Se analiza la relación del crecimiento económico con el gasto público de 16 países latinoamericanos de 1990 a 2017. Este trabajo contribuye a la literatura sobre el tema enfocándose en la región. Los resultados de un modelo para paneles cointegrados respaldan la ley de Wagner en el largo plazo y brindan evidencia parcial a favor de las hipótesis de Keynes en el corto plazo. PUBLIC SPENDING AND GROWTH IN LATIN AMERICA:WAGNER´S LAW AND KEYNES’S HYPOTHESISABSTRACTThe relationship between economic growth and public spending in 16 Latin American countries from 1990 to 2017 is analyzed. This paper contributes to the literature on the subject focusing on the region. The results from a model of cointegrated panels support Wagner’s Law in the long term and provides partial evidence in favor of the Keynesian hypotheses in the short term.

This paper studies the integration of the Mexican Stock Exchange (MSE) into the World Capital Market (WCM). We detect a long-run equilibrium relationship, despite the effects of structural breaks associated to different financial crises during our period of analysis (1987-2012). The analytical approach begins with the estimation of a bivariate VECM in the mean, including several dummy variables that capture the main crisis episodes that took place during the estimation period. Next, we specify a VARMA-GARCH model with Dynamic Conditional Correlation, and, finally, we fit a Clayton copula to returns, conditional on two volatility regimes (low and high), in order to further understand the nature of their dependence structure.

This paper evaluates machine learning (ML) algorithms for forex trading based on directional forecasting over the 2018–2023 period. We conduct a rigorous comparative analysis of seven ML techniques—Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, AdaBoost, XGBoost, and Neural Networks—across eight currency pairs against the US dollar, including major (EUR, JPY, CHF, AUD), managed float (CNY), and emerging market (MXN, ZAR, TRY) currencies. Our methodology implements comprehensive hyperparameter optimization through time series cross-validation, realistic dynamic transaction costs based on institutional spreads, and extends evaluation to future validation periods. Uniquely, we compare traditional accuracy-based optimization with the profit-aware Mean Absolute Directional Loss (MADL) function for model selection for the Logistic Regression model. Results demonstrate that simpler, interpretable models achieve superior risk-adjusted returns, with Logistic Regression optimized using MADL obtaining the highest Risk-Adjusted Performance Index (RAPI) scores of 1.45–1.58. All ML strategies show statistical significance after multiple testing corrections (Hansen’s SPA test, t-statistics: 2.96–2.89), with medium to large economic effect sizes (Cohen’s d: 0.47–0.60). However, performance varies dramatically with transaction costs—while major pairs remain profitable with round-trip costs below 0.4%, emerging market currencies become unviable at institutional spreads exceeding 1.0%. Value-at-Risk backtesting reveals that ML models calibrated for directional prediction fail catastrophically for risk estimation (29.1% average violations versus 5% expected), mandating separate risk management frameworks. Our findings challenge the complexity bias in financial ML, demonstrating that interpretable models with proper profit-aware optimization outperform black-box approaches in real-world trading conditions. Clinical trial number: not applicable.

This study comparatively evaluated the forecasting performance of a constant beta and two time-varying beta process specifications. Returns for 23 stocks were forecasted for several horizons in 2019–2020. The autoregressive and random walk betas showed superior forecasting performance before and during the COVID-19 pandemic, respectively. In a Diebold–Mariano test, the constant beta specification never dominated both time-varying beta models. Beta specification type should be considered when forecasting based on capital asset pricing or market models, particularly during crises. Results of rolling regressions based on arbitrage pricing theory considering other risk factors suggest time-varying systematic risk factors beyond market risks.

El objetivo del presente trabajo es averiguar si el desarrollo financiero ha influido en el crecimiento económico después de 1990 (el análisis comprende el periodo 1990-2004), lapso en el que se llevó a cabo una serie de reformas que han tendido a liberalizar el sector financiero en México. La hipótesis de McKinon-Shaw establece que las restricciones gubernamentales sobre el sector financiero restringen y distorsionan el proceso de desarrollo de este sector y, por ende, inhiben el proceso de crecimiento económico. La estimación de la incidencia del desarrollo financiero en el crecimiento económico se realiza por medio de una función de producción agregada dinámica, similar a la empleada por Asteriou y Price (2000). En contraposición a los resultados obtenidos por Tinoco, Torres y Venegas (2008), encontramos que el desarrollo financiero ha tenido un impacto positivo en el crecimiento económico en el periodo estudiado.