Bayesian density forecasting with applications to call center data and financial time series

This thesis consists of two parts. In the first part, we focus on modeling and forecasting arrival rates to a US commercial bank\u27s call center. In today\u27s economy, call centers have become the primary point of contact between customers and businesses. Accurate prediction of the call arrival rate is therefore indispensable for call center practitioners to staff their call center efficiently and cost effectively. We propose a, multiplicative model for modeling and forecasting within-day arrival rates. Markov chain Monte Carlo sampling methods are used to estimate both latent states and model parameters. One-day-ahead density forecasts for the rates and counts are provided. The calibration of these predictive distributions is evaluated through probability integral transforms. Furthermore, we provide one-day-ahead forecasts comparisons with classical statistical models. Our predictions show significant improvements of up to 25% over these standards. A sequential Monte Carlo algorithm is also proposed for sequential estimation and forecasts of the model parameters and rates. Finally, the effect of parameter uncertainty is analyzed in forecasting both the rates and call volumes. The second part focuses on yield curve modeling which has received extensive attention amongst academics and practitioners alike. Until recently though, very little effort was focused on forecasting the yield curve, which plays an important role in risk management, derivative pricing and portfolio management. Diebold and Li(2005) address this issue by fitting a dynamic version of the Nelson-Siegel Curve and compare the out-of-sample performance of this model with several existing models studied in the literature. We extend this work by considering more elaborate dynamics for the three latent factors. Markov chain Monte Carlo sampling methods are used to estimate both latent states and model parameters. In addition, one-step, six-step and twelve-step ahead forecast densities of the yields are provided. The calibration of these predictive distributions is evaluated through probability integral transforms