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Giovanni Montana

Personal Details

First Name:Giovanni
Middle Name:
Last Name:Montana
Suffix:
RePEc Short-ID:pmo385
https://warwick.ac.uk/fac/sci/wmg/research/digital/datascience/people

Affiliation

Department of Statistics
University of Warwick

Coventry, United Kingdom
http://www.warwick.ac.uk/go/statistics
RePEc:edi:dswaruk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Kostas Triantafyllopoulos & Giovanni Montana, 2008. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Papers 0808.1710, arXiv.org, revised May 2009.
  2. Kostas Triantafyllopoulos & Giovanni Montana, 2007. "Fast estimation of multivariate stochastic volatility," Papers 0708.4376, arXiv.org, revised Nov 2007.
  3. Giovanni Montana & Kostas Triantafyllopoulos & Theodoros Tsagaris, 2007. "Flexible least squares for temporal data mining and statistical arbitrage," Papers 0709.3884, arXiv.org.

Articles

  1. René Gaudoin & Giovanni Montana & Simon Jones & Paul Aylin & Alex Bottle, 2015. "Classifier calibration using splined empirical probabilities in clinical risk prediction," Health Care Management Science, Springer, vol. 18(2), pages 156-165, June.
  2. Lakshmana Ayaru & Petros-Pavlos Ypsilantis & Abigail Nanapragasam & Ryan Chang-Ho Choi & Anish Thillanathan & Lee Min-Ho & Giovanni Montana, 2015. "Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-14, July.
  3. Petros-Pavlos Ypsilantis & Musib Siddique & Hyon-Mok Sohn & Andrew Davies & Gary Cook & Vicky Goh & Giovanni Montana, 2015. "Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-18, September.
  4. Cozzini, Alberto & Jasra, Ajay & Montana, Giovanni & Persing, Adam, 2014. "A Bayesian mixture of lasso regressions with t-errors," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 84-97.
  5. Sim Aaron & Tsagkrasoulis Dimosthenis & Montana Giovanni, 2013. "Random forests on distance matrices for imaging genetics studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(6), pages 757-786, December.
  6. Matt Silver & Peng Chen & Ruoying Li & Ching-Yu Cheng & Tien-Yin Wong & E-Shyong Tai & Yik-Ying Teo & Giovanni Montana, 2013. "Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts," PLOS Genetics, Public Library of Science, vol. 9(11), pages 1-28, November.
  7. Silver Matt & Montana Giovanni & Alzheimer's Disease Neuroimaging Initiative, 2012. "Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-43, January.
  8. K. Triantafyllopoulos & G. Montana, 2011. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Computational Management Science, Springer, vol. 8(1), pages 23-49, April.
  9. Maurice Berk & Giovanni Montana, 2009. "Functional modelling of microarray time series with covariate curves," Statistica, Department of Statistics, University of Bologna, vol. 69(2), pages 159-186.
  10. Kendall, Wilfrid S. & Montana, Giovanni, 2002. "Small sets and Markov transition densities," Stochastic Processes and their Applications, Elsevier, vol. 99(2), pages 177-194, June.

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. Kostas Triantafyllopoulos & Giovanni Montana, 2008. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Papers 0808.1710, arXiv.org, revised May 2009.

    Cited by:

