A Data Science Central Community
Peter Feldh Linda S. Larsen Claus Munk Anders B. Trolle
We investigate the impact of parameter uncertainty on the performance of bond portfolios. We assume that the data generating process is represented by the well-established three-factor essentially ane Gaussian term structure model. We estimate this model and three simpler models to US data using the
Markov Chain Monte Carlo method which provides a posterior distribution of parameters given the data and a point estimate (the median). An investor following the seemingly optimal portfolio strategy for the true model using the parameter point estimate will suer a utility loss if the true parameters dier
from the point estimate, and we nd that the average utility loss based on the posterior parameter distribution is big. The degree of parameter uncertainty increases with the number of term structure factors, and we show that investors with moderate or high risk aversion will suer a smaller average utility loss if they follow the portfolio strategy of a simple one-factor model instead of the
more complex true model.