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I would recommend using the Johansen-Procedure for determining the cointegration vector, the ca.jo-function from library(urca). After determining the cointegration rank, a normalized cointegration vector is produced by estimating a restricted VECM with the command cajorls().


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More measurable effects to add to your list: "window dressing" - returns of the fourth quarter or 12th month (i.e. year-end) are higher on average than oher returns; the same to returns of 4th months (qtr-end) vs. others; "herding": changes in asset-classes shares of "big" funds (whatever you define "big") granger-cause changes in asset-classes shares of ...



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