Audrone Virbickaite
Personal Details
First Name: | Audrone |
Middle Name: | |
Last Name: | Virbickaite |
Suffix: | |
RePEc Short-ID: | pvi438 |
[This author has chosen not to make the email address public] | |
https://sites.google.com/view/audravirbickaitephd | |
Affiliation
Departament d'Economia Aplicada
Facultat de Ciències Econòmiques i Empresarials
Universitat de les Illes Balears
Palma de Mallorca, Spainhttp://dea.uib.cat/
RePEc:edi:dauibes (more details at EDIRC)
Research output
Jump to: Working papers ArticlesWorking papers
- Virbickaite, Audrone & Nguyen, Hoang & Tran, Minh-Ngoc, 2023.
"Bayesian Predictive Distributions of Oil Returns Using Mixed Data Sampling Volatility Models,"
Working Papers
2023:7, Örebro University, School of Business.
- Virbickaitė, Audronė & Nguyen, Hoang & Tran, Minh-Ngoc, 2023. "Bayesian predictive distributions of oil returns using mixed data sampling volatility models," Resources Policy, Elsevier, vol. 86(PA).
- Nguyen, Hoang & Virbickaite, Audrone, 2022.
"Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models,"
Working Papers
2022:5, Örebro University, School of Business.
- Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
- Audrone Virbickaite & Christoph Frey & Demian N. Macedo, 2019. "Sequential Stock Return Prediction Through Copulas," DEA Working Papers 91, Universitat de les Illes Balears, Departament d'Economía Aplicada.
- Audrone Virbickaite & Hedibert F. Lopes & Maria Concepción Ausín & Pedro Galeano, 2018.
"Particle Learning for Bayesian Semi-Parametric Stochastic Volatility Model,"
DEA Working Papers
88, Universitat de les Illes Balears, Departament d'Economía Aplicada.
- Audronė Virbickaitė & Hedibert F. Lopes & M. Concepción Ausín & Pedro Galeano, 2019. "Particle learning for Bayesian semi-parametric stochastic volatility model," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1007-1023, October.
Articles
- Nguyen, Hoang & Virbickaitė, Audronė, 2023.
"Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models,"
Energy Economics, Elsevier, vol. 124(C).
- Nguyen, Hoang & Virbickaite, Audrone, 2022. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Working Papers 2022:5, Örebro University, School of Business.
- Virbickaitė, Audronė & Nguyen, Hoang & Tran, Minh-Ngoc, 2023.
"Bayesian predictive distributions of oil returns using mixed data sampling volatility models,"
Resources Policy, Elsevier, vol. 86(PA).
- Virbickaite, Audrone & Nguyen, Hoang & Tran, Minh-Ngoc, 2023. "Bayesian Predictive Distributions of Oil Returns Using Mixed Data Sampling Volatility Models," Working Papers 2023:7, Örebro University, School of Business.
- Jaume Rosselló & Andreu Sansó & Audronė Virbickaitė, 2021. "How Local tourism managers can benefit from national surveys: estimating tourism and restaurant expenditures for small market segments," Current Issues in Tourism, Taylor & Francis Journals, vol. 24(24), pages 3433-3449, December.
- Virbickaitė, Audronė & Frey, Christoph & Macedo, Demian N., 2020. "Bayesian sequential stock return prediction through copulas," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
- Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2020. "Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction," Energy Economics, Elsevier, vol. 92(C).
- Audronė Virbickaitė & Hedibert F. Lopes, 2019. "Bayesian semiparametric Markov switching stochastic volatility model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(4), pages 978-997, July.
- Audronė Virbickaitė & Hedibert F. Lopes & M. Concepción Ausín & Pedro Galeano, 2019.
"Particle learning for Bayesian semi-parametric stochastic volatility model,"
Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1007-1023, October.
- Audrone Virbickaite & Hedibert F. Lopes & Maria Concepción Ausín & Pedro Galeano, 2018. "Particle Learning for Bayesian Semi-Parametric Stochastic Volatility Model," DEA Working Papers 88, Universitat de les Illes Balears, Departament d'Economía Aplicada.
- Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2016.
"A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection,"
Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 814-829.
- Audrone Virbickaite & M. Concepci'on Aus'in & Pedro Galeano, 2013. "A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model With Application to Portfolio Selection," Papers 1301.5129, arXiv.org, revised Jan 2014.
- Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
Citations
Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.Working papers
- Virbickaite, Audrone & Nguyen, Hoang & Tran, Minh-Ngoc, 2023.
"Bayesian Predictive Distributions of Oil Returns Using Mixed Data Sampling Volatility Models,"
Working Papers
2023:7, Örebro University, School of Business.
- Virbickaitė, Audronė & Nguyen, Hoang & Tran, Minh-Ngoc, 2023. "Bayesian predictive distributions of oil returns using mixed data sampling volatility models," Resources Policy, Elsevier, vol. 86(PA).
Cited by:
- Nguyen, Hoang & Virbickaitė, Audronė, 2023.
"Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models,"
Energy Economics, Elsevier, vol. 124(C).
- Nguyen, Hoang & Virbickaite, Audrone, 2022. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Working Papers 2022:5, Örebro University, School of Business.
- Nguyen, Hoang & Virbickaite, Audrone, 2022.
"Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models,"
Working Papers
2022:5, Örebro University, School of Business.
- Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
Cited by:
- Nguyen, Hoang & Javed, Farrukh, 2021.
"Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach,"
Working Papers
2021:15, Örebro University, School of Business.
- Nguyen, Hoang & Javed, Farrukh, 2023. "Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 272-292.
- Virbickaite, Audrone & Nguyen, Hoang & Tran, Minh-Ngoc, 2023.
"Bayesian Predictive Distributions of Oil Returns Using Mixed Data Sampling Volatility Models,"
Working Papers
2023:7, Örebro University, School of Business.
- Virbickaitė, Audronė & Nguyen, Hoang & Tran, Minh-Ngoc, 2023. "Bayesian predictive distributions of oil returns using mixed data sampling volatility models," Resources Policy, Elsevier, vol. 86(PA).
- Audrone Virbickaite & Hedibert F. Lopes & Maria Concepción Ausín & Pedro Galeano, 2018.
"Particle Learning for Bayesian Semi-Parametric Stochastic Volatility Model,"
DEA Working Papers
88, Universitat de les Illes Balears, Departament d'Economía Aplicada.
- Audronė Virbickaitė & Hedibert F. Lopes & M. Concepción Ausín & Pedro Galeano, 2019. "Particle learning for Bayesian semi-parametric stochastic volatility model," Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1007-1023, October.
Cited by:
- Guanyu Hu & Ming-Hui Chen & Nalini Ravishanker, 2023. "Bayesian analysis of spherically parameterized dynamic multivariate stochastic volatility models," Computational Statistics, Springer, vol. 38(2), pages 845-869, June.
- Virbickaitė, Audronė & Frey, Christoph & Macedo, Demian N., 2020. "Bayesian sequential stock return prediction through copulas," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
- Audrone Virbickaite & Hedibert F. Lopes, 2018. "Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model," DEA Working Papers 89, Universitat de les Illes Balears, Departament d'Economía Aplicada.
- Tevfik Aktekin & Nicholas G. Polson & Refik Soyer, 2020. "A family of multivariate non‐gaussian time series models," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 691-721, September.
Articles
- Nguyen, Hoang & Virbickaitė, Audronė, 2023.
"Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models,"
Energy Economics, Elsevier, vol. 124(C).
See citations under working paper version above.
- Nguyen, Hoang & Virbickaite, Audrone, 2022. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Working Papers 2022:5, Örebro University, School of Business.
- Virbickaitė, Audronė & Nguyen, Hoang & Tran, Minh-Ngoc, 2023.
"Bayesian predictive distributions of oil returns using mixed data sampling volatility models,"
Resources Policy, Elsevier, vol. 86(PA).
See citations under working paper version above.
