Evaluating monthly volatility forecasts using proxies at different frequencies

This paper analyses the forecastability of stock returns monthly volatility. The forecast obtained from GARCH and AGARCH models with Normal and Student's t errors are evaluated with respect to proxies for the unobserved volatility obtained through sampling at different frequencies. It is found that aggregation of daily multi-step ahead GARCH-type forecasts provide rather accurate predictions of monthly volatility.

Should cash transfer be conditional? Condition, preventive care, and health outcomes

We study a Conditional Cash Transfer program in which the cash transfers to the mother only depends on the fulfillment of the national preventive visit schedule by her children born before she registered in the program. We estimate that preventive visits of children born after the mother registered in the program are 50% lower because they are excluded from the conditionality requirement. Using the same variation, we also show that attendance to preventive care improves children's health.

Volatility and VaR Forecasting in the Madrid Stock Exchange

This paper provides an empirical study to assess the forecasting performance of a wide range of models for predicting volatility and VaR in the Madrid Stock Exchange. The models performance was measured by using different loss functions and criteria. The results show that FIAPARCH processes capture and forecast more accurately the dynamics of IBEX-35 returns volatility. It is also observed that assuming a heavy-tailed distribution does not improve models ability for predicting volatility. However, when the aim is forecasting VaR, we find evidence of that the Student’s t FIAPARCH outperforms the models it nests the lower the target quantile.