Term Structure Estimation in Low-Frequency Transaction Markets: A Kalman Filter Approach with Incomplete Panel-Data

38 Pages Posted: 26 Jul 2004

See all articles by Gonzalo Cortazar

Gonzalo Cortazar

Pontificia Universidad Catolica de Chile

Eduardo S. Schwartz

University of California, Los Angeles (UCLA) - Finance Area; Simon Fraser University (SFU); National Bureau of Economic Research (NBER)

Lorenzo Naranjo

Washington University in St. Louis - John M. Olin Business School

Date Written: March 2004

Abstract

There are two issues that are of central importance in term structure analysis. One is the modeling and estimation of the current term structure of spot rates. The second is the modeling and estimation of the dynamics of the term structure. These two issues have been addressed independently in the literature. The methods that have been proposed assume a sufficiently complete price data set and are generally implemented separately. However, when the methods are applied to markets with sparse bond price, results are unsatisfactory.

We develop a method for jointly estimating the current term structure and its dynamics for markets with low-frequency transactions. We propose solving both issues by using a dynamic term structure model estimated from incomplete panel data. To achieve this, we modify the standard Kalman filter approach to deal with the missing-observation problem. In this way, we can use historic price data in a dynamic model to estimate the current term structure. With this approach we are able to obtain an estimate of the current term structure even for days with an arbitrary low number of price observations.

The proposed methodology can be applied to a broad class of continuous-time term-structure models with any number of stochastic factors. To show the implementation of the approach, we estimate a three-factor generalized-Vasicek model using Chilean government bond price data. The approach, however, may be used in any market with low-frequency transactions, a common characteristic of many emerging markets.

Suggested Citation

Cortazar, Gonzalo and Schwartz, Eduardo S. and Schwartz, Eduardo S. and Naranjo, Lorenzo, Term Structure Estimation in Low-Frequency Transaction Markets: A Kalman Filter Approach with Incomplete Panel-Data (March 2004). Available at SSRN: https://ssrn.com/abstract=567090 or http://dx.doi.org/10.2139/ssrn.567090

Gonzalo Cortazar (Contact Author)

Pontificia Universidad Catolica de Chile ( email )

Departamento Ingenieria Industrial y de Sistemas
Av. Vicuna Mackenna 4860
Santiago
Chile

Eduardo S. Schwartz

University of California, Los Angeles (UCLA) - Finance Area ( email )

Los Angeles, CA 90095-1481
United States
310-825-1953 (Phone)
310-206-5455 (Fax)

Simon Fraser University (SFU) ( email )

8888 University Drive
Burnaby, British Columbia V5A 1S6
Canada

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Lorenzo Naranjo

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
664
Abstract Views
3,499
Rank
72,927
PlumX Metrics