- Virbickaite, Audrone & Nguyen, Hoang & Tran, Minh-Ngoc, 2023. "Bayesian Predictive Distributions of Oil Returns Using Mixed Data Sampling Volatility Models," Working Papers 2023:7, Örebro University, School of Business.
- Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2020.
"Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction,"
Energy Economics, Elsevier, vol. 92(C).
Cited by:
- Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022.
"Bayesian Forecasting in Economics and Finance: A Modern Review,"
Papers
2212.03471, arXiv.org, revised Jul 2023.
- Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
- Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
- Gong, Xiao-Li & Feng, Yong-Kang & Liu, Jian-Min & Xiong, Xiong, 2023. "Study on international energy market and geopolitical risk contagion based on complex network," Resources Policy, Elsevier, vol. 82(C).
- Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022.
"Bayesian Forecasting in Economics and Finance: A Modern Review,"
Papers
2212.03471, arXiv.org, revised Jul 2023.
- Audronė Virbickaitė & Hedibert F. Lopes, 2019.
"Bayesian semiparametric Markov switching stochastic volatility model,"
Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(4), pages 978-997, July.
Cited by:
- Nguyen, Hoang & Virbickaitė, Audronė, 2023.
"Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models,"
Energy Economics, Elsevier, vol. 124(C).
- Nguyen, Hoang & Virbickaite, Audrone, 2022. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Working Papers 2022:5, Örebro University, School of Business.
- Nguyen, Hoang & Virbickaitė, Audronė, 2023.
"Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models,"
Energy Economics, Elsevier, vol. 124(C).
- Audronė Virbickaitė & Hedibert F. Lopes & M. Concepción Ausín & Pedro Galeano, 2019.
"Particle learning for Bayesian semi-parametric stochastic volatility model,"
Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1007-1023, October.
See citations under working paper version above.
- Audrone Virbickaite & Hedibert F. Lopes & Maria Concepción Ausín & Pedro Galeano, 2018. "Particle Learning for Bayesian Semi-Parametric Stochastic Volatility Model," DEA Working Papers 88, Universitat de les Illes Balears, Departament d'Economía Aplicada.
- Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2016.
"A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection,"
Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 814-829.
- Audrone Virbickaite & M. Concepci'on Aus'in & Pedro Galeano, 2013. "A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model With Application to Portfolio Selection," Papers 1301.5129, arXiv.org, revised Jan 2014.
Cited by:
- Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
- Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
- Audronė Virbickaitė & Hedibert F. Lopes & M. Concepción Ausín & Pedro Galeano, 2019.
"Particle learning for Bayesian semi-parametric stochastic volatility model,"
Econometric Reviews, Taylor & Francis Journals, vol. 38(9), pages 1007-1023, October.
- Audrone Virbickaite & Hedibert F. Lopes & Maria Concepción Ausín & Pedro Galeano, 2018. "Particle Learning for Bayesian Semi-Parametric Stochastic Volatility Model," DEA Working Papers 88, Universitat de les Illes Balears, Departament d'Economía Aplicada.
- Donelli, Nicola & Peluso, Stefano & Mira, Antonietta, 2021. "A Bayesian semiparametric vector Multiplicative Error Model," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
- Nianling Wang & Lijie Zhang & Zhuo Huang & Yong Li, 2021. "Asymmetric Correlations in Predicting Portfolio Returns," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 97-120, March.
- Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.
- Marc S. Paolella, 2017. "The Univariate Collapsing Method for Portfolio Optimization," Econometrics, MDPI, vol. 5(2), pages 1-33, May.
- Martina Danielova Zaharieva & Mark Trede & Bernd Wilfling, 2017. "Bayesian semiparametric multivariate stochastic volatility with an application to international stock-market co-movements," CQE Working Papers 6217, Center for Quantitative Economics (CQE), University of Muenster.
- Jim Griffin & Maria Kalli & Mark Steel, 2018. "Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 207-218, June.
- Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015.
"Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey,"
Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
Cited by:
- Neenu Chalissery & Suhaib Anagreh & Mohamed Nishad T. & Mosab I. Tabash, 2022. "Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis," JRFM, MDPI, vol. 15(9), pages 1-23, September.
- Manabu Asai & Michael McAleer, 2022.
"Bayesian Analysis of Realized Matrix-Exponential GARCH Models,"
Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 103-123, January.
- Manabu Asai & Michael McAleer, 2018. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Tinbergen Institute Discussion Papers 18-005/III, Tinbergen Institute.
- Manabu Asai & Michael McAleer, 2018. "Bayesian analysis of realized matrix-exponential GARCH models," Documentos de Trabajo del ICAE 2018-04, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
- Asai, M. & McAleer, M.J., 2018. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Econometric Institute Research Papers 2018-005/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Komla M. Agudze & Monica Billio & Roberto Casarin & Francesco Ravazzolo, 2021.
"Markov Switching Panel with Endogenous Synchronization Effects,"
BEMPS - Bozen Economics & Management Paper Series
BEMPS82, Faculty of Economics and Management at the Free University of Bozen.
- Agudze, Komla M. & Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco, 2022. "Markov switching panel with endogenous synchronization effects," Journal of Econometrics, Elsevier, vol. 230(2), pages 281-298.
- Xin Jin & John M. Maheu, 2014.
"Bayesian Semiparametric Modeling of Realized Covariance Matrices,"
Working Paper series
34_14, Rimini Centre for Economic Analysis.
- Jin, Xin & Maheu, John M., 2016. "Bayesian semiparametric modeling of realized covariance matrices," Journal of Econometrics, Elsevier, vol. 192(1), pages 19-39.
- Jin, Xin & Maheu, John M, 2014. "Bayesian Semiparametric Modeling of Realized Covariance Matrices," MPRA Paper 60102, University Library of Munich, Germany.
- Vica Tendenan & Richard Gerlach & Chao Wang, 2020. "Tail risk forecasting using Bayesian realized EGARCH models," Papers 2008.05147, arXiv.org, revised Aug 2020.
- Zhong, Guang-Yan & Li, Jiang-Cheng & Jiang, George J. & Li, Hai-Feng & Tao, Hui-Ming, 2018. "The time delay restraining the herd behavior with Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 335-346.
- Martin Magris & Alexandros Iosifidis, 2023. "Variational Inference for GARCH-family Models," Papers 2310.03435, arXiv.org.
- Ioannis Papageorgiou & Ioannis Kontoyiannis, 2023. "The Bayesian Context Trees State Space Model for time series modelling and forecasting," Papers 2308.00913, arXiv.org, revised Oct 2023.
- Foos, Daniel & Lütkebohmert, Eva & Markovych, Mariia & Pliszka, Kamil, 2017. "Euro area banks' interest rate risk exposure to level, slope and curvature swings in the yield curve," Discussion Papers 24/2017, Deutsche Bundesbank.
- Audrone Virbickaite & Hedibert F. Lopes, 2018. "Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model," DEA Working Papers 89, Universitat de les Illes Balears, Departament d'Economía Aplicada.
- Marius Galabe Sampid & Haslifah M Hasim & Hongsheng Dai, 2018. "Refining value-at-risk estimates using a Bayesian Markov-switching GJR-GARCH copula-EVT model," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-33, June.
- Yuzhi Cai, 2021. "Estimating expected shortfall using a quantile function model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4332-4360, July.
More information
Research fields, statistics, top rankings, if available.Statistics
Access and download statistics for all items
Co-authorship network on CollEc
NEP Fields
NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 3 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.- NEP-ECM: Econometrics (3) 2018-05-28 2022-05-30 2023-05-22. Author is listed
- NEP-ENE: Energy Economics (2) 2022-05-30 2023-05-22. Author is listed
- NEP-ETS: Econometric Time Series (2) 2018-05-28 2023-05-22. Author is listed
- NEP-MAC: Macroeconomics (1) 2022-05-30. Author is listed
- NEP-ORE: Operations Research (1) 2018-05-28. Author is listed
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