Essays in Market Integrations, and Economic Forecasting
Keywords: Market Integration
Emerging Markets
Economic Forecasting

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.