    1. Tim Leung & Brian Ward, 2015. "The golden target: analyzing the tracking performance of leveraged gold ETFs," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(3), pages 278-297, August.
    2. David S. Sun & Shih-Chuan Tsai & Wei Wang, 2013. "Behavioral Investment Strategy Matters: A Statistical Arbitrage Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S3), pages 47-61, July.
    3. Trent Spears & Stefan Zohren & Stephen Roberts, 2023. "On statistical arbitrage under a conditional factor model of equity returns," Papers 2309.02205, arXiv.org.
    4. Clegg, Matthew & Krauss, Christopher, 2016. "Pairs trading with partial cointegration," FAU Discussion Papers in Economics 05/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    5. Tim Leung & Xin Li, 2014. "Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit," Papers 1411.5062, arXiv.org, revised May 2015.
    6. Kevin Guo & Tim Leung, 2016. "Understanding the Tracking Errors of Commodity Leveraged ETFs," Papers 1610.09404, arXiv.org.
    7. Focardi, Sergio M. & Fabozzi, Frank J. & Mitov, Ivan K., 2016. "A new approach to statistical arbitrage: Strategies based on dynamic factor models of prices and their performance," Journal of Banking & Finance, Elsevier, vol. 65(C), pages 134-155.
    8. Bolgun, Evren & Kurun, Engin & Guven, Serhat, 2009. "Dynamic Pairs Trading Strategy For The Companies Listed In The Istanbul Stock Exchange," MPRA Paper 19887, University Library of Munich, Germany.
    9. João Frois Caldeira & Gulherme Valle Moura, 2013. "Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy," Brazilian Review of Finance, Brazilian Society of Finance, vol. 11(1), pages 49-80.
    10. Phélippé-Guinvarc'h, Martial & Cordier, Jean, 2015. "Machine Learning for Semi-Strong Efficiency Test of Inter-Market Wheat Futures," MPRA Paper 68410, University Library of Munich, Germany.
    11. Kevin Guo & Tim Leung & Brian Ward, 2019. "How to mine gold without digging," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-30, March.
    12. Boming Ning & Kiseop Lee, 2024. "Advanced Statistical Arbitrage with Reinforcement Learning," Papers 2403.12180, arXiv.org.
    13. Kiseop Lee & Tim Leung & Boming Ning, 2023. "A Diversification Framework for Multiple Pairs Trading Strategies," Risks, MDPI, vol. 11(5), pages 1-18, May.
    14. Matthew Clegg & Christopher Krauss, 2018. "Pairs trading with partial cointegration," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 121-138, January.
    15. Adrian Pizzinga & Marcelo Fernandes, 2021. "Extensions to the invariance property of maximum likelihood estimation for affine‐transformed state‐space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 355-371, May.
    16. Yerkin Kitapbayev & Tim Leung, 2018. "Mean Reversion Trading With Sequential Deadlines And Transaction Costs," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 1-22, February.
    17. Boming Ning & Prakash Chakraborty & Kiseop Lee, 2023. "Optimal Entry and Exit with Signature in Statistical Arbitrage," Papers 2309.16008, arXiv.org, revised Mar 2024.
    18. 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.
    19. Fernando Caneo & Werner Kristjanpoller, 2021. "Improving statistical arbitrage investment strategy: Evidence from Latin American stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4424-4440, July.

  2. Giovanni Montana & Kostas Triantafyllopoulos & Theodoros Tsagaris, 2007. "Flexible least squares for temporal data mining and statistical arbitrage," Papers 0709.3884, arXiv.org.

    Cited by:

    1. Zsolt Darvas & Balẳ Varga, 2014. "Inflation persistence in central and eastern European countries," Applied Economics, Taylor & Francis Journals, vol. 46(13), pages 1437-1448, May.
    2. Josipa VIŠIC & Blanka ŠKRABIC, 2010. "Determinants of Incoming Cross-Border M&A: Evidence from European Transition Economies," EcoMod2010 259600168, EcoMod.
    3. Matthew J. Lebo & Janet M. Box‐Steffensmeier, 2008. "Dynamic Conditional Correlations in Political Science," American Journal of Political Science, John Wiley & Sons, vol. 52(3), pages 688-704, July.
    4. Uliha, Gábor, 2016. "Az olajár gyengülő makrogazdasági hatásai. Két versengő elmélet szintézise [Weakening macroeconomic effects of the oil price. A synthesis of two competing theories]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(7), pages 787-818.
    5. Jeff Stephenson & Bruce Vanstone & Tobias Hahn, 2021. "A Unifying Model for Statistical Arbitrage: Model Assumptions and Empirical Failure," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 943-964, December.
    6. Evžen Kočenda & Balázs Varga, 2018. "The Impact of Monetary Strategies on Inflation Persistence," International Journal of Central Banking, International Journal of Central Banking, vol. 14(4), pages 229-274, September.
    7. Zsuzsanna Zsibók & Balázs Varga, 2012. "Inflation Persistence in Hungary: a Spatial Analysis," Working Papers 1203, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    8. K. Triantafyllopoulos & G. Montana, 2011. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Computational Management Science, Springer, vol. 8(1), pages 23-49, April.
    9. Theodoros Tsagaris & Ajay Jasra & Niall Adams, 2010. "Robust and Adaptive Algorithms for Online Portfolio Selection," Papers 1005.2979, arXiv.org.
    10. Zsolt Darvas & Balázs Varga, 2012. "Uncovering Time-Varying Parameters with the Kalman-Filter and the Flexible Least Squares: a Monte Carlo Study," Working Papers 1204, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    11. 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.
    12. Sheunesu Zhou, 2021. "Examining the Sources of Sovereign Risk for South Africa: A Time Varying Flexible Least Squares Approach," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 9(1), pages 29-45.
    13. Kuethe, Todd H. & Foster, Kenneth A. & Florax, Raymond J.G.M., 2008. "A Spatial Hedonic Model with Time-Varying Parameters: A New Method Using Flexible Least Squares," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6306, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

