In finance literature, Capital Asset Pricing Model predict only systematic risk is priced in equilibrium and neglect firm specific (idiosyncratic) risk which can be eliminated by diversification. However in real world investors, who are disable to diversify their portfolios, should take into consideration idiosyncratic risk beside of systematic risk in prediction of expected return. In this article, we examine real market conditions in Istanbul Stock Exchange (ISE), an emerging market stock exchange, over the period 2007:01 to 2010:12 by studying market wide and idiosyncratic volatility following the methodology of Campbell et al.(2001). Our findings suggest that, in 2007-2010 period, idiosyncratic volatility is the biggest component of total volatility and shows no trend in this period. Beside that our analyses about the predictive ability of various measures of idiosyncratic risk provide evidence that idiosyncratic volatility is not a significant predictor for future return. ARTICLE INFO

The influence of the behavior and strategies of traders on stock price formation has attracted much interest. It is assumed that there is a positive correlation between the total net demand and the price change. A buy order is expected to increase the price, whereas a sell order is assumed to decrease it. We perform data analysis based on a recently proposed stochastic model for stock prices. The model involves long‐range dependence, self‐similarity, and no arbitrage principle, as observed in real data. The arrival times of orders, their quantity, and their duration are created by a Poisson random measure. The aggregation of the effect of all orders based on these parameters yields the log‐price process. By scaling the parameters, a fractional Brownian motion or a stable Levy process can be obtained in the limit. In this paper, our aim is twofold; first, to devise statistical methodology to estimate the model parameters with an application on high‐frequency price data, and second, to validate the model by simulations with the estimated parameters. We find that the statistical properties of agent level behavior are reflected on the stock price, and can affect the entire process. Moreover, the price model is suitable for prediction through simulations when the parameters are estimated from real data. The methods developed in the present paper can be applied to frequently traded stocks in general. Copyright © 2013 John Wiley & Sons, Ltd.