A Comparison of Risk-Premium Forecasts Implied by Parametric Versus Nonparametric Conditional Mean Estimators
20 Pages Posted: 7 Oct 2011
Date Written: October 7, 2011
Abstract
This paper computes parametric estimates of a time-varying risk premium model and compares the one-step-ahead forecasts implied by that model with those given by a nonparametric kernel estimator of the conditional mean function. The conditioning information used for the nonparametric analysis is that implied by the theoretical model of time-varying risk. Thus, the kernel estimator is used, in conjunction with a nonparametric diagnostic test for in-sample residual nonlinear structure, to assess the adequacy of the parametric model in capturing any structure in the excess returns.
Our results support the parametric specification of an asset pricing model in which the conditional beta is the ratio of the relevant components of the conditional covariance matrix of returns modelled as a bivariate generalized ARCH process. Although the predictable component of the conditional moments is relatively small, the parametric estimator of the risk premia has somewhat more out-of-sample forecasting ability than does the kernel estimator. Hence, the superior in-sample performance of the latter may be attributed to overfitting.
Keywords: bivariate generalized ARCH, out-of-sample versus in-sample forecasting, equity premiium forecasts, conditional asset pricing model
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Modeling and Forecasting Realized Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
Modeling and Forecasting Realized Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
The Distribution of Realized Exchange Rate Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
The Distribution of Exchange Rate Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
The Distribution of Exchange Rate Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
The Distribution of Stock Return Volatility
By Torben G. Andersen, Tim Bollerslev, ...
-
By Torben G. Andersen, Tim Bollerslev, ...
-
Range-Based Estimation of Stochastic Volatility Models
By Sassan Alizadeh, Michael W. Brandt, ...
-
By Torben G. Andersen, Tim Bollerslev, ...
-
By Torben G. Andersen, Tim Bollerslev, ...