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  1. 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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    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 776
    Inter-library loan:
    No inter-library loan
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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
  2. 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

    Access:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 776
    Inter-library loan:
    No inter-library loan

     

    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.

     
    Export to reference management software   RIS file
      BibTeX file
    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