Assumptions Behind FVE Model

My FVE model calculates "fair value" of VIX based on several assumptions:
1) After 2008 market crash, realized volatility leads implied volatility (estimated 70% of time)
2) Derivatives market are zero-sum games, thus you would often see forced liquidation of losing bets (sometimes large), adding to more frequent over/under valued situations
3) Volatility, although mean-reverting, exhibits strong trends.
3a) Many market makers do not utilize an "absolute value' model but rather "relative value", or they piggy back off of whatever bid/ask prices are, thus would add fuel to volatility moving from one equilibrium to another.
4) Implied Volatility is a reflexive function of realized volatility of the underlying + statistical relationships on supply & demand of options (as reflected in implied volatility or VIX) based on characteristics movement of the underlying.
4a) VIX in general is inversely correlated to S&P500 index direction
4b) The more and faster investors are losing money, the more they would seek out protection
4c) I believe whether the underlying is in a trend or a range affects supply and demand for options.

FVE indicator and simple trading rules takes into account these assumptions in a crude (yet seemingly very effective and relatively simple) model.  I am not a programmer or a mathematician, so anyone with knowledge and skills in these areas could probably come up with a superior volatility (VIX) model.  I hope my contribution would be my insight into the market volatility.
I do believe in a top-down (qualitative-->quantitative) approach, where one would detail assumptions about a market (current environment), create and apply effective algorithms, then test the algorithms in their effectiveness. I guess the other approach would be bottom-up through data mining.

I would love to learn about assumptions made in other models.  Thanks.