Publication: Nonparametric estimation of conditional beta pricing models
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2008-05
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Abstract
We propose a new procedure to estimate and test conditional beta pricing models which allows for
flexibility in the dynamics of assets' covariances with risk factors and market prices of risk (MPR). The
method can be seen as a nonparametric version of the two-pass approach commonly employed in the
context of unconditional models. In the first stage, conditional covariances are estimated
nonparametrically for each asset and period using the time-series of previous data. In the second stage,
time-varying MPR are estimated from the cross-section of returns and covariances, using the entire
sample and allowing for heteroscedastic and cross-sectionally correlated errors. We prove the desirable
properties of consistency and asymptotic normality of the estimators. Finally, an empirical application to
the term structure of interest rates illustrates the method and highlights several drawbacks of existing
parametric models.
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Kernel estimation, Locally stationary processes, Time-varying coefficients, Conditional asset pricing models