• Stochastic Volatility Python

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Stochastic Volatility Python

(Submitted on 17 Mar 2017) Abstract: In this paper, we relax the power parameter of instantaneous variance anddevelop a new stochastic volatility plus jumps model that generalize the Hestonmodel and 3/2 model as special cases. This model has two distinctive features.First, we do not restrict the new parameter, letting the data speak as to itsdirection. The Generalized Methods of Moments suggests that the newly addedparameter is to create varying volatility fluctuation in different perioddiscovered in financial market. Moreover, upward and downward jumps areseparately modeled to accommodate the market data. Our model is novel andhighly tractable, which means that the quasi-closed-form solutions for futureand option prices can be effectively derived. We have employed data on VIXfuture and corresponding option contracts to test this model to evaluate itsability of performing pricing and capturing features of the implied volatility.To sum up, the free stochastic volatility model with asymmetric jumps is ableto adequately capture implied volatility dynamics and thus it can be seen as asuperior model relative to the fixed volatility model in pricing VIXderivatives.