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  1. Subsampling Sequential Monte Carlo for static Bayesian models
    Author: Gunawan, David; Dang, Khue-dung; Quiroz, Matias; Kohn, Robert; Minh-Ngoc Tran
    Published: April 2019
    Publisher:  Sveriges Riksbank, Stockholm

    We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to... more

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    We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distribution. Each update of the particle cloud consists of three steps: reweighting, resampling, and moving. In the move step, each particle is moved using a Markov kernel and this is typically the most computationally expensive part, particularly when the dataset is large. It is crucial to have an effcient move step to ensure particle diversity. Our article makes two important contributions. First, in order to speed up the SMC computation, we use an approximately unbiased and effcient annealed likelihood estimator based on data subsampling. The subsampling approach is more memory efficient than the corresponding full data SMC, which is an advantage for parallel computation. Second, we use a Metropolis within Gibbs kernel with two conditional updates. A Hamiltonian Monte Carlo update makes distant moves for the model parameters, and a block pseudo-marginal proposal is used for the particles corresponding to the auxiliary variables for the data subsampling. We demonstrate the usefulness of the methodology for estimating three generalized linear models and a generalized additive model with large datasets.

     
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    Volltext (kostenfrei)
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/215449
    Series: Sveriges Riksbank working paper series ; 371
    Scope: 1 Online-Ressource (circa 39 Seiten), Illustrationen
  2. Hamiltonian Monte Carlo with energy conserving subsampling
    Author: Dang, Khue-dung; Quiroz, Matias; Kohn, Robert; Minh-Ngoc Tran; Villani, Mattias
    Published: April 2019
    Publisher:  Sveriges Riksbank, Stockholm

    Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the Hamiltonian dynamics. The iterations make HMC computationally costly,... more

    Access:
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    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 204
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    Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the Hamiltonian dynamics. The iterations make HMC computationally costly, especially in problems with large datasets, since it is necessary to compute posterior densities and their derivatives with respect to the parameters. Naively computing the Hamiltonian dynamics on a subset of the data causes HMC to lose its key ability to generate distant parameter proposals with high acceptance probability. The key insight in our article is that efficient subsampling HMC for the parameters is possible if both the dynamics and the acceptance probability are computed from the same data subsample in each complete HMC iteration. We show that this is possible to do in a principled way in a HMC-within-Gibbs framework where the subsample is updated using a pseudo marginal MH step and the parameters are then updated using an HMC step, based on the current subsample. We show that our subsampling methods are fast and compare favorably to two popular sampling algorithms that utilize gradient estimates from data subsampling. We also explore the current limitations of subsampling HMC algorithms by varying the quality of the variance reducing control variates used in the estimators of the posterior density and its gradients.

     
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    Content information
    Volltext (kostenfrei)
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/215450
    Series: Sveriges Riksbank working paper series ; 372
    Scope: 1 Online-Ressource (circa 47 Seiten), Illustrationen
  3. A dynamic leverage stochastic volatility model
    Author: Nguyen, Hoang; Trong-Nghia Nguyen; Minh-Ngoc Tran
    Published: [2021]
    Publisher:  Örebro University School of Business, Örebro, Sweden

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/244588
    Series: Array ; 2021, 14
    Subjects: Dynamic leverage; GAS; stochastic volatility (SV)
    Scope: 1 Online-Ressource (circa 15 Seiten), Illustrationen
  4. Bayesian predictive distributions of oil returns using mixed data sampling volatility models
    Author: Virbickaite, Audrone; Nguyen, Hoang; Minh-Ngoc Tran
    Published: [2023]
    Publisher:  Örebro University School of Business, Örebro, Sweden

    This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as... more

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    This study explores the benefits of incorporating fat-tailed innovations, asymmetric volatility response, and an extended information set into crude oil return modeling and forecasting. To this end, we utilize standard volatility models such as Generalized Autoregressive Conditional Heteroskedastic (GARCH), Generalized Autoregressive Score (GAS), and Stochastic Volatility (SV), along with Mixed Data Sampling (MIDAS) regressions, which enable us to incorporate the impacts of relevant financial/macroeconomic news into asset price movements. For inference and prediction, we employ an innovative Bayesian estimation approach called the density-tempered sequential Monte Carlo method. Our findings indicate that the inclusion of exogenous variables is beneficial for GARCH-type models while offering only a marginal improvement for GAS and SV-type models. Notably, GAS-family models exhibit superior performance in terms of in-sample fit, out-of-sample forecast accuracy, as well as Value-at-Risk and Expected Shortfall prediction.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/274606
    Series: Array ; 2023, 7
    Subjects: ES; GARCH; GAS; log marginal likelihood; MIDAS; SV; VaR
    Scope: 1 Online-Ressource (circa 35 Seiten), Illustrationen