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David Ardia

Personal Details

First Name:David
Middle Name:
Last Name:Ardia
Suffix:
RePEc Short-ID:par194
[This author has chosen not to make the email address public]
Terminal Degree:2008 Faculté des sciences économiques et sociales - Wirtschafts- und Sozialwissenschaftliche Fakultät; Université de Fribourg - Universität Freiburg (from RePEc Genealogy)

Affiliation

(95%) Institut d'analyse financière (IAF)
Faculté des sciences économiques (FSE)
Université de Neuchâtel

Neuchâtel, Switzerland
http://www2.unine.ch/iaf

: +41 32 718 1350
+41 32 718 1401
Pierre-à-Mazel 7, CH-2000 Neuchâtel
RePEc:edi:iafnech (more details at EDIRC)

(5%) Département finance, assurance et immobilier
Faculté des sciences de l'administration
Université Laval

Québec, Canada
http://www5.fsa.ulaval.ca/sgc/faculte/departementsecole/financeassurance/cache/bypass

: (418) 656-2180
(418)656-2624
(418) 656-2180
RePEc:edi:dflavca (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Books

Working papers

  1. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Generalized Autoregressive Score Models in R: The GAS Package," Papers 1609.02354, arXiv.org.
  2. David Ardia & Kris Boudt & Leopoldo Catania, 2016. "Value-at-Risk Prediction in R with the GAS Package," Papers 1611.06010, arXiv.org.
  3. David Ardia & Lukasz Gatarek & Lennart F. hoogerheide, 2014. "A New Bootstrap Test for the Validity of a Set of Marginal Models for Multiple Dependent Time Series: an Application to Risk Analysis," Cahiers de recherche 1413, CIRPEE.
  4. Attilio Meucci & David Ardia & Simon Keel, 2013. "Fully Flexible Views in Multivariate Normal Markets," Cahiers de recherche 1311, CIRPEE.
  5. David Ardia & Lennart F. Hoogerheide, 2013. "Cross-Sectional Distribution of GARCH Coefficients across S&P 500 Constituents: Time-Variation over the Period 2000-2012," Cahiers de recherche 1313, CIRPEE.
  6. David Ardia & Lennart Hoogerheide, 2013. "GARCH Models for Daily Stock Returns: Impact of Estimation Frequency on Value-at-Risk and Expected Shortfall Forecasts," Tinbergen Institute Discussion Papers 13-047/III, Tinbergen Institute.
  7. David Ardia & Lennart F. Hoogerheide, 2013. "Worldwide equity Risk Prediction," Cahiers de recherche 1312, CIRPEE.
  8. David Ardia & Kris Boudt, 2013. "Implied Expected Returns and the Choice of a Mean-Variance Efficient Portfolio Proxy," Cahiers de recherche 1328, CIRPEE.
  9. David Ardia & Kris Boudt, 2013. "The Peer Performance of Hedge Funds," Cahiers de recherche 1329, CIRPEE.
  10. Ardia, David & Lennart, Hoogerheide & Nienke, Corré, 2011. "Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation?," MPRA Paper 28259, University Library of Munich, Germany.
  11. Ardia, David & Boudt, Kris & Carl, Peter & Mullen, Katharine M. & Peterson, Brian, 2010. "Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization," MPRA Paper 22135, University Library of Munich, Germany.
  12. David Ardia & Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2010. "A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihood," Tinbergen Institute Discussion Papers 10-059/4, Tinbergen Institute.
  13. Ardia, David & Ospina, Juan & Giraldo, Giraldo, 2010. "Jump-Diffusion Calibration using Differential Evolution," MPRA Paper 26184, University Library of Munich, Germany, revised 25 Oct 2010.
  14. David Ardia & Lennart F. Hoogerheide, 2010. "Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations," Tinbergen Institute Discussion Papers 10-045/4, Tinbergen Institute.
  15. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.
  16. David Ardia & Lennart Hoogerheide & Herman K. van Dijk, 2009. "To Bridge, to Warp or to Wrap? A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihoods," Tinbergen Institute Discussion Papers 09-017/4, Tinbergen Institute.
  17. Ardia, David, 2009. "Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R," MPRA Paper 17414, University Library of Munich, Germany.
  18. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2009. "DEoptim: An R Package for Global Optimization by Differential Evolution," MPRA Paper 21743, University Library of Munich, Germany, revised 26 Dec 2010.
  19. Keel, Simon & Ardia, David, 2009. "Generalized Marginal Risk," MPRA Paper 17258, University Library of Munich, Germany.
  20. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2008. "Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit," DQE Working Papers 9, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 07 Jan 2009.
  21. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2008. "AdMit: Adaptive Mixtures of Student-t Distributions," DQE Working Papers 10, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 07 Jan 2009.
  22. Ardia, David, 2007. "Bayesian Estimation of a Markov-Switching Threshold Asymmetric GARCH Model with Student-t Innovations," DQE Working Papers 6, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 08 Jul 2008.
  23. Ardia, David, 2007. "Tests d’arbitrage sur options: une analyse empirique des cotations de market-makers," DQE Working Papers 8, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland.
  24. Ardia, David, 2002. "Tests d'arbitrage et surfaces de volatilité : analyse empirique sur données haute fréquence
    [Arbitrage tests and surface of implied volatility: An empirical analysis of high frequency data]
    ," MPRA Paper 17415, University Library of Munich, Germany.

