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Estimating, Filtering and Forecasting Realized Betas

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  • Claudio Morana

Abstract

A 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.

Suggested Citation

  • Claudio Morana, 2007. "Estimating, Filtering and Forecasting Realized Betas," ICER Working Papers - Applied Mathematics Series 6-2007, ICER - International Centre for Economic Research.
    • Handle: RePEc:icr:wpmath:6-2007
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    File URL: http://www.bemservizi.unito.it/repec/icr/wp2007/ICERwp6-07.pdf
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    References listed on IDEAS

    as
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    1. Hollstein, Fabian & Prokopczuk, Marcel, 2022. "Testing Factor Models in the Cross-Section," Journal of Banking & Finance, Elsevier, vol. 145(C).

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    More about this item

    Keywords

    realized regression; factor betas; long memory; structural change; forecasting; noise ?ltering.;
    All these keywords.

    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; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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