摘要：
Traditional methods of modeling and estimating the term structure of interest rates assume that bonds are frequently
traded and have a complete data set. In this paper, we use the Kalman filter approach to estimate and compare the
term structure of assets with complete and sparse data set in the same country. Brazil offers a unique case study because
the stock of government bonds is one of the largest in the world. We test for two types of financial assets: government
bonds (which are characterized by infrequent trading) and One-Day Interbank Deposit Futures (which are the most liquid
interest rate derivative in the Brazilian market). Our results indicate that the model performs well in fitting observed yields
of both government bonds and interest rates futures contracts. Most importantly, out-of-sample errors for government
bonds are very close to those of interest rates futures contracts, which suggests that the model can be successfully used for
forecasting yield curves of sporadically traded assets.
