Quantcast
This is the best quant finance book I’ve yet read.  The symbols on the cover may look daunting, but the text actually keeps notation simple.  Many topics are covered quickly and accessibly; this is a maths book you can actually skim, or skip around in.  I think that’s due to good writing.
Also:  I stand firmly in the Robust camp.  After my class with Karen Kafadar, I’m confident that Robust models are easier to explain and more reliable.  Her typical example was to mis-type just one of the data by repeating a digit or moving the decimal place — and how likely is that! — and see how much the output changed.  Ideally your real-world recommendation shouldn’t change too much based on just one data point.  (If that’s unavoidable, you should withdraw any recommendation.)
So many mathematical questions or ideas yield up a flowering of possible tweaks and adjustments that can be made to a model, with no recommendation of which parameter value to use.  A good answer is:  whatever is most stable across different potential scenarios.
There is a wide variance among the Frank J. Fabozzi series (Advanced Stochastic Optimization, for example, is way worse than this).  If you only have time to read one, read this one.

This is the best quant finance book I’ve yet read.  The symbols on the cover may look daunting, but the text actually keeps notation simple.  Many topics are covered quickly and accessibly; this is a maths book you can actually skim, or skip around in.  I think that’s due to good writing.

Also:  I stand firmly in the Robust camp.  After my class with Karen Kafadar, I’m confident that Robust models are easier to explain and more reliable.  Her typical example was to mis-type just one of the data by repeating a digit or moving the decimal place — and how likely is that! — and see how much the output changed.  Ideally your real-world recommendation shouldn’t change too much based on just one data point.  (If that’s unavoidable, you should withdraw any recommendation.)

So many mathematical questions or ideas yield up a flowering of possible tweaks and adjustments that can be made to a model, with no recommendation of which parameter value to use.  A good answer is:  whatever is most stable across different potential scenarios.

There is a wide variance among the Frank J. Fabozzi series (Advanced Stochastic Optimization, for example, is way worse than this).  If you only have time to read one, read this one.


hi-res

36 notes

  1. isomorphismes posted this