It is indeed possible to show that the Pearson correlation is essentially the way to measure linearity of association when you elect to use standard deviations to measure the dispersion of random variables.
Let's begin by noting that the Pearson correlation $\rho$ has to be considered as a (rather large) equivalence class of properties of bivariate random variables, because any invertible monotonic re-expression of it (such as its exponential) will carry identical information.
The question concerns what the term "linear relationship" might mean for random variables $(X,Y)$ that are not collinear. An important special case of this occurs when $(X,Y)$ is the empirical distribution of any bivariate dataset (which we may think of in terms of its scatterplot): are there natural ways to measure the departure of such a dataset (or scatterplot) from "linearity"?
Note that any invertible linear re-expression of the variables
$$(\xi,\eta) = (aX + b, cY + d)$$
(for constant $a,b,c,d$ where $a$ and $c$ are positive) will not change the linearity of their relationship (or lack thereof). We may therefore adopt some measure of "center" of a variable (such as its mean or median) and a measure of its "dispersion" around that center (such as its standard deviation or interquartile range) and stipulate that $a,b,c,d$ be chosen to place the centers of $\xi$ and $\eta$ at $0$ and scale their dispersions to unity.
If $(X,Y)$ are linearly related, this initial standardization will cause the support of $(\xi,\eta)$ to lie either on the line $\xi=\eta$ (for a positive relationship) or $\xi=-\eta$ (for a negative relationship). In the former case, the dispersion of $\xi-\eta$ is a natural measure of deviation from the line, while in the latter case the dispersion of $\xi+\eta$ measures deviation from the line. As a quantitative measurement of linearity, we may therefore compare one of these two quantities to the other. The larger this value is in size, the more linear is the original relationship between $X$ and $Y$.
Because two positive quantities are to be compared and a universal (unitless) measure is sought, the simplest way to make the comparison is the ratio. While the distributions of ratios tend to be skewed, the distributions of their logarithms tend not to be. Moreover, although a ratio cannot be negative, its logarithm potentially could be any real number.
Accordingly, linearity of relationship ought to be measured as the log ratio of dispersions of the sum and difference of the standardized variables.
As an example, consider using the first two moments to measure the center (the mean) and the dispersion (the standard deviation). The associated measure of linearity of $(X,Y)$ is
$$Z = \log \frac{\operatorname{SD}(\xi + \eta)}{\operatorname{SD} (\xi-\eta)} = \frac{1}{2}\log\frac{\operatorname{Var}(\xi+\eta)}{\operatorname{Var}(\xi-\eta)} = \frac{1}{2}\log \frac{2 + 2\rho}{2 - 2\rho} = \frac{1}{2}\log \frac{1 + \rho}{1 - \rho}$$
where $\rho$ is the Pearson correlation of $(X,Y)$. This expression for $Z$ is recognizable as the Fisher transformation of $\rho$, and therefore is equivalent to $\rho$ for assessing linearity. It is pleasing to see it drop out automatically from such basic principles.
This derivation has shown that the Pearson correlation coefficient is the natural way to measure the linearity of any bivariate distribution $(X,Y)$ when the first two moments are employed to evaluate central tendency and dispersion of variables.
One can go further and demonstrate that, among all the possible invertible monotonic transformations of $Z$, $\rho = \tanh(z)$ enjoys a special relationship to measures of linearity in simple ordinary least squares (OLS) regression: $\rho^2$ is identical to the coefficient of determination, $R^2$ in the regression of $Y$ against $X$ and in the regression of $X$ against $Y$. This is why $\rho$ rather than $Z$ is most often used, even in non-regression settings.
Best Answer
Yes, if you consider explaining the variance of $y_t$ by $x_{t-h}$ where $h$ is the lag order. (But not necessarily so if you consider explaining the variance of $y_t$ by $x_t$.)
Yes. It is actually quite practical because you have the lagged values earlier than you have the contemporaneous values, hence it is natural to use lagged -- rather than contemporaneous -- values for prediction.
Cross correlation is the Pearson correlation for lagged time series (when one series is lagged with respect to another.)
Also note that correlation is a natural measure for cross-sectional data where the observations can be assumed to be $i.i.d.$, but it is not that natural in the time series setting where there is time dependence between observations. For example, Pearson correlation is not very useful when applied on two series sharing a common deterministic or stochastic trend (see Spurious correlations website for some examples).