In this thesis, we analyze the forecasting performance of three versions of the Dynamic Nelson-Siegel (DNS) model derived by Diebold and Li (2006), applied to LIBOR rates during the time period between January 2003 and March 2015. Our objective is to determine if it would be suitable to use the DNS model to forecast LIBOR rates for the Counterparty Credit Risk (CCR) measurement. The rst version represents the standard DNS AR(1) model with a xed decay parameter ( ), where lambda governs the speed of decay for the other model factors. Small values of lambda results in a better t at longer horizons and vice versa. Our second DNS model also has the same AR(1) factor dynamics, but with a time dependent decay parameter, i.e., ( ) varies over time. Lastly, we have a DNS model with VAR(1) factor dynamics. We compare the results of these estimates to those from benchmark models, including the random walk model, simple AR(1) and VAR(1) models, AR(1) on three principal components, and a slope regression model. Before assessing the forecasting ability we also analyse the in-sample t and nd that the DNS models show good insample results. The forecasting section involves out-of-sample forecasts, distribution forecasts, and backtesting of the DNS model. First, by letting lambda vary over time in the DNS model we are able to produce slightly better out-of-sample forecasting results than the traditional DNS model with xed lambda. However, our overall ndings indicate that none of our DNS models are able to keep up with the forecasting performance of the random walk model or the simple AR(1) model. Thus, we can conclude that from our analysis there is no convincing advantage in using the more advanced and complicated Dynamic Nelson-Siegel model over a simple AR(1) or random walk model. Finally, our backtesting results support our ndings of the overall poor forecasting ability of the DNS model, and indicate that further studies need to be conducted to develop a forecasting model suitable to include in CCR measurement.
|Educations||MSc in Advanced Economics and Finance, (Graduate Programme) Final Thesis|
|Number of pages||107|