Python for Finance: Analyze Big Financial Data

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options (e.g., on a volatility index). The model we use is the one of Gruenbichler and


Longstaff (1996). They model the volatility process (e.g., the process of a volatility index)


in direct fashion by a square-root diffusion, provided in Equation 14-1. This process is


known to exhibit convenient features for volatility modeling, like positivity and mean


reversion.


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Equation 14-1. Square-root diffusion for volatility modeling


The variables and parameters in Equation 14-1 have the following meanings:


Vt


The time t value of the volatility index (for example, the VSTOXX)


θV


The long-run mean of the volatility index


κV


The rate at which Vt reverts to


ΣV


The volatility of the volatility (“vol-vol”)


θV, κV, and ΣV


Assumed to be constant and positive


Zt


A standard Brownian motion


Based on this model, Gruenbichler and Longstaff (1996) derive the formula provided in


Equation 14-2 for the value of a European call option. In the formula, D(T) is the


appropriate discount factor. The parameter denotes the expected premium for volatility


risk, while is the complementary noncentral


2

distribution.


Equation 14-2. Call option formula of Gruenbichler and Longstaff (1996)

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