Exposure to ambient air pollution is a risk factor for morbidity and mortality from lung and heart disease. Does short term exposure to ambient air pollution influence COVID-19 related mortality? Using time series analyses we tested the association between daily changes in air pollution measured by stationary monitors in and around Santiago, Chile and deaths from laboratory confirmed or suspected COVID-19 between March 16 and August 31, 2020. Results were adjusted for temporal trends, temperature and humidity, and stratified by age and sex. There were 10,069 COVID-19 related deaths of which 7659 were laboratory confirmed. Using distributed lags, the cumulative relative risk (RR) (95% CI) of mortality for an interquartile range (IQR) increase in CO, NO₂ and PM_2.5 were 1.061 (1.033-1.089), 1.067 (1.023-1.103) and 1.058 (1.034-1.082), respectively There were no significant differences in RR by sex.. In those at least 85 years old, an IQR increase in NO₂ was associated with a 12.7% (95% CI 4.2-22.2) increase in daily mortality. This study provides evidence that daily increases in air pollution increase the risk of dying from COVID-19, especially in the elderly.

This letter applies the Hinich portmanteau bicorrelation test jointly with the windowed testing procedure to detect nonlinear behaviour in the rate of returns series for seven Latin American stock market indices. Our results suggest that the nonlinear serial dependencies are episodic in nature. All the stock returns series are characterized by few brief periods of highly significant nonlinearity, followed by long time periods in which the returns follow a pure noise process. Our findings help explain why there are difficulties in forecasting asset returns.

This study searches for economic and political events that may explain the episodic nonlinearities detected in the returns series of the Chilean stock market index. This methodology is a reverse form of event study. After applying the Hinich portmanteau bicorrelation test to detect episodes of nonlinear behaviour of the index, we investigate what might be the explanation of this behaviour. Our findings may help to explain the difficulty to forecast asset returns. We also shed some light into the major political and economic events that contribute to the numerous short bursts of nonlinear dependence in the Chilean stock market.

We use a new statistical test based on the signal coherence function to detect subtle periodicities in the Chilean exchange rate. We resort to a unique intraday data set that allows us to capture persistent cyclical movements during the day that challenge the random walk hypothesis. We providea microstructural explanationfor the observed behavior, and also look at the day-of-the-week effect for the Chilean peso and find that the different days of the week indeed have different behavior patterns. This is an important result for investment allocation and risk assessment.

This article checks for the adequacy of using GARCH models in exchange rate series. Using the Hinich portmanteau bicorrelation test, we find that a GARCH formulation or any of its variants fails to capture the data generating process of the main Latin American exchange rates. Our results highlight the potential of having misleading public policy when estimates are based in GARCH types of models. This article also complements recent similar findings encountered in European and Asian economies.