Digging deeper into deep hedging
Quants have harnessed machine learning to hedge vanilla derivatives. But dynamic techniques and gen-AI simulated data can push the limits of deep hedging even further, as derivatives guru John Hull and colleagues explain
Traditionally, derivatives portfolios have been hedged by managing their sensitivity to changes in underlying factors such as volatility or interest rates. These sensitivities are labelled with Greek letters: delta, gamma, vega, etc. The Greeks have the advantage that they are easy to calculate and additive. (For example, if portfolio Z is the sum of portfolios X and Y, the delta of Z is the delta of X plus the delta of Y.) However, they are imperfect tools. They look at the portfolio at one
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