Dominique Guégan
Présentation
Publications
Publications
Fintech and BlockchainReading seminars 2018-2019, University Ca Foscari, Apr 2019, Venise, Italy |
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Risks and Blockchain1st International Symposium on Entrepreneurship, Blockchain and Crypto-Finance, UTC Tunis, Apr 2019, Tunis, Tunisia |
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Big Data, Artificial Intelligence and BlockchainBig Data, Artificial Intelligence and Blockchain, Université Saint-Louis du Sénégal, Mar 2019, Sénégal, Senegal |
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Operational risk in blockchain paymentsFin-Tech HO2020 European Project: FINTECH Risk Management, University of Pavia, Feb 2019, Pavie, Italy |
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Blockchain seminar: Risk and BlockchainBlockchain seminar: Risk and Blockchain, Conservatoire des Arts et Métiers (CNAM), Jan 2019, Paris, France |
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Credit Risk Analysis using Machine and Deep Learning ModelsCredit Risk Analysis Using Machine and Deep Learning Models, Università degli Studi di Padova, Jan 2019, Padoue, Italy |
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Credit Risk Analysis Using machine and Deep Learning Models3small Business Risk, Financial Regulation and Big Data Analytics, Sep 2018, Palazzo Franchetti - Venice, Italy |
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Measuring risk an explosive environmentForecasting Financial Markets (FFM), Sep 2018, Oxford, United Kingdom |
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A new token: the CommodCoin. What could be its interest for financial market? A macro-economic modellingDigital, Innovation, Entrepreneurship and Financing, Jun 2018, Lyon, France |
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Initial Token Offerings (ITOs) and corporate governanceForecasting Financial Markets (FFM), Sep 2018, Oxford, United Kingdom |
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Measuring risk in an explosive environmentVietnam Symposium in Banking and Finance (VSBF), Oct 2018, Hué City, Vietnam |
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Assessment of proxy-hedging in jet-fuel marketsIRMBAM 2018, Jul 2018, Nice, France |
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Regulatory Learning: Credit Scoring Application of Machine LearningDMBD 2017, Jul 2017, Fukuoka, Japan |
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Impact of multimodality of distributions on VaR and ES calculation10th International conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2017), Dec 2017, Senate House - Londres, United Kingdom |
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Bitcoin and the challenge for regulationVietnam Symposium in Banking and Finance, Oct 2017, Ho Chi Minh City, Vietnam |
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Financial Regulation: More Accurate Measurements for Control Enhancements and the Capture of the Intrinsic Uncertainty of the VaRvsbf: 2016 Vietnam Symposium in Banking and Finance, Nov 2016, Hanoi, Vietnam |
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Risk Measures at Risk- Are we missing the point? Discussions around sub-additivity and distortionConference on Banking and Finance, Sep 2016, Porthmouth, United Kingdom |
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Pricing alternatives in incomplete markets. An application for Carbon allowances2011 International Conference on Information and Finance (ICIF 2011), Nov 2011, Malaysia |
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Derivative pricing and hedging on carbon market2009 International Conference on Computer and Development, Feb 2009, Kota Kinabalu, Malaysia |
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Fractional seasonality: Models and Application to Economic Activity in the Euro AreaConference on Seasonality, Seasonal Adjustment and their Implications for Short-Term Analysis and Forecasting, May 2006, Luxembourg. pp.137 - 153 |
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A k- factor GIGARCH process : estimation and application to electricity market spot prices,Probabilistic methods applied to power systems, Jul 2004, United States. pp.1 - 7 |
Comparison of several methods to predict chaotic time seriesInternational Conference on Complex Systems, 1997, Munich, Germany. pp.3793 - 3797 |
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prediction in chaotic time series: methods and comparisons using simulations5th International ECASP Conference, 1997, Prague, Czech Republic. pp.215 - 218 |
Risk MeasurementSpringer. 215 p., 2019 |
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Future Perspectives in Risk Models and FinanceSpringer, 2015, 978-3-319-07524-2. ⟨10.1007/978-3-319-07524-2⟩ |
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A time series approach to option pricing: Models, Methods and Empirical PerformancesSpringer, 2015 |
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Les chaos en finance: approche statistiqueEconomica, pp.465, 2003, Statistique mathématique et probabilité, Paul Deheuvels |
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Analyser les séries chronologiques avec S-Plus: une approche paramétriquePresses Universitaires de renne, pp.147, 2003 |
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Séries chronologiques non linéaires à temps discretEconomica, pp.308, 1994, Statistique mathématique et probabilité |
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Stress Testing Engineering: The Real Risk Measurement?Alain Bensoussan, Dominique Guégan et Charles S. Tapiero. Future Perspectives in Risk Models and Finance, Springer, pp.89-124, 2015, 978-3-319-07523-5. ⟨10.1007/978-3-319-07524-2_3⟩ |
