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  • 1. Karlsson, Sune
    et al.
    Mazur, Stepan
    Nguyen, Hoang
    Linköping University, Department of Management and Engineering, Production Economics.
    Vector autoregression models with skewness and heavy tails2023In: Journal of Economic Dynamics and Control, Vol. 146Article in journal (Refereed)
  • 2. Kiss, T.
    et al.
    Mazur, S.
    Nguyen, Hoang
    Linköping University, Department of Management and Engineering, Production Economics.
    Predicting returns and dividend growth ?: The role of non-Gaussian innovations2022In: Finance Research Letters, Vol. 46Article in journal (Refereed)
  • 3. Kiss, T.
    et al.
    Nguyen, Hoang
    Linköping University, Department of Management and Engineering, Production Economics.
    Österholm, P.
    The Relation between the High-Yield Bond Spread and the Unemployment Rate in the Euro Area2022In: Finance Research Letters, Vol. 46Article in journal (Refereed)
  • 4.
    Kiss, Tamás
    et al.
    School of Business, Örebro University, Sweden.
    Mazur, Stepan
    School of Business, Örebro University, Sweden; School of Business and Economics,Linnaeus University, Växjö, Sweden.
    Nguyen, Hoang
    School of Business, Örebro University, Sweden.
    Österholm, Pär
    School of Business, Örebro University, Sweden; National Institute of Economic Research, Stockholm, Sweden.
    Modeling the relation between the US real economy and the corporate bond-yield spread in Bayesian VARs with non-Gaussian innovations2023In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 42, no 2, p. 347-368Article in journal (Refereed)
  • 5.
    Kiss, Tamás
    et al.
    Division of Economics, School of Business, Örebro University, Sweden.
    Nguyen, Hoang
    Division of Statistics, School of Business, Örebro University, Sweden.
    Österholm, Pär
    of Economics, School of Business, Örebro University, Sweden; National Institute of Economic Research, Stockholm, Sweden.
    Modelling Okun’s law: Does non-Gaussianity matter?2023In: Empirical Economics, ISSN 0377-7332, E-ISSN 1435-8921, Vol. 64, no 5, p. 2183-2213Article in journal (Refereed)
  • 6.
    Kiss, Tamás
    et al.
    Division of Economics, School of Business, Örebro University, Örebro, Sweden.
    Nguyen, Hoang
    2 Division of Statistics, School of Business, Örebro University, Örebro, Sweden.
    Österholm, Pär
    Division of Economics, School of Business, Örebro University, Örebro, Sweden; National Institute of Economic Research, Stockholm, Sweden.
    Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails2021In: Journal of Risk and Financial Management, E-ISSN 1911-8074, Vol. 14, no 11, p. 506-506Article in journal (Refereed)
    Abstract [en]

    In this paper, we analysed the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data from January 1954 to September 2019, the properties of the models were assessed both within- and out-of-sample. We found strong evidence in favour of modelling both GARCH effects and non-Gaussianity. Accounting for these properties improves within-sample performance as well as point and density forecasts.

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  • 7.
    Nguyen, Hoang
    et al.
    Linköping University, Department of Management and Engineering, Production Economics.
    Ausín, M. C.
    Galeano, P.
    Parallel Bayesian inference for high-dimensional dynamic factor copulas2019In: Journal of Financial Econometrics, Vol. 17, no 1, p. 118-151Article in journal (Refereed)
  • 8.
    Nguyen, Hoang
    et al.
    Linköping University, Department of Management and Engineering, Production Economics.
    Ausín, M. C.
    Galeano, P.
    Variational inference for high dimensional structured factor copulas2020In: Computational Statistics and Data Analysis, Vol. 151Article in journal (Refereed)
  • 9.
    Nguyen, Hoang
    et al.
    Linköping University, Department of Management and Engineering, Production Economics.
    Javed, Farrukh
    Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach2023In: Journal of Empirical Finance, ISSN 0927-5398, E-ISSN 1879-1727, Vol. 73, p. 272-292Article in journal (Refereed)
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  • 10.
    Nguyen, Hoang
    et al.
    Linköping University, Department of Management and Engineering, Production Economics.
    Nguyen, Trong-Nghia
    Discipline of Business Analytics, The University of Sydney Business School and ACEMS, Sydney, NSW, Australia.
    Tran, Minh-Ngoc
    Discipline of Business Analytics, The University of Sydney Business School and ACEMS, Sydney, NSW, Australia.
    A dynamic leverage stochastic volatility model2021In: Applied Economics Letters, ISSN 1350-4851, E-ISSN 1466-4291, Vol. 30, no 1, p. 97-102Article in journal (Refereed)
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  • 11.
    Nguyen, Hoang
    et al.
    Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.
    Osterholm, Par
    Orebro Univ, Sweden; Natl Inst Econ Res, Sweden; Univ Sydney, Australia.
    A note on the dynamic effects of supply and demand shocks in the crude oil market2024In: Applied Economics Letters, ISSN 1350-4851, E-ISSN 1466-4291Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate whether key relations in the crude oil market have been stable over time. This is done by estimating hybrid time-varying parameter structural Bayesian VAR models using monthly data ranging from February 1973 to May 2023. Model selection suggests that while stochastic volatility is preferred over homoscedasticity, the dynamics of the model are best described by constant parameters in all equations.

  • 12.
    Nguyen, Hoang
    et al.
    Linköping University, Department of Management and Engineering, Production Economics.
    Virbickaite, Audrone
    Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models2023In: Energy Economics, Vol. 124Article in journal (Refereed)
  • 13.
    Virbickaitė, Audronė
    et al.
    Department of Quantitative Methods, CUNEF Universidad, Calle Pirineos 55, 28040 Madrid, Spain.
    Nguyen, Hoang
    Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.
    Tran, Minh-Ngoc
    Discipline of Business Analytics, the University of Sydney Business School, Australia.
    Bayesian predictive distributions of oil returns using mixed data sampling volatility models2023In: Resources policy, ISSN 0301-4207, E-ISSN 1873-7641, Vol. 86, article id 104167Article in journal (Refereed)
    Abstract [en]

    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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1 - 13 of 13
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