Modern Computational Finance book

SmoothStep is monotonous and positive but its convexity changes midway between knots.
SmoothStep is well suited to risk views because it is local and interpolates a bell-shaped bump as Jherek mentioned. But I would not recommend it for the construction of the underlying yield curve, especially with a sparse set of instruments.
Cubic spline is less suited to risks because it spills out of the bracketing knots unless modified but it may be more suitable for the construction of curves. A higher order scheme as suggested by Jherek may yield even better results.
The risk view allows to dissociate construction from risk and select the more appropriate scheme for each task. For risks, a well localized scheme works best: piecewise constant, linear or smoothstep depending on differentiation requirements. Construction is a different story.
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I wanted to let the community known about the book Modern Computational Finance, which I published last month with Wiley. In order to limit repetition, I refer to my post on Medium for the story:
Modern Computational Finance: AAD and Parallel Simulations by Antoine Savine
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Antoine Savine

Hi Antoine,

I recently landed a job as a quantitative analyst. I am new to quantitative finance, mostly self taught. What would be the pre-requisites before beginning to read this book, for someone who's a newbie?