# How to prepare data for calibration

I want to calibrate different models by minimizing RMSE. When I use data from Schoutens (2003) everything is OK i.e I get reasonable parameters. The problem appears when I try to calibrate models to the data from yahoo finance. As the $$S_0$$ I take adjusted Close price and the option price is the average of the bid and ask prices. Out of over $$1000$$ options, I choose about $$100$$ options with different expiry times and strike prices. Ufortunately, when I calbrate different model I get very strange values. For example in BS model $$\sigma=0.47$$ (with $$\sigma=0.18$$ for Schoutens data) and in Kou model $$\sigma$$ is the same as in BS model and $$\lambda=0$$ so I get the same result as for BS model (and for Schoutens: $$\sigma=0.136, \lambda=0.104, p=0.621, \eta_1=48.778, \eta_2=0.046$$ and much better RMSE than for BS model). In Merton model I get very good fit but $$\lambda$$ is $$20$$ so very unrealistic. Where is the problem? How to choose data to get normal parameters?

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