Articles

  1. Trottier, Denis-Alexandre & Ardia, David, 2016. "Moments of standardized Fernandez–Steel skewed distributions: Applications to the estimation of GARCH-type models," Finance Research Letters, Elsevier, vol. 18(C), pages 311-316.
  2. Ardia, David & Boudt, Kris & Wauters, Marjan, 2016. "The economic benefits of market timing the style allocation of characteristic-based portfolios," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 38-62.
  3. David Ardia & Kris Boudt & Marjan Wauters, 2016. "Smart beta and CPPI performance," Finance, Presses universitaires de Grenoble, vol. 37(3), pages 31-65.
  4. David Ardia & Lukasz T. Gatarek & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-19, March.
  5. Ardia, David & Boudt, Kris, 2015. "Testing equality of modified Sharpe ratios," Finance Research Letters, Elsevier, vol. 13(C), pages 97-104.
  6. Ardia, David & Hoogerheide, Lennart F., 2014. "GARCH models for daily stock returns: Impact of estimation frequency on Value-at-Risk and Expected Shortfall forecasts," Economics Letters, Elsevier, vol. 123(2), pages 187-190.
  7. David Ardia & Lennart F. Hoogerheide, 2013. "Worldwide equity risk prediction," Applied Economics Letters, Taylor & Francis Journals, vol. 20(14), pages 1333-1339, September.
  8. Hoogerheide, Lennart F. & Ardia, David & Corré, Nienke, 2012. "Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?," Economics Letters, Elsevier, vol. 116(3), pages 322-325.
  9. Ardia, David & Baştürk, Nalan & Hoogerheide, Lennart & van Dijk, Herman K., 2012. "A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3398-3414.
  10. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
  11. David Ardia, 2009. "Bayesian estimation of a Markov-switching threshold asymmetric GARCH model with Student-t innovations," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 105-126, March.
  12. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2009. "Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i03).

Books

  1. David Ardia, 2008. "Financial Risk Management with Bayesian Estimation of GARCH Models," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-78657-3, July.

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

  1. David Ardia & Lennart F. Hoogerheide, 2013. "Cross-Sectional Distribution of GARCH Coefficients across S&P 500 Constituents: Time-Variation over the Period 2000-2012," Cahiers de recherche 1313, CIRPEE.

    Cited by:

    1. Georgios Bampinas & Konstantinos Ladopoulos & Theodore Panagiotidis, 2017. "A note on the estimated GARCH coefficients from the S&P1500 universe," Working Paper series 17-09, Rimini Centre for Economic Analysis.

  2. David Ardia & Lennart Hoogerheide, 2013. "GARCH Models for Daily Stock Returns: Impact of Estimation Frequency on Value-at-Risk and Expected Shortfall Forecasts," Tinbergen Institute Discussion Papers 13-047/III, Tinbergen Institute.

    Cited by:

    1. Lucas, André & Zhang, Xin, 2016. "Score-driven exponentially weighted moving averages and Value-at-Risk forecasting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 293-302.
    2. Iglesias, Emma M., 2015. "Value at Risk and expected shortfall of firms in the main European Union stock market indexes: A detailed analysis by economic sectors and geographical situation," Economic Modelling, Elsevier, vol. 50(C), pages 1-8.
    3. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    4. Krämer, Walter & Wied, Dominik, 2015. "A simple and focused backtest of value at risk," Economics Letters, Elsevier, vol. 137(C), pages 29-31.

  3. David Ardia & Kris Boudt, 2013. "Implied Expected Returns and the Choice of a Mean-Variance Efficient Portfolio Proxy," Cahiers de recherche 1328, CIRPEE.

