In this paper we compare the in-sample fit and out-of-sample forecasting performance of no-arbitrage quadratic and essentially affine term structure models, as well as the dynamic Nelson-Siegel model. In total eleven model variants are evaluated, comprising five quadratic, four affine and two Nelson-Siegel models. Recursive re-estimation and out-of-sample one-, six- and twelve-months ahead forecasts are generated and evaluated using monthly US data for yields observed at maturities of 1, 6, 12, 24, 60 and 120 months. Our results indicate that quadratic models provide the best in-sample fit, while the best out-of-sample performance is generated by three-factor affine models and the dynamic Nelson-Siegel model variants. However, statistical tests fail to identify one single-best forecasting model class.