Prediction of systematic risk: "a case from Turkey"
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/17447
This stjid}^ sugpjosts Bayesian and time-varying models to adjust for the regression tc'ndeiK'y of lietas [iresent in standard asset i)ricing applications. Beta, adjustment techniciues are a])])li('d to the Istcinl.^ul Stock Exchange da.ta. Empirical findings show tlia.t MSE (Mean Square Error) are lowest among all models used in tlie study when log-linear or sciuare-root linear Blume modcds are used and lietas predicted according to Bayesian models have lower MSl·^ tlian unadjusted Ivetas. Also, it is oliserved tha,t inediciency ])art of tlie MSE changes most when various adjustment teclmiques are uschL