Estimating option greeks under the stochastic volatility using simulation

Shafi, Khuram; Latif, Natasha; Shad, Shafqat Ali; Idrees, Zahra; Gulzar, Shazia

doi:10.1016/j.physa.2018.08.032

Cited by 4 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…LFI methods are able to infer the states and avoid learning g θ by using a simulator as the observation model. Simulators are widespread in SSM settings (Ghassemi et al 2017;Shafi et al 2018;Georgiou and Demiris 2017) since they enable the incorporation of additional prior knowledge about data-generating mechanisms without the need for a tractable likelihood p(x t | θ t ). In this paper, we focus on LFI for SSMs, which fall under the category of approximate methods in the broader context of SSM inference.…”

Section: Introductionmentioning

confidence: 99%

Likelihood-free inference in state-space models with unknown dynamics

Aushev,

Tran,

Pesonen

et al. 2023

Stat Comput

View full text Add to dashboard Cite

Likelihood-free inference (LFI) has been successfully applied to state-space models, where the likelihood of observations is not available but synthetic observations generated by a black-box simulator can be used for inference instead. However, much of the research up to now has been restricted to cases in which a model of state transition dynamics can be formulated in advance and the simulation budget is unrestricted. These methods fail to address the problem of state inference when simulations are computationally expensive and the Markovian state transition dynamics are undefined. The approach proposed in this manuscript enables LFI of states with a limited number of simulations by estimating the transition dynamics and using state predictions as proposals for simulations. In the experiments with non-stationary user models, the proposed method demonstrates significant improvement in accuracy for both state inference and prediction, where a multi-output Gaussian process is used for LFI of states and a Bayesian neural network as a surrogate model of transition dynamics.

show abstract

Section: Introductionmentioning

confidence: 99%

Likelihood-free inference in state-space models with unknown dynamics

Aushev,

Tran,

Pesonen

et al. 2023

Stat Comput

View full text Add to dashboard Cite

show abstract

“…Hull and White in [4] first introduced an SV model called Heston model in which the volatility of the market follows a mean-reverting Cox-Ingersoll-Ross process. The theoretical development of the SV model was introduced in [5] by studying the following equations…”

Section: Introductionmentioning

confidence: 99%