Improved beta modeling and forecasting: An unobserved component approach with conditional heteroscedastic disturbances

Recent research on time-varying systematic-risk (beta) modeling reveals significant advantages in utilizing daily financial data and unobserved-component models. This research proposes a state-space market model with conditional heteroscedastic errors, thus addressing the leptokurtosis of the unconditional distribution of the disturbances and reducing the influence of outliers in the estimation process. This approach outperforms the conventional models, providing better levels of in-sample goodness of fit and more accurate point- and interval-dynamic assets returns forecasts. The proposed model provides better levels of empirical, conditional, and unconditional coverage and independence of its interval returns forecasts and reaches lower loss-function scores. Therefore, our model allows improving financial strategies, such as stock pricing, determining the companies’ cost-of-equity, evaluating the performance of managed-investment and pension funds, making portfolio-rebalancing processes and computing the value at risk (VAR) of investment portfolios.