    Cited by:

    1. David Ardia & Guido Bolliger & Kris Boudt & Jean-Philippe Gagnon-Fleury, 2017. "The impact of covariance misspecification in risk-based portfolios," Annals of Operations Research, Springer, vol. 254(1), pages 1-16, July.

  4. Ardia, David & Boudt, Kris & Carl, Peter & Mullen, Katharine M. & Peterson, Brian, 2010. "Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization," MPRA Paper 22135, University Library of Munich, Germany.

    Cited by:

    1. Ankit Dangi, 2013. "Financial Portfolio Optimization: Computationally guided agents to investigate, analyse and invest!?," Papers 1301.4194, arXiv.org.
    2. Jarraya, Bilel, 2013. "Asset allocation and portfolio optimization problems with metaheuristics: a literature survey," MPRA Paper 53698, University Library of Munich, Germany, revised 2013.
    3. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2009. "DEoptim: An R Package for Global Optimization by Differential Evolution," MPRA Paper 21743, University Library of Munich, Germany, revised 26 Dec 2010.
    4. Ardia, David & Ospina, Juan & Giraldo, Giraldo, 2010. "Jump-Diffusion Calibration using Differential Evolution," MPRA Paper 26184, University Library of Munich, Germany, revised 25 Oct 2010.

  5. David Ardia & Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2010. "A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihood," Tinbergen Institute Discussion Papers 10-059/4, Tinbergen Institute.

    Cited by:

    1. Bauwens, Luc & Dufays, Arnaud & Rombouts, Jeroen V.K., 2014. "Marginal likelihood for Markov-switching and change-point GARCH models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 508-522.
    2. Bauwens, Luc & Carpantier, Jean-François & Dufays, Arnaud, 2015. "Autoregressive moving average infinite hidden markov-switching models," CORE Discussion Papers 2015007, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    4. Lukasz Gatarek & Lennart Hoogerheide & Koen Hooning & Herman K. van Dijk, 2013. "Censored Posterior and Predictive Likelihood in Left-Tail Prediction for Accurate Value at Risk Estimation," Tinbergen Institute Discussion Papers 13-060/III, Tinbergen Institute, revised 06 Mar 2014.
    5. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
    6. Chan, Joshua & Eisenstat, Eric, 2012. "Marginal Likelihood Estimation with the Cross-Entropy Method," MPRA Paper 40051, University Library of Munich, Germany.
    7. David Ardia & Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2010. "A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihood," Tinbergen Institute Discussion Papers 10-059/4, Tinbergen Institute.
    8. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
    9. Perrakis, Konstantinos & Ntzoufras, Ioannis & Tsionas, Efthymios G., 2014. "On the use of marginal posteriors in marginal likelihood estimation via importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 54-69.
    10. Joshua C.C. Chan & Eric Eisenstat, 2015. "Bayesian model comparison for time-varying parameter VARs with stochastic volatility," CAMA Working Papers 2015-32, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    11. David Ardia & Lennart Hoogerheide, 2013. "GARCH Models for Daily Stock Returns: Impact of Estimation Frequency on Value-at-Risk and Expected Shortfall Forecasts," Tinbergen Institute Discussion Papers 13-047/III, Tinbergen Institute.
    12. Guidolin, Massimo & Ravazzolo, Francesco & Tortora, Andrea Donato, 2013. "Alternative econometric implementations of multi-factor models of the U.S. financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(2), pages 87-111.
    13. CARPANTIER, Jean-François & DUFAYS, Arnaud, 2014. "Specific Markov-switching behaviour for ARMA parameters," CORE Discussion Papers 2014014, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Nalan Baştürk & Stefano Grassi & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Parallelization Experience with Four Canonical Econometric Models Using ParMitISEM," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-20, March.
    15. Lennart Hoogerheide & Richard Kleijn & Francesco Ravazzolo & Herman K. Van Dijk & Marno Verbeek, 2010. "Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 251-269.
    16. Arnold Zellner & Tomohiro Ando & Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2011. "Instrumental Variables, Errors in Variables, and Simultaneous Equations Models: Applicability and Limitations of Direct Monte Carlo," Tinbergen Institute Discussion Papers 11-137/4, Tinbergen Institute.
    17. Jean-François Carpantier, 2014. "Specific Markov-switching behaviour for ARMA parameters," CREA Discussion Paper Series 14-07, Center for Research in Economic Analysis, University of Luxembourg.
    18. Loza-Reyes, E. & Hurn, M.A. & Robinson, A., 2014. "Classification of molecular sequence data using Bayesian phylogenetic mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 81-95.
    19. Fiorentini, G. & Planas, C. & Rossi, A., 2012. "The marginal likelihood of dynamic mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2650-2662.

