In pricing financial derivatives, we often first assume that the volatility of the stock price is constant.
$$\mathrm{d}S(t) = \alpha S(t) \mathrm{d}t + \sigma S(t) \mathrm{d}W(t)\text{.}$$
The volatility itself, $\sigma$, may be modelled as a random process, however, like in the Heston Model:
$$\mathrm{d}S(t) = \alpha S(t) \mathrm{d}t + \sqrt{\upsilon(t)} S(t) \mathrm{d}W_1(t)$$
$$\mathrm{d}\upsilon(t) = \kappa (\theta(t)-\upsilon(t)) \mathrm{d}t + \xi \sqrt{\upsilon(t)} \mathrm{d}W_2(t)\text{.}$$
Other models that include stochastic volatility can be found here.
We could keep going, however, and treat $\xi$ in the above as a random process. This might be thought of as "stochastic volatility of the volatility."
$$\mathrm{d}S(t) = \alpha S(t) \mathrm{d}t + \sqrt{\upsilon(t)} S(t) \mathrm{d}W_1(t)$$
$$\mathrm{d}\upsilon(t) = \kappa (\theta(t)-\upsilon(t)) \mathrm{d}t + \sqrt{\xi(t)} \sqrt{\upsilon(t)} \mathrm{d}W_2(t)\text{.}$$
$$\mathrm{d}\xi(t) = \ldots$$
This process could be repeated infinitely, but I am mostly concerned with whether or not this third step is even a good idea. Are there (reasonable) models that allow the volatility of the volatility to itself be a random process? Has anyone investigated a model like this before?