چکیده:
Noise is essential for the existence of a liquid market, and if noise traders are not present in the market, the trade volume will drop severely and an important aspect of the market philosophy will be lost. However, these noise traders bring noise to the market, and the existence of noise in prices indicates a temporary deviation in prices from their fundamental values. In particular, high-frequency prices carry a significant amount of noise that is not eliminated by averaging. If the level of noise in stock prices remains high for a period of time, it can be identified as a risk factor because it indicates that the deviation from fundamental values has been sustained. In this paper, after estimating the microstructure noise in high-frequency prices through a modified parametric approach, using a portfolio switching method, we compared the performance of portfolios having a high level of noise with the performance of portfolios having a lower level of noise and concluded that the risk of the high noise level presents itself as a risk premium in the future return and that asset pricing models which capture the systematic risks cannot capture the noise risk in prices.
خلاصه ماشینی:
"Microstructure noise; High frequency data; Quasi-maximum LikelihoodEstimation (QMLE); Portfolio switching JEL Classification: C13, G11, G12 Department of Financial Management, Science & Research Branch, Islamic Azad University, Tehran, Iran ; E-mail : jalal.
Therefore, based on the research purposes and questions, after estimating the market microstructure noise in prices, the main hypothesis that will be examined is "whether the high-level of noise in high-frequency price data is priced as a risk premium in stock returns and whether this return can be explained by efficient market asset pricing models".
g. , Bandi & Russell (2006) and (2008), Mancino & Sanfelici (2008), Griffin & Oomen (2011)].
The quasi-likelihood function for the vector R of observed log returns as a function of the transformed parameters (γ2, η) is given by: (رجوع شود به تصویر صفحه) After estimating the noise via high-frequency price data, we guess that if the value of noise is high in a company for a period of time, then a premium should be considered for it, and consequently, portfolios with higher noise, compared to portfolios formed by stocks having a low level of noise, have a higher return.
We investigated this hypothesis through co-integration and portfolio switching approaches, and based on our findings, we can conclude that if the average noise in the prices of a stock is high for a time period, it can be considered as a risk for the stock and it is compensated by future returns."