  6. Ardia, David & Ospina, Juan & Giraldo, Giraldo, 2010. "Jump-Diffusion Calibration using Differential Evolution," MPRA Paper 26184, University Library of Munich, Germany, revised 25 Oct 2010.

    Cited by:

    1. Mullen, Katharine M., 2014. "Continuous Global Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i06).
    2. Jevtić, Petar & Luciano, Elisa & Vigna, Elena, 2013. "Mortality surface by means of continuous time cohort models," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 122-133.
    3. Rodrigue Oeuvray & Pascal Junod, 2013. "On time scaling of semivariance in a jump-diffusion process," Papers 1311.1122, arXiv.org.

  7. David Ardia & Lennart F. Hoogerheide, 2010. "Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations," Tinbergen Institute Discussion Papers 10-045/4, Tinbergen Institute.

    Cited by:

    1. Tore Selland Kleppe, 2016. "Adaptive Step Size Selection for Hessian-Based Manifold Langevin Samplers," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 788-805, September.
    2. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Economics Working Papers 2012-11, Christian-Albrechts-University of Kiel, Department of Economics.
    3. Jairo Fúquene & Marta Álvarez & Luis Raúl Pericchi, 2015. "A robust Bayesian dynamic linear model for Latin-American economic time series: “the Mexico and Puerto Rico cases”," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 24(1), pages 1-17, December.

  8. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.

    Cited by:

    1. Ardia, David & Lennart, Hoogerheide & Nienke, Corré, 2011. "Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation?," MPRA Paper 28259, University Library of Munich, Germany.
    2. Hoogerheide, Lennart F. & Ardia, David & Corré, Nienke, 2012. "Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?," Economics Letters, Elsevier, vol. 116(3), pages 322-325.
    3. David Ardia & Lennart Hoogerheide, 2013. "GARCH Models for Daily Stock Returns: Impact of Estimation Frequency on Value-at-Risk and Expected Shortfall Forecasts," Tinbergen Institute Discussion Papers 13-047/III, Tinbergen Institute.
    4. Galeano San Miguel, Pedro & Ausín Olivera, María Concepción & Virbickaite, Audrone, 2013. "A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection," DES - Working Papers. Statistics and Econometrics. WS ws131009, Universidad Carlos III de Madrid. Departamento de Estadística.

  9. David Ardia & Lennart Hoogerheide & Herman K. van Dijk, 2009. "To Bridge, to Warp or to Wrap? A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihoods," Tinbergen Institute Discussion Papers 09-017/4, Tinbergen Institute.

    Cited by:

    1. Bauwens, Luc & Rombouts, Jeroen V.K., 2012. "On marginal likelihood computation in change-point models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3415-3429.
    2. Lennart Hoogerheide & Richard Kleijn & Francesco Ravazzolo & Herman K. Van Dijk & Marno Verbeek, 2010. "Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 251-269.

  10. Ardia, David, 2009. "Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R," MPRA Paper 17414, University Library of Munich, Germany.

    Cited by:

    1. Shi, Yanlin & Feng, Lingbing, 2016. "A discussion on the innovation distribution of the Markov regime-switching GARCH model," Economic Modelling, Elsevier, vol. 53(C), pages 278-288.
    2. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
    3. David Ardia & Lennart F. Hoogerheide, 2010. "Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations," Tinbergen Institute Discussion Papers 10-045/4, Tinbergen Institute.
    4. Morgan Kelly & Cormac Ó Gráda, 2012. "Change points and temporal dependence in reconstructions of annual temperature : did Europe experience a little Ice Age?," Working Papers 201210, School of Economics, University College Dublin.

  11. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2009. "DEoptim: An R Package for Global Optimization by Differential Evolution," MPRA Paper 21743, University Library of Munich, Germany, revised 26 Dec 2010.