Articles

  1. Lakshmana Ayaru & Petros-Pavlos Ypsilantis & Abigail Nanapragasam & Ryan Chang-Ho Choi & Anish Thillanathan & Lee Min-Ho & Giovanni Montana, 2015. "Prediction of Outcome in Acute Lower Gastrointestinal Bleeding Using Gradient Boosting," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-14, July.

    Cited by:

    1. Luca Di Persio & Nicola Fraccarolo, 2023. "Energy Consumption Forecasts by Gradient Boosting Regression Trees," Mathematics, MDPI, vol. 11(5), pages 1-17, February.
    2. Khudri, Md Mohsan & Hussey, Andrew, 2024. "Breastfeeding and Child Development Outcomes across Early Childhood and Adolescence: Doubly Robust Estimation with Machine Learning," IZA Discussion Papers 17080, Institute of Labor Economics (IZA).

  2. Cozzini, Alberto & Jasra, Ajay & Montana, Giovanni & Persing, Adam, 2014. "A Bayesian mixture of lasso regressions with t-errors," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 84-97.

    Cited by:

    1. Lee, Kuo-Jung & Feldkircher, Martin & Chen, Yi-Chi, 2021. "Variable selection in finite mixture of regression models with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    2. Zhang, Yifan & Fong, Duncan K.H. & DeSarbo, Wayne S., 2021. "A generalized ordinal finite mixture regression model for market segmentation," International Journal of Research in Marketing, Elsevier, vol. 38(4), pages 1055-1072.

  3. Matt Silver & Peng Chen & Ruoying Li & Ching-Yu Cheng & Tien-Yin Wong & E-Shyong Tai & Yik-Ying Teo & Giovanni Montana, 2013. "Pathways-Driven Sparse Regression Identifies Pathways and Genes Associated with High-Density Lipoprotein Cholesterol in Two Asian Cohorts," PLOS Genetics, Public Library of Science, vol. 9(11), pages 1-28, November.

    Cited by:

    1. Artem Sokolov & Daniel E Carlin & Evan O Paull & Robert Baertsch & Joshua M Stuart, 2016. "Pathway-Based Genomics Prediction using Generalized Elastic Net," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-23, March.

  4. Silver Matt & Montana Giovanni & Alzheimer's Disease Neuroimaging Initiative, 2012. "Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-43, January.

    Cited by:

    1. Binder Harald & Müller Tina & Schwender Holger & Golka Klaus & Steffens Michael & Hengstler Jan G. & Ickstadt Katja & Schumacher Martin, 2012. "Cluster-Localized Sparse Logistic Regression for SNP Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-31, August.

  5. K. Triantafyllopoulos & G. Montana, 2011. "Dynamic modeling of mean-reverting spreads for statistical arbitrage," Computational Management Science, Springer, vol. 8(1), pages 23-49, April.
    See citations under working paper version above.
  6. Kendall, Wilfrid S. & Montana, Giovanni, 2002. "Small sets and Markov transition densities," Stochastic Processes and their Applications, Elsevier, vol. 99(2), pages 177-194, June.

    Cited by:

    1. van Lieshout, M.N.M. & Stoica, R.S., 2006. "Perfect simulation for marked point processes," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 679-698, November.
    2. Dedecker, Jérôme & Merlevède, Florence & Rio, Emmanuel, 2024. "Deviation inequalities for dependent sequences with applications to strong approximations," Stochastic Processes and their Applications, Elsevier, vol. 174(C).

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