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This article provides an overview of the main indicators of Pakistan's stock market development and their possible relevance for real economic activity focusing on the post-liberalisation period. The aspects of the market investigated include liberalisation of the market, integration of the market with the global markets, microstructure issues of trading and settlement mechanism and corporate governance issues. A comparison with a selected set of emerging and developed markets reveals that the Pakistani stock market is small in size and a relatively insignificant source of capital mobilisation. These factors limit the role of the stock market in boosting economic activity. Also, the market seems to be excessively volatile owing to noise traders and speculators. On a positive note, the market seems to yield enormous gains to investors which compensate for higher market volatility. Also, the relative segmentation of the market makes it a potential venue for international diversification. JEL Classification: G10, G15 KeywordsStock market development, real activity Article Journal of Emerging Market Finance 11(1) 61-91

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a b s t r a c tThe 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 conditioning variables such as Trading Volume and Dividend Yield generally result in small pricing errors. However, a graphical analysis reveals that the predictions of conditional models are generally upward biased. We demonstrate that the bias in prediction may be the consequence of ignoring frequent large variation in asset returns caused by volatile institutional, political and macroeconomic conditions. This is characterised by excess kurtosis. An unconditional Fama-French model augmented with a cubic market factor performs the best among some competing models when local risk factors are employed. Moreover, the conditional models with global risk factors scaled by global conditioning variables perform better than the unconditional models with global risk factors.

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