    Cited by:

    1. Scholten, Lisa & Schuwirth, Nele & Reichert, Peter & Lienert, Judit, 2015. "Tackling uncertainty in multi-criteria decision analysis – An application to water supply infrastructure planning," European Journal of Operational Research, Elsevier, vol. 242(1), pages 243-260.
    2. Nash, John C., 2014. "On Best Practice Optimization Methods in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i02).
    3. Ferlito, S. & Adinolfi, G. & Graditi, G., 2017. "Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production," Applied Energy, Elsevier, vol. 205(C), pages 116-129.
    4. Debora Gil & David Roche & Agnés Borràs & Jesús Giraldo, 2015. "Terminating evolutionary algorithms at their steady state," Computational Optimization and Applications, Springer, vol. 61(2), pages 489-515, June.
    5. Cano Berlanga, Sebastian & Giménez Gómez, José M. (José Manuel), 2016. "On Chinese stock markets: How have they evolved along time?," Working Papers 2072/267085, Universitat Rovira i Virgili, Department of Economics.
    6. Shi, Yanlin & Feng, Lingbing, 2016. "A discussion on the innovation distribution of the Markov regime-switching GARCH model," Economic Modelling, Elsevier, vol. 53(C), pages 278-288.
    7. Villacorta, Pablo J. & Verdegay, José L., 2016. "FuzzyStatProb: An R Package for the Estimation of Fuzzy Stationary Probabilities from a Sequence of Observations of an Unknown Markov Chain," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i08).
    8. Shi, Yanlin & Ho, Kin-Yip & Liu, Wai-Man, 2016. "Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 291-312.
    9. Mullen, Katharine M., 2014. "Continuous Global Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i06).
    10. Sebastián Cano-Berlanga & José-Manuel Giménez-Gómez, 2018. "On Chinese stock markets: How have they evolved over time?," Annals of Operations Research, Springer, vol. 266(1), pages 499-510, July.
    11. Bergmeir, Christoph & Molina, Daniel & Benítez, José M., 2016. "Memetic Algorithms with Local Search Chains in R: The Rmalschains Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 75(i04).
    12. Arne Henningsen & Géraldine Henningsen, 2011. "Econometric Estimation of the “Constant Elasticity of Substitution" Function in R: Package micEconCES," IFRO Working Paper 2011/9, University of Copenhagen, Department of Food and Resource Economics.
    13. Bisht Deepak & Laha, A. K., 2017. "Pricing Option on Commodity Futures under String Shock," IIMA Working Papers WP 2017-07-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    14. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    15. Ardia, David & Boudt, Kris & Carl, Peter & Mullen, Katharine M. & Peterson, Brian, 2010. "Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization," MPRA Paper 22135, University Library of Munich, Germany.
    16. Mfon Akpan & Peter Wanke & Jorge Junio Moreira Antunes & Rangan Gupta, 2018. "Unveiling the Endogenous Relationship between Technical Efficiency and Value Creation in Mergers and Acquisitions in Nigeria," Working Papers 201821, University of Pretoria, Department of Economics.
    17. Scrucca, Luca, 2013. "GA: A Package for Genetic Algorithms in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i04).
    18. Benham, Tim & Duan, Qibin & Kroese, Dirk P. & Liquet, Benoît, 2017. "CEoptim: Cross-Entropy R Package for Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i08).
    19. Ardia, David & Ospina, Juan & Giraldo, Giraldo, 2010. "Jump-Diffusion Calibration using Differential Evolution," MPRA Paper 26184, University Library of Munich, Germany, revised 25 Oct 2010.
    20. Chen, Zhongfei & Wanke, Peter & Antunes, Jorge Junio Moreira & Zhang, Ning, 2017. "Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model," Energy Economics, Elsevier, vol. 68(C), pages 89-108.
    21. Steele Christopher D. & Greenhalgh Matthew & Balding David J., 2016. "Evaluation of low-template DNA profiles using peak heights," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(5), pages 431-445, October.
    22. Joel E. Cohen & Christina Bohk & Roland Rau, 2018. "Gompertz, Makeham, and Siler models explain Taylor's law in human mortality data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(29), pages 773-842, March.
    23. Eling, Martin & Holder, Stefan, 2012. "The Value of Interest Rate Guarantees in Participating Life insurance Contracts: Status Quo and Alternative Product Design," Working Papers on Finance 1221, University of St. Gallen, School of Finance.
    24. Hartemink, Nienke & Missov, Trifon I. & Caswell, Hal, 2017. "Stochasticity, heterogeneity, and variance in longevity in human populations," Theoretical Population Biology, Elsevier, vol. 114(C), pages 107-116.
    25. Rodrigue Oeuvray & Pascal Junod, 2013. "On time scaling of semivariance in a jump-diffusion process," Papers 1311.1122, arXiv.org.
    26. Scheiter, Simon & Savadogo, Patrice, 2016. "Ecosystem management can mitigate vegetation shifts induced by climate change in West Africa," Ecological Modelling, Elsevier, vol. 332(C), pages 19-27.
    27. Eling, Martin & Holder, Stefan, 2013. "The value of interest rate guarantees in participating life insurance contracts: Status quo and alternative product design," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 491-503.

