Defining asset classes from a quantitative perspective is an interesting question that is not really addressed "officially" as far as I know.
Let's try to write some requirements
you want strategic decisions to make sense: each asset class should have at least one or two different "economic drivers" than the others
you want tactical ...
From the documentation of matplotlib.finance (under parse_yahoo_historical_ochl(...)) it is specified that the dollars traded/dollar-volume is the unadjusted volume multiplied by the adjusted closing prices of the given ticker (At quotes_yahoo_historical_ochl(...) they refer to the above function in order to understand the output format):
adjusted : bool
A quant technique that could be used to (partially) address this problem is the Mean
Variance Spanning Test of Huberman and Kandel (1987). Abstract
This is a statistical test of whether adding K new assets to an existing set of N assets improves the Efficient Frontier or not. Roughly speaking the test involves checking whether the new assets "add ...
Price overshooting doesn’t necessarily imply that the price is too high. It implies that if the price changes from one level to another level, the price first moves past the final level and then back to the final level.
"How to roughly measure how much premium investors would demand if a stock could not be sold and its investors had to stick with it permanently using just dividends not capital gain as return?"
A starting point might be to look at the private/public valuation arbitrage in the sector.
As stated by user42108 in his comment, there are many different contracts for a given commodity. As an example for the energy market, you can have monthly/quarterly or even daily futures (for instance, power at EEX).
Thus a product with a daily delivery can be liquid only nearly the contract expires.
Therefore, you can define the liquidity using the average ...
The usual measures are trade volumes (from all sources of trades / order books), bid/offer spread, order book depth, quote ages, trade frequency, etc. For quick comparisons, average daily volume is the best (and easy to obtain).
I will attempt to elaborate on this from risk management perspective.
scenario analysis approach: An example of this is stress testing that Fed mandates for investment banks. Fed gives stress variables to various fundamental macro variables. For example, a certain market stress scenario will be rates down 100bps, volatility up 30%, curve flatter by 30bps, ...