Ioannis D. Vrontos

Associate Professor in the Department of Statistics of Athens University of Economics and Business

 

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Appointments

Research Interests

Publications

Students

Teaching
(in greek)

†††††† Published Papers

1.      Koki, C., Meligkotsidou, L. and Vrontos, I.D. (2020). Forecasting under model uncertainty:Non-homogeneous hidden Markov models with Polya-Gamma data augmentation, Journal of Forecasting, to appear.

2.      Meligkotsidou L., Panopoulou, E., Vrontos I.D. and Vrontos S.D. (2019). Out-of-sample equity premium prediction: a complete subset quantile regression approach, European Journal of Finance, to appear.

3.      Meligkotsidou L., Panopoulou, E., Vrontos I.D. and Vrontos S.D. (2019). Quantile forecast combinations in realised volatility prediction, Journal of the Operational Research Society, 70, 10, 1720-1733.

4.      Meligkotsidou, L., Tzavalis E. and Vrontos I.D. (2017). On Bayesian Analysis and Unit Root Testing for Autoregressive Models in the Presence of Multiple Structural Breaks, Econometrics and Statistics, 4, 70-90.

5.      Andersen, J.V., Vrontos, I.D., Dellaportas, P. and Galam, S. (2014). Communication impacting financial markets, Europhysics Letters, 108, 2, 28007-p1-p6.

6.      Meligkotsidou L., Panopoulou, E., Vrontos I.D. and Vrontos S.D. (2014). A Quantile Regression Approach to Equity Premium Prediction, Journal of Forecasting, 33, 558-576.

7.      Meligkotsidou L. and Vrontos I.D. (2014). Detecting Structural Breaks in Multivariate Financial Time Series: Evidence from Hedge Fund Investments, Journal of Statistical Computation and Simulation, 84, 5, 1115-1135.

8.      Meligkotsidou L., Tzavalis E. and Vrontos I.D. (2014).A Bayesian method of distinguishing unit root from stationary processes based on panel data models with cross-sectional dependence, Statistics and Computing, 24, 297-315.

9.      Vrontos S.D., Vrontos I.D. and Meligkotsidou L. (2013). Asset-Liability Management for Pension Funds in a Time-Varying Volatility Environment, Journal of Asset Management, 14, 306-333.

10.  Meligkotsidou L., Tzavalis E. and Vrontos I.D. (2012). A Bayesian panel data framework for examining the economic growth convergence hypothesis: do the G7 countries converge?, Journal of Applied Statistics, 39, 9, 1975-1990.

11.  Vrontos I.D. (2012). Evidence for Hedge Fund Predictability from a Multivariate Studentís t Full Factor GARCH model, Journal of Applied Statistics, 39, 1295-1321.

12.  Vrontos I.D., Meligkotsidou L. and Vrontos S.D. (2011). Performance Evaluation of Mutual Fund Investments: The impact of Non-Normality and Time-Varying Volatility, Journal of Asset Management, 12, 292-307.

13.  Giannikis, D., and Vrontos I.D. (2011).A Bayesian approach to detecting nonlinear risk exposures in hedge fund strategies. Journal of Banking and Finance, 35, 1399-1414.

14.  Meligkotsidou L., Tzavalis E. and Vrontos I.D. (2011). A Bayesian Analysis of Unit Roots and Structural Breaks in the Level, the Trend and the Error Variance of Autoregressive Models of Economic Series, Econometric Reviews, 30, 2, 208-249.

15.  Diamantopoulos K. and Vrontos I.D. (2010). A Student-t Full Factor Multivariate GARCH model. Computational Economics, 35, 63-83.

16.  Meligkotsidou L., Vrontos I.D. and Vrontos S.D. (2009). Quantile Regression Analysis of Hedge Fund Strategies. Journal of Empirical Finance, 16, 264-279.

17.  Meligkotsidou L. and Vrontos I.D. (2008). Detecting Structural Breaks and Identifying Risk factors in Hedge Fund returns: A Bayesian approach. Journal of Banking and Finance, 32, 2471-2481.

18.  Giannikis D., Vrontos I.D. and Dellaportas P. (2008). Modelling nonlinearities and heavy tails via threshold Normal mixture GARCH models, Computational Statistics and Data Analysis, 52, 1549-1571. 

19.  Vrontos S.D.,  Vrontos I.D. and Giamouridis D. (2008). Hedge fund pricing and model uncertainty, Journal of Banking and Finance, 32, 741-753.

