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.
- 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.
- Virbickaitė, Audronė & Frey, Christoph & Macedo, Demian N., 2020. "Bayesian sequential stock return prediction through copulas," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
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:
- 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 & 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Vica Tendenan & Richard Gerlach & Chao Wang, 2020. "Tail risk forecasting using Bayesian realized EGARCH models," Papers 2008.05147, arXiv.org, revised Aug 2020.
- 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.
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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