| DEoptim: An R package for global optimization by differential evolution K Mullen, D Ardia, DL Gil, D Windover, J Cline Journal of Statistical Software 40 (6), 1-26, 2011 | 768 | 2011 |
| Regime changes in Bitcoin GARCH volatility dynamics D Ardia, K Bluteau, M Rüede Finance Research Letters 29, 266-271, 2019 | 289 | 2019 |
| Climate change concerns and the performance of green vs. brown stocks D Ardia, K Bluteau, K Boudt, K Inghelbrecht Management Science 69 (12), 7607-7632, 2023 | 285* | 2023 |
| Differential evolution with DEoptim: an application to non-convex portfolio optimization D Ardia, K Boudt, P Carl, K Mullen, BG Peterson The R Journal 3 (1), 27-34, 2011 | 278 | 2011 |
| Forecasting risk with Markov-switching GARCH models: A large-scale performance study D Ardia, K Bluteau, K Boudt, L Catania International Journal of Forecasting 34 (4), 733-747, 2018 | 183 | 2018 |
| Financial risk management with Bayesian estimation of GARCH models D Ardia Lecture Notes in Economics and Mathematical Systems - Springer, 2008 | 175* | 2008 |
| COVID-19 data hub E Guidotti, D Ardia Journal of Open Source Software 5 (51), 2376, 2020 | 157 | 2020 |
| Markov-switching GARCH models in R: The MSGARCH package D Ardia, K Bluteau, K Boudt, L Catania, DA Trottier Journal of Statistical Software 91 (4), 2019 | 153 | 2019 |
| Econometrics meets sentiment: An overview of methodology and applications A Algaba, D Ardia, K Bluteau, S Borms, K Boudt Journal of Economic Surveys 34 (3), 512-547, 2020 | 125 | 2020 |
| DEoptim: Differential evolution in R D Ardia, KM Mullen, BG Peterson, J Ulrich R package version, 2.2-3, 2015 | 110* | 2015 |
| Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values D Ardia, K Bluteau, K Boudt International Journal of Forecasting 35 (4), 1370-1386, 2019 | 96 | 2019 |
| Bayesian estimation of a Markov‐switching threshold asymmetric GARCH model with Student‐t innovations D Ardia The Econometrics Journal 12 (1), 105-126, 2009 | 96 | 2009 |
| Bayesian estimation of the garch (1, 1) model with student-t innovations D Ardia, LF Hoogerheide The R Journal 2 (2), 41-47, 2010 | 85 | 2010 |
| Generalized autoregressive score models in R: The GAS package D Ardia, K Boudt, L Catania Journal of Statistical Software 88 (6), 1-28, 2019 | 74 | 2019 |
| GARCH models for daily stock returns: Impact of estimation frequency on Value-at-Risk and Expected Shortfall forecasts D Ardia, LF Hoogerheide Economics Letters 123 (2), 187-190, 2014 | 66 | 2014 |
| Jump‐diffusion calibration using differential evolution D Ardia, J David, O Arango, NDG Gómez Wilmott 2011 (55), 76-79, 2011 | 57 | 2011 |
| The impact of covariance misspecification in risk-based portfolios D Ardia, G Bolliger, K Boudt, JP Gagnon-Fleury Annals of Operations Research 254, 1-16, 2017 | 56 | 2017 |
| A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood D Ardia, N Baştürk, L Hoogerheide, HK Van Dijk Computational Statistics & Data Analysis 56 (11), 3398-3414, 2012 | 56 | 2012 |
| Moments of standardized Fernandez–Steel skewed distributions: Applications to the estimation of GARCH-type models DA Trottier, D Ardia Finance Research Letters 18, 311-316, 2016 | 48 | 2016 |
| Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: The R package AdMit D Ardia, LF Hoogerheide, HK Van Dijk Journal of Statistical Software 29 (3), 1-32, 2009 | 45 | 2009 |
Kris BoudtGhent University, Vrije Universiteit Brussel, Vrije Universiteit AmsterdamVerified email at ugent.be
