An adaptive sequential Monte Carlo method for approximate Bayesian computation

Moral, Pierre Del; Doucet, Arnaud; Jasra, Ajay

doi:10.1007/s11222-011-9271-y

Cited by 432 publications

(523 citation statements)

References 25 publications

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“…We also filter the observable variable log RV5, using our procedure and the HAR-RV-J method, using as conditioning variables those of equation 25. This serves as a check on the reliability of the parametric model that is used to generate the long simulation which underlies the filtering and smoothing results, as the HAR-RV-J method does not rely on the parametric model.…”

Section: Monte Carlo Results For Filtering and Smoothingmentioning

confidence: 99%

ABC of SV: Limited information likelihood inference in stochastic volatility jump-diffusion models

Creel

Kristensen

2015

Journal of Empirical Finance

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We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative to standard likelihood-based versions since they rely on low-dimensional auxiliary statistics and so avoid computation of high-dimensional integrals. Despite their computational simplicity, we find that estimators and filters perform well in practice and lead to precise estimates of model parameters and latent variables. We show how the methods can incorporate intra-daily information to improve on the estimation and filtering. In particular, the availability of realized volatility measures help us in learning about parameters and latent states. The method is employed in the estimation of a flexible stochastic volatility model for the dynamics of the S&P 500 equity index. We find evidence of the presence of a dynamic jump rate and in favor of a structural break in parameters at the time of the recent financial crisis. We find evidence that possible measurement error in log price is small and has little effect on parameter estimates. Smoothing shows that, recently, volatility and the jump rate have returned to the low levels of 2004-2006.

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Section: Monte Carlo Results For Filtering and Smoothingmentioning

confidence: 99%

ABC of SV: Limited information likelihood inference in stochastic volatility jump-diffusion models

Creel

Kristensen

2015

Journal of Empirical Finance

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“…t represents the importance of the associated sample x (i) t [23]. The samples x (i) t at time t are generated from the importance distribution η t , which is constructed by a Markov transition kernel K and the previous distribution π t−1 [24]; each sample x (i) t evolves from x (i) t−1 according to the kernel K, and the importance distribution η t is represented by…”

Section: Sequential Monte Carlo For Graph Matchingmentioning

confidence: 99%

Graph Matching via Sequential Monte Carlo

Suh

Cho

Lee

2012

Computer Vision – ECCV 2012

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Abstract. Graph matching is a powerful tool for computer vision and machine learning. In this paper, a novel approach to graph matching is developed based on the sequential Monte Carlo framework. By constructing a sequence of intermediate target distributions, the proposed algorithm sequentially performs a sampling and importance resampling to maximize the graph matching objective. Through the sequential sampling procedure, the algorithm effectively collects potential matches under one-to-one matching constraints to avoid the adverse effect of outliers and deformation. Experimental evaluations on synthetic graphs and real images demonstrate its higher robustness to deformation and outliers.

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“…We therefore refer to this as the ABC-PMC algorithm. There is also a wider family of related ABC-SMC algorithms, including Sisson et al (2007) and Del Moral et al (2012), which update their particles in more complex ways based on MCMC moves, as described in Del Moral et al (2006). (c) Simulate dataset D * from the model using parameters θ * .…”

Section: Abc-pmcmentioning

confidence: 99%