  In a recent paper, we have demonstrated how the affinity between TPUs and
multi-dimensional financial simulation resulted in fast Monte Carlo simulations
that could be setup in a few lines of python Tensorflow code. We also presented
a major benefit from writing high performance simulations in an automated
differentiation language such as Tensorflow: a single line of code enabled us
to estimate sensitivities, i.e. the rate of change in price of financial
instrument with respect to another input such as the interest rate, the current
price of the underlying, or volatility. Such sensitivities (otherwise known as
the famous financial "Greeks") are fundamental for risk assessment and risk
mitigation. In the present follow-up short paper, we extend the developments
exposed in our previous work about the use of Tensor Processing Units and
Tensorflow for TPUs.