20.  Dellaportas P. and Vrontos I.D. (2007). Modelling Volatility Asymmetries: A Bayesian analysis of a class of tree structured multivariate GARCH models, Econometrics Journal, 10, 503-520. 

21.  Giamouridis D., and Vrontos I.D. (2007). Hedge fund portfolio construction: A comparison of static and dynamic approaches, Journal of Banking and Finance, 31, 199-217.

22.  Vrontos I.D, Dellaportas P. and Politis D.N. (2003). A full-factor multivariate GARCH model. Econometrics Journal, 6, 312-334. 

23.  Vrontos I.D, Dellaportas P. and Politis D.N. (2003). Inference for some multivariate ARCH and GARCH models. Journal of Forecasting, 22, 427-446.

24.  Vrontos I.D., Giakoumatos S.G., Dellaportas P. and Politis D.N. (2001). An application of three bivariate time-varying volatility models. Applied Stochastic Models in Business and Industry, 17, 121-133.

25.  Vrontos I.D., Dellaportas P. and Politis D.N. (2000). Full Bayesian inference for GARCH and EGARCH models. Journal of Business and Economics Statistics, 18, 187-198. 

26.  Giakoumatos S.G., Vrontos I.D., Dellaportas P. and Politis D.N. (1999). An MCMC Convergence Diagnostic using Subsampling. Journal of Computational and Graphical Statistics, 8, 431-451. 

††††† Conference Proceedings

1.      Vrontos I.D., Dellaportas P. and Politis D.N. (1999). Bayesian analysis of bivariate ARCH and GARCH models. Hercma '98: 4th Hellenic European Conference on Computer Mathematics and its applications, E.A. Lipitakis (Ed), pp. 459-466. 

††††† Work in Progress

1.      Vrontos,S. D., Galakis, J. and Vrontos I.D. (2020). Modeling and Predicting U.S. Recessions.

2.      Galakis, J., Vrontos,S. D. and Vrontos I.D. (2019). Fund Manager Selection based on dynamic non-linear models.

3.      Galakis, J., Vrontos, I.D. and Xidonas, P. (2019). On tree-structured linear and quantile regression based asset pricing.

4.      Galakis, J. and Vrontos I.D. (2019). Stock return predictability using Multivariate Regression models.

5.      Vrontos I.D. (2018). Investigating Hedge Fund return Predictability: A Bayesian approach. 

†††††† Funded Research Projects

1.      A Bayesian Tree structured quantile regression approach to financial series predictability (ELKE OPA-funded research project, 2018-2019)

2.      Bayesian Threshold Regression Models with Application to Economic and Financial Data (ELKE OPA-funded research project, 2017-2018).

3.      Quantile Autoregressions in Realised Volatility Prediction (ELKE OPA-funded research project, 2015-2017).

4.      Large Shocks, Structural Breaks and Macroeconomic Relationships (ARISTEIA II), Supported by the General Secretariat for Research and Technology (Primary and Scientific Coordinator: Elias Tzavalis, Athens University of Economics and Business), Member of the Research Group, 2014-2016.

5.      Systemic Risk Tomography (SYRTO) Project, Funded by the European Union under the 7th framework programme (FP7-SSH/2007-2013) Grant Agreement no 320270 (Primary and Scientific Coordinator: Roberto Savona, University of Brescia, Leader of the AUEB research group: Petros Dellaportas), Member of the AUEB Research Group, 2013-2016.

6.      The Dark Side of The Accretion History of the Universe, Thales Program, Supported by the European Commission and Greek National Resources (Project Coordinator: Antonios Georgakakis, National Observatory of Athens, Leader of the Statistics Research Group: Loukia Meligkotsidou), Member of the Statistics Research Group, 2012-2015.

7.      Analysis of Financial time series using Bayesian non-parametric methods (ELKE OPA-funded research project, 2009-2010).

8.      Asset-Liability Management for Pension Funds in a Time-Varying Volatility Environment (CKER SOA US - funded research project, joint with S. Vrontos and L. Meligkotsidou, 2008-2009)

9.      Hedge funds return predictability in the presence of model uncertainty and implications for wealth allocation (INQUIRE UK - funded research project, joint with D. Giamouridis, 2006)

††††† Unpublished Papers

1.      Vrontos I.D. and Giamouridis D. (2008). Hedge fund return predictability in the presence of model uncertainty and implications for wealth allocation.