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The capital-asset-pricing model (CAPM) is one of the most popular methods of financial market analysis. But, evidence of the poor empirical performance of the CAPM has accumulated in the literature. For example, based on their empirical results regarding the relation between market Beta and average return, Fama and French (1996) conclude that the CAPM is no longer a useful tool for empirical financial market analysis. Most empirical studies of the conventional CAPM take, however, neither the fat-tails of return data nor the price relationship between an asset of interest and the bench market portfolio into account. In the framework of a univariate Beta-model we consider a stable long-run CAPM taking account of the fat-tails of stock returns and the common stochastic trends between stock prices. Using the same data used by Fama and French (1996), the stable long-run CAPM demonstrates that Markowitz rule of the expected returns and variance of returns can (still)- without any use of firm specific variables- explain the variation of the cross-sectional average returns.

Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling technique. Other improvements are also studied mostly in two directions: a pre-selection is used to optimally choose a small number of indicators from a large number of indicators. The error correction mechanism takes into account the co-integrating relationship between the key variables and factors and, hence, captures the long-run dynamics of the non-stationary macroeconomic variables. This papers proposes the factor error correction model using targeted mixedfrequency indicators, which combines the three refinements for the dynamic factor model, namely the mixed data sampling technique, pre-selection methods, and the error correction mechanism. The empirical results based on euro-area data show that the now- and forecasting performance of our new model is superior to that of the subset models.

The capital-asset-pricing model (CAPM) is one of the most popular methods of financial market analysis. But, evidence of the poor empirical performance of the CAPM has accumulated in the literature. For example, based on their empirical results regarding the relation between market Beta and average return, Fama and French (1996) conclude that the CAPM is no longer a useful tool for empirical financial market analysis. Most empirical studies of the conventional CAPM take, however, neither the fat-tails of return data nor the price relationship between an asset of interest and the bench market portfolio into account. In the framework of a univariate Beta-model we consider a stable long-run CAPM taking account of the fat-tails of stock returns and the common stochastic trends between stock prices. Using the same data used by Fama and French (1996), the stable long-run CAPM demonstrates that Markowitz rule of the expected returns and variance of returns can (still) -without any use of firm specific variables- explain the variation of the cross-sectional average returns.