Estimating, Filtering and Forecasting Realized Betas
AbstractA strategy for estimating, ?filtering and forecasting time-varying factor betas is proposed. The approach is based on the multivariate realized regression principle, an omnibus noise ?filter and an adaptive long memory forecasting model. While the multivariate realized regression approach allows for an accurate estimation of the betas also when more than a (non-orthogonal) risk factor affects stock returns, the omnibus noise ?filter and adaptive long memory forecasting model, by accounting for the time series properties of factor betas, allow for accurate estimation and forecasting.
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Bibliographic InfoPaper provided by ICER - International Centre for Economic Research in its series ICER Working Papers - Applied Mathematics Series with number 6-2007.
Length: 37 pages
Date of creation: Mar 2007
Date of revision:
realized regression; factor betas; long memory; structural change; forecasting; noise ?ltering.;
Find related papers by JEL classification:
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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