  12. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2008. "Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit," DQE Working Papers 9, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 07 Jan 2009.

    Cited by:

    1. David Ardia & Lennart Hoogerheide & Herman K. van Dijk, 2009. "To Bridge, to Warp or to Wrap? A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihoods," Tinbergen Institute Discussion Papers 09-017/4, Tinbergen Institute.
    2. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2009. "Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i03).
    3. Nalan Basturk & Lennart Hoogerheide & Anne Opschoor & Herman K. van Dijk, 2012. "The R Package MitISEM: Mixture of Student-t Distributions using Importance Sampling Weighted Expectation Maximization for Efficient and Robust Simulation," Tinbergen Institute Discussion Papers 12-096/III, Tinbergen Institute.
    4. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
    5. David Ardia & Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2010. "A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihood," Tinbergen Institute Discussion Papers 10-059/4, Tinbergen Institute.
    6. Hautsch, Nikolaus & Yang, Fuyu, 2012. "Bayesian inference in a Stochastic Volatility Nelson–Siegel model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3774-3792.
    7. Jean-Francois Richard, 2016. "Finite Gaussian Mixture Approximations to Analytically Intractable Density Kerkels," Working Paper 5980, Department of Economics, University of Pittsburgh.
    8. David, D. & Hoogerheide, L.F. & van Dijk, H.K., 2008. "The AdMit Package," Econometric Institute Research Papers EI 2008-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Anne Opschoor & Herman K. van Dijk, 2015. "The R-package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Tinbergen Institute Discussion Papers 15-042/III, Tinbergen Institute, revised 04 Jul 2017.
    10. Kleppe, Tore Selland & Liesenfeld, Roman, 2011. "Efficient high-dimensional importance sampling in mixture frameworks," Economics Working Papers 2011-11, Christian-Albrechts-University of Kiel, Department of Economics.
    11. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2008. "AdMit: Adaptive Mixtures of Student-t Distributions," DQE Working Papers 10, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 07 Jan 2009.
    12. Kleppe, Tore Selland & Liesenfeld, Roman, 2014. "Efficient importance sampling in mixture frameworks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 449-463.

  13. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2008. "AdMit: Adaptive Mixtures of Student-t Distributions," DQE Working Papers 10, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 07 Jan 2009.

    Cited by:

    1. David Ardia & Lennart Hoogerheide & Herman K. van Dijk, 2009. "To Bridge, to Warp or to Wrap? A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihoods," Tinbergen Institute Discussion Papers 09-017/4, Tinbergen Institute.
    2. Hoogerheide, Lennart & van Dijk, Herman K., 2010. "Bayesian forecasting of Value at Risk and Expected Shortfall using adaptive importance sampling," International Journal of Forecasting, Elsevier, vol. 26(2), pages 231-247, April.
    3. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2009. "Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i03).
    4. Nalan Basturk & Lennart Hoogerheide & Anne Opschoor & Herman K. van Dijk, 2012. "The R Package MitISEM: Mixture of Student-t Distributions using Importance Sampling Weighted Expectation Maximization for Efficient and Robust Simulation," Tinbergen Institute Discussion Papers 12-096/III, Tinbergen Institute.
    5. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
    6. David Ardia & Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2010. "A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihood," Tinbergen Institute Discussion Papers 10-059/4, Tinbergen Institute.
    7. Nalan Basturk & Stefano Grassi & Lennart Hoogerheide & Anne Opschoor & Herman K. van Dijk, 2015. "The R-package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Tinbergen Institute Discussion Papers 15-042/III, Tinbergen Institute, revised 04 Jul 2017.
    8. BAUWENS, Luc & DUFAYS, Arnaud & DE BACKER, Bruno, 2011. "Estimating and forecasting structural breaks in financial time series," CORE Discussion Papers 2011055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Bauwens, Luc & De Backer, Bruno & Dufays, Arnaud, 2014. "A Bayesian method of change-point estimation with recurrent regimes: Application to GARCH models," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 207-229.

  14. Ardia, David, 2007. "Bayesian Estimation of a Markov-Switching Threshold Asymmetric GARCH Model with Student-t Innovations," DQE Working Papers 6, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 08 Jul 2008.

