chapter 1, I study the ability of multi-factor asset pricing models to explain the unconditional and conditional cross-section of expected returns in Mexico. Two sets of factors, local and foreign factors, are evaluated consistent with the hypotheses of segmentation and of integration of the international finance literature. Only one variable, the Mexican U.S. exchange rate, appears in the list of both foreign and local factors. Empirical evidence suggests that the foreign factors do a better job explaining the cross-section of returns in Mexico in both the unconditional and conditional versions of the model. This evidence provides some suggestive support for the hypothesis of integration of the Mexican stock exchange to the U.S. market. In Chapter 2, I study further the integration between Mexico and U.S. equity markets. Based on the result from chapter 1, I assume that the Fama and French factors are the mimicking portfolios of the underlying risk factors in both countries. Market integration implies the same prices of risk in both countries. I evaluate the performance of the asset pricing model under the hypothesis of segmentation (country dependent risk rewards) and integration over the 1990-2004 period. The results indicate a higher degree of integration at the end of the sample period. However, the degree of integration exhibits wide swings that are related to both local and global events. At the same time, the limitations that arise in empirical asset pricing methodologies with emerging market data are evident. The data set is short in length, has missing observations, and includes data from thinly traded securities. Finally, Chapter 3, coauthored with John Maheu and Alex Maynard, studies the ability of daily spreads at different maturities to forecast inflation. Many pricing models imply that nominal interest rates contain information on inflation expectations. This has lead to a large empirical literature that investigates the use of interest rates as predictors of future inflation. Most of these focus on the Fisher hypothesis in which the interest rate maturity matches the inflation horizon. In general, forecast improvements have been modest. Rather than use only monthly interest rates that match the maturity of inflation, this chapter advocates using the whole term structure of daily interest rates and their lagged values to forecast monthly inflation. Principle component methods are employed to combine information from interest rates across both the term structure and time series dimensions. Robust forecasting improvements are found as compared to the Fisher hypothesis and autoregressive benchmarks.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2009. / Includes bibliographical references (p. 72-75).

Thesis (Ph. D.)--University of Wisconsin--Madison, 1959. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Bibliography, leaves 167-170.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 196-201). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 196-201). Also available on the Internet.

Thesis (Ph. D.)--University of California, Santa Cruz, 1998. / Typescript. Includes bibliographical references (leaves 114-118).