manner. Such an approach is made available through application of the Kalman filter with a time varying parameter specification. An example of a time varying parameter model using the Kalman filter can be found in Kim and Nelson (1989).
Our interest here is to develop a robust dynamic trading model for economic sectors using factors identified as significant in the preceding literature. Further, the model we develop assumes time-variation in factor sensitivities to capture changing risk premia over time. Time variation in factor betas is approached using dynamic updating in the Kalman filter. The result is a highly responsive model that significantly outperforms comparable static and rolling parameter specifications. Employing this methodology, we would like a model that is particularly prescient
at business cycle turning points.
Such a model may provide an important hedge against
model developed here also appears with the benchmark CAPM.
The balance of this paper is organized as follows: In Section 2 a time varying parameter factor model (TVPFM) using lagged economic factors and industry sectors as portfolios is motivated and developed. Section 3 describes the full Bayesian estimation and model selection criteria employed for evaluating the model. Some preliminary indications from the model output are also discussed. Section 4 investigates the behavior of out of sample risk premia on the predicted model sector returns. In Section 5, the potential profitability of a simple trading strategy using the predicted returns of the TVPFM is investigated and discussed. Section 6 concludes.
A Time Varying Parameter Factor Model
General Model Specification
We begin with a time series factor model of equity returns. The factors are assumed to be lagged fundamental macroeconomic variables. The return generating process for each portfolio i is expressed as
ri , t 1
1 t t f