    Cited by:

    1. Haas Markus, 2010. "Skew-Normal Mixture and Markov-Switching GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-56, September.
    2. Shi, Yanlin & Feng, Lingbing, 2016. "A discussion on the innovation distribution of the Markov regime-switching GARCH model," Economic Modelling, Elsevier, vol. 53(C), pages 278-288.
    3. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
    4. Raggi, Davide & Bordignon, Silvano, 2012. "Long memory and nonlinearities in realized volatility: A Markov switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3730-3742.
    5. Thomas Chuffart, 2013. "Selection Criteria in Regime Switching Conditional Volatility Models," Working Papers halshs-00844413, HAL.
    6. Deschamps, Philippe J., 2011. "Bayesian estimation of an extended local scale stochastic volatility model," Journal of Econometrics, Elsevier, vol. 162(2), pages 369-382, June.
    7. Kris Boudt & Jon Danielsson & Siem Jan Koopman & Andre Lucas, 2012. "Regime switches in the volatility and correlation of financial institutions," Working Paper Research 227, National Bank of Belgium.
    8. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    9. Marcel Aloy & Gilles De Truchis & Gilles Dufrénot & Benjamin Keddad, 2013. "Shift-Volatility Transmission in East Asian Equity Markets," Working Papers halshs-00935364, HAL.
    10. Haas Markus & Liu Ji-Chun, 2018. "A multivariate regime-switching GARCH model with an application to global stock market and real estate equity returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(3), pages 1-27, June.
    11. Haas, Markus & Liu, Ji-Chun, 2015. "Theory for a Multivariate Markov--switching GARCH Model with an Application to Stock Markets," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112855, Verein für Socialpolitik / German Economic Association.
    12. Wilson Ye Chen & Richard H. Gerlach, 2017. "Semiparametric GARCH via Bayesian model averaging," Papers 1708.07587, arXiv.org.
    13. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    14. BenSaïda, Ahmed, 2015. "The frequency of regime switching in financial market volatility," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 63-79.
    15. Shi, Yanlin & Ho, Kin-Yip, 2015. "Modeling high-frequency volatility with three-state FIGARCH models," Economic Modelling, Elsevier, vol. 51(C), pages 473-483.

Articles

  1. Ardia, David & Boudt, Kris & Wauters, Marjan, 2016. "The economic benefits of market timing the style allocation of characteristic-based portfolios," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 38-62.

    Cited by:

    1. Algaba, Andres & Boudt, Kris, 2017. "Generalized financial ratios to predict the equity premium," Economic Modelling, Elsevier, vol. 66(C), pages 244-257.

  2. David Ardia & Lukasz T. Gatarek & Lennart Hoogerheide & Herman K. van Dijk, 2016. "Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-19, March.

    Cited by:

    1. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

  3. Ardia, David & Boudt, Kris, 2015. "Testing equality of modified Sharpe ratios," Finance Research Letters, Elsevier, vol. 13(C), pages 97-104.

    Cited by:

    1. Ardia, David & Boudt, Kris, 2018. "The peer performance ratios of hedge funds," Journal of Banking & Finance, Elsevier, vol. 87(C), pages 351-368.

  4. Ardia, David & Hoogerheide, Lennart F., 2014. "GARCH models for daily stock returns: Impact of estimation frequency on Value-at-Risk and Expected Shortfall forecasts," Economics Letters, Elsevier, vol. 123(2), pages 187-190. See citations under working paper version above.
  5. Hoogerheide, Lennart F. & Ardia, David & Corré, Nienke, 2012. "Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?," Economics Letters, Elsevier, vol. 116(3), pages 322-325.

    Cited by:

    1. Leopoldo Catania & Nima Nonejad, 2016. "Density Forecasts and the Leverage Effect: Some Evidence from Observation and Parameter-Driven Volatility Models," Papers 1605.00230, arXiv.org, revised Nov 2016.
    2. David Ardia & Lennart Hoogerheide, 2013. "GARCH Models for Daily Stock Returns: Impact of Estimation Frequency on Value-at-Risk and Expected Shortfall Forecasts," Tinbergen Institute Discussion Papers 13-047/III, Tinbergen Institute.

