Contents
How does cointegration work in a time series?
Cointegration allows us to construct a stationary time series from two asset price series, if only we can find the magic weight, or more formally, the cointegration coefficient. Then we can apply a mean-reversion strategy to trade both assets at the same time weighted by.
What do you need to know about cointegration?
Cointegration describes a long-term relationship between asset prices. Cointegration can be seen as a measure of similarity of assets in terms of risk exposure profiles. The prices of cointegrated assets are tethered due to the stationarity of their spread. Correlation and cointegration are two different concepts.
When to use auto regressive distributed lags in cointegration?
If the sample size is too small then the results will not be reliable and one should use Auto Regressive Distributed Lags (ARDL).
Which is a good example of a cointegrated pair?
When two assets are cointegrated, the underpinning factors that made their price non-stationary should be similar; or in financial terms, the two assets should have similar risk exposure so that their prices move together. For example, good candidates for cointegrated pairs could be:
How do I interpret regression model when some variables are log transformed?
In the log scale, it is the difference in the expected geometric means of the log of write between the female students and male students. In the original scale of the variable write, it is the ratio of the geometric mean of write for female students over the geometric mean of write for male students, exp ( .1032614) = 54.34383 / 49.01222 = 1.11.
How is a regression coefficient related to log of Y?
Since this is just an ordinary least squares regression, we can easily interpret a regression coefficient, say β 1, as the expected change in log of y with respect to a one-unit increase in x 1 holding all other variables at any fixed value, assuming that x 1 enters the model only as a main effect.
What makes a series cointegrate in a stochastic trend?
If they are stationary the series cointegrate! What is crucial for cointegration is that the series share a common stochastic trend and that they are at least integrated of order 1.
What are the differences in the cointegration equation?
The differences (or term error) in the cointegration equation are interpreted as the unbalance error for each particular point in time. More formally: two non-stationary series are cointegrated if there is a stationary linear combination of these series. Stationary series are those in which the variance is constant over time.
When are two non stationary series cointegrated?
More formally: two non-stationary series are cointegrated if there is a stationary linear combination of these series. Stationary series are those in which the variance is constant over time. The Dickey-Fuller test allows us to distinguish whether a series is stationary or not.