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A review of vast literature base on Asset Pricing testing in advanced and emerging markets suggests that today no consensus has been reached on what is the right approach for dealing with the emerging market’s specifics such as thin trading, market concentration and high volatility. This paper will consider a class of linear factor models, which are particularly famous due to the acknowledgement of Capital Asset Pricing Model (CAPM) and its subsequent modifications, which are the ultimate topic of this paper. Despite numerous research papers criticising traditional linear models and attempting to alter their embedded limitations, practitioners as well as academics return to already existing models such as the CAPM repeatedly. Anderson, Bollerslev, Diebold and Wu (2006), argue that the death of the model was over exaggerated. Firstly, because the model often works well despite its wrinkles and secondly more advanced multi-factor models that offer better statistical fit, lack the economic explanation of the variables and their interpretation in terms of systematic risk.
This opens a floor for discussion about the choice of asset pricing models, specifically should traditional CAPM model be applied or should alternative models such as D-CAPM be preferred. This paper offers statistical testing of traditional CAPM, Fama French CAPM and D-CAPM on a set of indices and portfolios with the use of GMM two-step simple and multiple regressions. Results has shown that on average downside beta tends to perform better in both emerging and developed markets than traditional beta. However, caution should be given to the use of systematic risk measures, as in case of emerging markets total risk measures such as semivariance and standard deviation can be preferable. Overall, unconditional models should not be a centre of discussion as many research papers along with this master thesis has shown that beta is non-constant over time, which confirms a general finding of non-constant volatility. The last chapter of this paper therefore looks at key conditional models.