Testing conditional asset pricing models: an emerging market perspective

The CAPM as the benchmark asset pricing model generally performs poorly in both developed and emerging markets. We investigate whether allowing the model parameters to vary improves the performance of the CAPM and the Fama-French model. Conditional asset pricing models scaled by conditional variables such as Trading Volume and Dividend Yield generally result in small pricing errors. However, a graphical analysis shows that the predictions of conditional models are generally upward biased. We demonstrate that the bias in prediction may be caused by not accommodating frequent large variation in asset pricing models. In emerging markets, volatile institutional, political and macroeconomic conditions results in thick tails in the return distribution. This is characterized by excess kurtosis. It is found that the unconditional Fama-French model augmented with a cubic market factor performs the best among the competing models. This model is also more parsimonious compared to the conditional Fama-French model in terms of number of parameters.