Since September 2020, Audra works as an Assistant professor of Statistics and Econometrics at CUNEF. Her research interests include Bayesian econometrics, particle filters, time series analysis, time-varying volatility models, among others. Prior to joining CUNEF, Audra worked as an Assistant Professor of Econometrics at the University of Balearic Islands, Palma, and, before that, she did a two-year post-doctoral stay at the University of Konstanz, Germany. During her doctoral studies at the Statistics Department, Universidad Carlos III de Madrid, Audra did a pre-doctoral reserach stay at the Chicago Booth School of Business, where she worked with prof. Hedibert Lopes. Audra has presented at many international congresses and conferences. She has published in internationally recognized scientific journals. Audra currently serves as a board member of European Association of Young Economists.
2008 Bachelor's degree in Economics, Vilnius University, Lithuania.
2011 Master's degree in Business Administration and Quantitative Methods, Statistics Department, Universidad Carlos III de Madrid
2015 Doctoral Degree in Business Administration and Quantitative Methods, Statistics Department, Universidad Carlos III de Madrid
Áreas de interés
Bayesian econometrics, Financial econometrics,Time-varying volatility models, Sequential Monte Carlo Methods, Particle filters, Copulas, Time series analysis
Publicaciones en revistas científicas
- Virbickaite, A., Frey, C., Macedo, D.N (2020). Sequential Stock Return Prediction Through Copulas, The Journal of Economic Asymmetries, Forthcoming.
- Virbickaite, A., Lopes, H.F. (2019). Bayesian Semi-Parametric Markov Switching Stochastic Volatility Model, Applied Stochastic Models in Business and Industry, 35(4), 978-997.
- Virbickaite, A., Lopes, H.F., Ausin, C., Galeano, P. (2019). Particle learning for Bayesian semi-parametric stochastic volatility model, Econometric Reviews, 38 (9), 1007-1023.
- Virbickaite, A., Ausin, C., Galeano, P. (2016). A Bayesian Non-Parametric Approach to Asymmetric Dynamic Conditional Correlation Model With Application to Portfolio Selection, Computational Statistics and Data Analysis, 100, 814–829.
- Virbickaite, A., Ausin, C., Galeano, P. (2015). Bayesian Inference Methods for Univariate and Multivariate GARCH Models: a Survey, Journal of Economic Surveys, 29 (1), 76–96.