  6. Ardia, David & Baştürk, Nalan & Hoogerheide, Lennart & van Dijk, Herman K., 2012. "A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3398-3414.
    See citations under working paper version above.
  7. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2011. "DEoptim: An R Package for Global Optimization by Differential Evolution," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i06).
    See citations under working paper version above.
  8. David Ardia, 2009. "Bayesian estimation of a Markov-switching threshold asymmetric GARCH model with Student-t innovations," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 105-126, March.
    See citations under working paper version above.
  9. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2009. "Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i03).
    See citations under working paper version above.

Books

  1. David Ardia, 2008. "Financial Risk Management with Bayesian Estimation of GARCH Models," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-78657-3, July.

    Cited by:

    1. Ardia, David & Lennart, Hoogerheide & Nienke, Corré, 2011. "Stock index returns’ density prediction using GARCH models: Frequentist or Bayesian estimation?," MPRA Paper 28259, University Library of Munich, Germany.
    2. Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2009. "Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i03).
    3. Monica Billio & Roberto Casarin & Anthony Osuntuyi, 2012. "Efficient Gibbs Sampling for Markov Switching GARCH Models," Working Papers 2012:35, Department of Economics, University of Venice "Ca' Foscari".
    4. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
    5. Mullen, Katharine M. & Ardia, David & Gil, David L. & Windover, Donald & Cline, James, 2009. "DEoptim: An R Package for Global Optimization by Differential Evolution," MPRA Paper 21743, University Library of Munich, Germany, revised 26 Dec 2010.
    6. David Ardia, 2009. "Bayesian estimation of a Markov-switching threshold asymmetric GARCH model with Student-t innovations," Econometrics Journal, Royal Economic Society, vol. 12(1), pages 105-126, March.
    7. Sebastián Cano-Berlanga & José-Manuel Giménez-Gómez, 2018. "On Chinese stock markets: How have they evolved over time?," Annals of Operations Research, Springer, vol. 266(1), pages 499-510, July.
    8. Deschamps, Philippe J., 2011. "Bayesian estimation of an extended local scale stochastic volatility model," Journal of Econometrics, Elsevier, vol. 162(2), pages 369-382, June.
    9. Ardia, David, 2009. "Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R," MPRA Paper 17414, University Library of Munich, Germany.
    10. Deschamps, Philippe J., 2011. "Bayesian Estimation of Generalized Hyperbolic Skewed Student GARCH Models," DQE Working Papers 16, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 09 Jun 2012.
    11. Tafakori, Laleh & Pourkhanali, Armin & Fard, Farzad Alavi, 2018. "Forecasting spikes in electricity return innovations," Energy, Elsevier, vol. 150(C), pages 508-526.
    12. Tetsuya Takaishi, 2009. "Markov Chain Monte Carlo on Asymmetric GARCH Model Using the Adaptive Construction Scheme," Papers 0909.1478, arXiv.org.
    13. T. R. Santos, 2018. "A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach," Papers 1809.01489, arXiv.org.
    14. Romero, Eva & Rodríguez Bernal, M. T. & Marín Díazaraque, Juan Miguel, 2013. "Data cloning estimation of GARCH and COGARCH models," DES - Working Papers. Statistics and Econometrics. WS ws132723, Universidad Carlos III de Madrid. Departamento de Estadística.

More information

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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 22 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.
  1. NEP-ECM: Econometrics (7) 2007-04-28 2008-07-05 2009-03-22 2010-06-04 2010-11-06 2011-02-26 2014-03-22. Author is listed
  2. NEP-ETS: Econometric Time Series (7) 2007-04-28 2009-09-26 2010-06-04 2011-01-30 2013-06-04 2014-03-22 2015-04-25. Author is listed
  3. NEP-RMG: Risk Management (7) 2009-09-19 2013-03-30 2013-06-04 2013-06-04 2014-03-22 2015-04-25 2016-11-27. Author is listed
  4. NEP-FOR: Forecasting (4) 2007-04-28 2011-01-30 2013-06-04 2016-11-27
  5. NEP-ORE: Operations Research (4) 2009-03-22 2010-04-11 2010-04-24 2014-03-22
  6. NEP-CMP: Computational Economics (2) 2010-04-11 2010-04-24
  7. NEP-EVO: Evolutionary Economics (2) 2010-04-11 2010-04-24
  8. NEP-FMK: Financial Markets (2) 2013-06-04 2013-09-26
  9. NEP-CFN: Corporate Finance (1) 2007-08-08
  10. NEP-EFF: Efficiency & Productivity (1) 2013-09-26
  11. NEP-IFN: International Finance (1) 2011-02-26
  12. NEP-MON: Monetary Economics (1) 2011-02-26

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