Calibration in the real world of a partially specified stochastic volatility model

We study the real-world calibration of a partially specified stochastic volatility model, where the analytic expressions of the asset price drift rate and of the stochastic variance drift are not specified. The model is calibrated matching the observed asset log returns and the priors assigned by the investor. No option price data are used in the calibration. The priors chosen for the asset price drift rate and for the stochastic variance drift are those suggested by the Heston model. For this reason, the model presented can be considered as an enhanced Heston model. The calibration problem is formulated as a stochastic optimal control problem and solved using the dynamic programming principle. The model presented and the Heston model are calibrated using synthetic and Standard & Poor 500 (S&P500) data. The calibrated models are used to produce 6, 12, and 24 months in the future synthetic and S&P500 forecasts.

Automated summary

This paper studies the real-world calibration of a partially specified stochastic volatility model. The calibration is based on matching the observed asset log returns and the priors assigned by the investor, without using option price data. The model presented in this paper can be considered as an enhanced Heston model, with the calibration problem formulated as a stochastic optimal control problem.