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Quantitative Finance · Glossário

O que é Vega weighting?

Definition 24.5 Derivatives and Volatility · Capítulo 24 — Fourier Pricing and Calibration Engineering

Vega weighting divides each price error by the Black vega of its quote, so that (Cmodel−Cmid)/V≈σmodel−σmid(C^{\text{model}}-C^{\text{mid}})/ \mathcal V\approx\sigma^{\text{model}}-\sigma^{\text{mid}}: a price calibration then minimises implied-volatility errors to first order, without inverting.

Exemplos

Example 24.6 (What the weights buy)

On the first day of the synthetic market (four expiries from one month to one year, five strikes each, eighteen after the filter), the Heston fit’s root-mean-square error by expiry, in volatility points, is:

weights1 month3 months6 months1 yearall
vega0.350.210.140.110.22
equal (price errors)0.460.150.120.060.24

Equal weights buy a better one-year fit with a worse one-month one. At the money a one-month option’s vega is the one-year’s divided by 12\sqrt{12}, so in price its errors count twelve times less.

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