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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/31582
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| Title: | Dynamic Conditional Beta |
| Authors: | Engle, Robert |
| Keywords: | Dynamic Conditional Beta |
| Issue Date: | 2-Jul-2012 |
| Abstract: | In empirical finance and in time series applied economics in general,
the least squares model is the workhorse. In class there is much
discussion of the assumptions of exogeneity, homoskedasticity and serial
correlation. However in practice it may be unstable regression
coefficients that are most troubling. Rarely is there a credible
economic rationale for the assumption that the slope coefficients are
time invariant. Econometricians have developed a variety of statistical
methodologies for dealing with time series regression models with time
varying parameters. The three most common are rolling window estimates,
interaction with trends, splines or economic variables, and state space
models where the parameters are treated as a state variable to be
estimated by some version of the Kalman Filter. Each approach makes very
specific assumptions on the path of the unknown coefficients. The first
approach specifies how fast the parameters can evolve, and by using
least squares on each moving window, employs an inconsistent set of
assumptions. The second specifies a family of deterministic paths for
the coefficients that may have undesirable or inconsistent implications
particularly when extrapolated. The third requires specifying a
stochastic process for the latent vector of parameters which may include
unit roots and stochastic trends that are generally unmotivated and
rarely based on any economic analysis. There is no standardized
approach that has become widely accepted. This paper will propose such
an approach for a wide class of data generating processes. In addition,
it will allow a test of the constancy of the parameter vector. |
| URI: | http://hdl.handle.net/2451/31582 |
| Appears in Collections: | Finance Working Papers
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