As regards the causes of heteroskedasticity in an econometrics setting:
In a regression setting and in econometrics, a fundamental distinction must be made between "conditional heteroskedasticity" (meaning, being a function of the regressors), and unconditional heteroskedasticity (meaning not being a function of the regressors).
We are talking about a property of the "error" term. While this is a terminology linked to the view of a regression relation as an approximation, in econometrics this is not so. In econometrics, regressions represent causal relationships between variables, where causality is argued from theory, past experience and logic (when the causal relations reflected in a specific regression are incomplete, then we have issues as to whether we can actually estimate the causal relationship accurately, but that is another matter).
Given this, the "error" term represents *all other factors that affect the dependent variable". And these "other factors" rarely are some "out of nowhere" "random shocks", some lottery to make life more interesting. And so here a "proportionality argument" kicks in:
Suppose that we examine a sample of production data of firms in the same industry. We expect to see firms of many different "sizes" (measured by output level, inputs levels). Say, firm $k$ is 10 times larger than firm $j$ in this sample. Is it reasonable to assume that the "error" related to firm $k$ has the same variance as the "error" related to the $j$ variable? Namely that it has the same "usual variation around its mean"?
If this was the case, then "all other factors" would affect disproportionately more the output of the small $j$ firm than the output of the big $k$ firm. This does not seem a very realistic assumption to make. Rather we anticipate that with each "size" goes an analogous "strength" of "all other factors" that affect the dependent variable. (In my language a proverb exists roughly translated as: "Big ship, big storms"). Big firms exert much more influence on their socioeconomic environment and this environment takes notice and attempts to influence back the big firms.
To mend this, the concept of "heteroskedasticity conditional on the regressors", was born. The higher the level of the regressors (say, production inputs), the higher the variance of the "error", to provide a degree of proportionality between the two groups of forces (regressors and "error") that determine the dependent variable.
In light of the above, what interpretation could we give to, and what usage could we have for unconditional heteroskedasticity (meaning, heteroskedasticity independent of the regressors)? Well, it is a convenient way to model a "summary" heteroskedasticity situation. Essentially, it is not trully "unconditional" in general, just independent of the regressors. In econometrics, such unconditional heteroskedasticity is essentially behind the concepts of "group-wise heteroskedasticity" which has evolved into what is nowadays called "clustered standard errors". Here, we assume heteroskedasticity not observation-per-observation, but between groups of observations (clusters), where group-membership is usually determined by a single variable which often is not part of the theoretically postulated regressors, but on some other aspect of the entities that are sampled (and so it would be part of "all other factors affecting the dependent variable").
See also, https://stats.stackexchange.com/a/499049/28746