Zobrazeno 1 - 10 of 19 pro vyhledávání: '"Sessi Tokpavi"'
1
Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects
Autor: Sessi Tokpavi, Elena Ivona Dumitrescu, Christophe Hurlin, Sullivan Hué
Publikováno v: European Journal of Operational Research
European Journal of Operational Research, Elsevier, 2022, 297 (3), pp.1178-1192. ⟨10.1016/j.ejor.2021.06.053⟩
European Journal of Operational Research, Elsevier, 2022, 297 (3), pp.1178-1192
In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in th
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::11f6dc00fb61cce05fae24fcca625035
https://doi.org/10.1016/j.ejor.2021.06.053
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2
Measuring network systemic risk contributions: A leave-one-out approach
Autor: Yannick Lucotte, Sessi Tokpavi, Sullivan Hué
Publikováno v: Journal of Economic Dynamics and Control
Journal of Economic Dynamics and Control, Elsevier, 2019, 100, pp.86-114. ⟨10.1016/j.jedc.2018.12.001⟩
Granger-causality measures of interconnectedness between financial institutions are useful indicators of systemic risk (Billio et al., 2012) [Journal of Financial Economics], as they help in evaluating how far the distress of one institution is disse
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df94a57177979085f4c550c8c4b98b9f
https://doi.org/10.1016/j.jedc.2018.12.001
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3
Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds
Autor: Sullivan Hué, Sessi Tokpavi, Christophe Hurlin, Elena Ivona Dumitrescu
In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in th
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19f38ae622c42784af02f693b6fd19d8
https://hal.science/hal-02507499v3
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4
Smart Alpha: active management with unstable and latent factors
Autor: Christophe Boucher, P. Kouontchou, A. Jasinski, Sessi Tokpavi
Publikováno v: Quantitative Finance
Quantitative Finance, 2021
Quantitative Finance, Taylor & Francis (Routledge), 2021
Factor investing has attracted increasing interest in the investment industry because purely active and passive solutions have underperformed. Its success depends critically on identifying the factors involved and timing this well, but this is hard t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::262c485c89d84eae922fa95056a90118
https://hal.science/hal-03130957
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6
Stocks and Bonds: Flight-to-Safety for Ever?
Autor: Christophe Boucher, Sessi Tokpavi
Publikováno v: Journal of International Money and Finance
Journal of International Money and Finance, Elsevier, 2019
Journal of International Money and Finance, 2019
This paper gives new insights about flight-to-safety from stocks to bonds, asking whether the strength of this phenomenon remains the same in the current environment of low yields. The motivations lie in the conjecture that when yields are low, the t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ffc114b9b92a62b895141bd274e0b67
https://hal.archives-ouvertes.fr/hal-02067096/file/S0261560618305722.pdf
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7
A Nonparametric Test for Granger Causality in Distribution With Application to Financial Contagion
Autor: Sessi Tokpavi, Bertrand Candelon
Publikováno v: Journal of Business and Economic Statistics
Journal of Business and Economic Statistics, Taylor & Francis, 2016, 34 (2), pp.240-253. ⟨10.1080/07350015.2015.1026774⟩
Journal of Business and Economic Statistics, 2016, 34 (2), pp.240-253. ⟨10.1080/07350015.2015.1026774⟩
Journal of Business & Economic Statistics, 34(2), 240-253. Taylor and Francis
This article introduces a kernel-based nonparametric inferential procedure to test for Granger causality in distribution. This test is a multivariate extension of the kernel-based Granger causality test in tail event. The main advantage of this test
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6464a52b1011483f9e84b6233313d878
https://doi.org/10.1080/07350015.2015.1026774
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8
Forecasting High-Frequency Risk Measures
Autor: Denisa Banulescu, Christophe Hurlin, Sessi Tokpavi, Gilbert Colletaz
Publikováno v: Journal of Forecasting. 35:224-249
This article proposes intraday high-frequency risk (HFR) measures for market risk in the case of irregularly spaced high-frequency data. In this context, we distinguish three concepts of value-at-risk (VaR): the total VaR, the marginal (or per-time-u
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ee16ea2313f1908456b7c4f99119b801
https://doi.org/10.1002/for.2374
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10
Testing for Granger causality in distribution tails: An application to oil markets integration
Autor: Marc Joëts, Sessi Tokpavi, Bertrand Candelon
Publikováno v: Economic Modelling, 31, 276-285. Elsevier
This paper proposes an original procedure which allows for testing of Granger-causality for multiple risk levels across tail distributions, hence extending the procedure proposed by Hong et al. (2009). Asymptotic and finite sample properties of the t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8219e4033b01f4e3da1ec674e7b1bcb1
https://doi.org/10.1016/j.econmod.2012.11.049
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