Distorsion Risk Measure or the Transformation of Unimodal Distributions into Multimodal FunctionsAlain Bensoussan, Dominique Guégan et Charles S. Tapiero. Future Perspectives in Risk Models and Finance, Springer, pp.71-88, 2015, 978-3-319-07523-5. ⟨10.1007/978-3-319-07524-2_2⟩ |
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Nonlinear Dynamics and Wavelets for Business Cycle AnalysisWavelet Applications in Economics and Finance, 2014, 978-3-319-07060-5. ⟨10.1007/978-3-319-07061-2_4⟩ |
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Non-stationary sample and meta-distributionA. Basu, T. Samanta, A. Sen Gupta. ISI Platinum Jubilee volume: statistical science and interdisciplinary research (International Conference of Statistical Paradigms - Recent Advances and Reconciliations), Word Scientific Publishing, à paraître, 2013 |
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Predicting chaos with Lyapunov exponents: zero plays no role in forecasting chaotic systemsE. Tielo-Cuantle. Chaotic Systems, InTech Publishers, 25-38 (chapitre 2), 2011 |
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Contagion Between the Financial Sphere and the Real Economy. Parametric and non Parametric Tools: A ComparisonCatherine Kyrtsou, Costas Vorlow. Progress in financial market research, NOVA publishers, pp.233-254, 2011 |
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Value at Risk Computation in a Non-Stationary SettingGreg N. Gregoriou, Carsten S. Wehn, Christian Hoppe. Handbook on Model Risk : Measuring, managing and mitigating model risk, lessons from financial crisis, John Wiley, 431-454 - chapter 19, 2010 |
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Alternative methods for forecasting GDPR. Barnett, F. Jawady. Nonlinear Modeling of Economic and Financial Time-Series, Emerald Publishers, Chapiter 5 (29 p.), 2010, Series International Symposia in Economic Theory and Econometrics - n°21 |
Former les analystes et opérateurs financiersGaël Giraud, Cécile Renouard. 20 propositions pour réformer le capitalisme, Flammarion, 95-104 (chapitre 6), 2009 |
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Mettre les mathématiques financières au service du réelGaël Giraud, Cécile Renouard. 20 propositions pour réformer le capitalisme, Flammarion, 141-152 (chapitre 10), 2009 |
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Local Lyapunov Exponents: A new way to predict chaotic systemsChristos H. Skiadas, Ioannis Dimotikalis, Charilaos Skiadas. Topics on Chaotic Systems: Selected papers from CHAOS 2008, International Conference, World Scientific Publishing, pp.158-185, 2009 |
Derivative pricing and hedging on carbon market2009 International Conference on Computer and Development, Kota Kinanalu (Malaysia), pp.130-133, 2009 |
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Fractional and seasonal filteringJ.L. Mazi. Proceeding Book on the Conference Seasonality, Seasonal adjustment and its implication for short term analysis and forecasting, Eurostat, pp.121-132, 2008 |
Synthetic CDO Squared Pricing MethodologiesGreg N. Gregoriou, Paul U. Ali. Credit Derivatives Handbook - Global Perspectives, Innovations, and Market Drivers, MCGraw Hill, 361-377 (chapiter 16), 2008 |
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Real-time detection of the business cycle using SETAR modelsG.L. Mazzi and G. Savio. Growth and Cycle in the Euro-zone, Palgrave MacMillan, New York, pp.221-232, 2006 |
Forecasting with non Gaussian long memory processesProc. XXXV ème Journées de Stat., Lyon, ASU, pp.285 - 288, 2003 |
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Estimation de la tail dependance à l'aide de la notion de copuleProc. XXXV ème Journées de Stat., Lyon, ASU, pp.289 - 292, 2003 |
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Some remarks on the statistical modelling of chaotic systemsAlistair I. Mees. Nonlinear Dynamics and Statistics, Birkhäuser Boston, 400 - Chapitre 5, 2001 |
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Comparison of parameter estimation methods in cyclical long memory time seriesChristian L. Dunis, Allan Timmermann, John E. Moody. Developments in Forecast Combination and Portfolio Choice, Wiley, pp.330, 2001 |
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Forecasting financial time series with generalized long memory processesChristian Dunis. Advances in Quantitative Asset Management, Kluver Academic Press, chapter 14, 2000, Studies in computational finance |
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Non parametric forecasting techniques for mixing chaotic time seriesAles Prochazka, N.G. Kingsbury, P.J.W. Payner, J. Uhlir. Signal Analysis and Prediction, Birkhäuser Boston, chapter 25, 1998 |
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Stochastic or chaotic dynamics in high frequency financial dataChristian L. Dunis, Bin Zhou. Nonlinear Modelling of High Frequency Financial Time Series, Wiley, chapter 5, 1998 |
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Some Recent Developments in Non Linear Time SeriesAtti del Convegno in Honore di Oliviero Lessi, Universita degli Studi di padova, pp.17-38, 1998 |
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Nonparametric Methods for Time Series and Dynamical SystemsGutti Jogesh Babu, Eric D. Feigelson. Statistical Challenges in Modern Astronomy II, Springer, 303-320 chapter 17, 1997 |
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From data to modelsT. Subba Rao, M.B. Priestly, O. Lessi. Applications of Time Series Analysis in Astronomy and Meteorology, Chapman & Hall, chapter 8, 1997 |
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An Omnibus Test to Detect Time-Heterogeneity in Time Series2012 |
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Viewing Risk Measures as information2012 |
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Breaks or long memory behaviour : an empirical investigation2012 |
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Changing-regime volatility : A fractionally integrated SETAR model2006 |