Volatility clustering, long-range dependence, non-Gaussianity and anomalous scaling are all well-known stylized facts of financial assets return dynamics. These elements  have a relevant impact on the aptness of models for the pricing of options written on financial assets. We make us of a model developed in physics that captures the previously cited returns features . The model allows deriving closed form equations for option pricing . We present the model providing a financial interpretation of its components and discuss the parameters estimation. We then derive pricing equations and use them in an empirical application based on  a major equity index option dataset  .
Volatility clustering, long-range dependence, and non-Gaussian scaling are stylized facts of financial assets dynamics. They are ignored in the Black Scholes framework, but have a relevant impact on the pricing of options written on financial assets. Using a recent model for market dynamics which adequately captures the above stylized facts, we derive closed form equations for option pricing , obtaining the Black Scholes as a special case. By applying our pricing equations to a major equity index option dataset , we show that inclusion of stylized features in financial modeling moves derivative prices about 30\% closer to the market values without the need of calibrating models parameters on available derivative prices .
R
non-Gaussianity and anomalous scaling are all well-known
and non-Gaussian scaling are
R
return dynamics. These elements
dynamics. They are ignored in the Black
A
Scholes framework, but
D
aptness of models for the
R
We make us of a model developed in physics that captures the previously cited returns features . The model allows deriving
Using a recent model for market dynamics which adequately captures the above stylized facts, we derive
R
. We present the model providing a financial interpretation of its components and discuss the parameters estimation. We then derive pricing equations and use them in an empirical application based on
, obtaining the Black
A
Scholes as a special case. By applying our pricing equations to
A
, we show that inclusion of stylized features in financial modeling moves derivative prices about 30\% closer to the market values without the need of calibrating models parameters on available derivative prices