medium · Quantitative Finance numerical
Which of the following describes the 'curse of dimensionality' as it relates to finite difference methods for pricing derivatives?
- Numerical stability is lost whenever the time step chosen is too small.
- The error of the resulting price estimate shrinks only as 1/sqrt(M), slowly.
- The finite difference method is fundamentally unable to price early-exercise features.
- Computational cost grows exponentially with the number of underlying factors.
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