Conditional Density Estimation and Density Forecast With Applications

The stocks that were studied were the NYSE and TSX. The dependence of one stock market on the other was also studied and how much of a factor it plays in forecasting the next price. Weather data was also studied. The three weather variables studied were the daily averages of pressure,wind speed and temperature for the Ottawa region. Multiple forecast densities were built and compared to see which method has the highest accuracy. It was found that when dealing with non-linear data, the one-step method is more efficient in estimating the true conditional density. The two-step approach is better when trying to estimate a univariate conditional density. When trying to estimate multivariate conditional density the further one goes away from stationarity, the better that the 2-step method is. Real world data indicates that multivariate methodology was better at predicting future events.