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DTSTART:20220327T010000
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DTSTART;TZID=Europe/Helsinki:20220609T113000
DTEND;TZID=Europe/Helsinki:20220609T123000
DTSTAMP:20260713T000347
CREATED:20220530T105842Z
LAST-MODIFIED:20220607T123818Z
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SUMMARY:Sullivan HUE (Marseille University)  "GAM(L)A: AN ECONOMETRIC MODEL FOR INTERPRETABLE MACHINE LEARNING"
DESCRIPTION:The Financial Econometrics Seminar: \nTime: 11:30 pm\nDate: 9th of June 2022\nRoom 3001+ zoom \nSullivan HUE (Marseille University) “GAM(L)A: AN ECONOMETRIC MODEL FOR INTERPRETABLE MACHINE LEARNING” \nAbstract :Despite their high predictive performance\, random forest and gradient boosting are often considered as black boxes or uninterpretable models which has raised concerns from practitioners and regulators. As an alternative\, we propose in this paper to use partial linear models that are inherently interpretable. Specifically\, this article introduces GAM-lasso (GAMLA) and GAM-autometrics (GAMA)\, denoted as GAM(L)A in short. GAM(L)A combines parametric and non-parametric functions to accurately capture linearities and non-linearities prevailing between dependent and explanatory variables\, and a variable selection procedure to control for overfitting issues. Estimation relies on a two-step procedure building upon the double residual method. We illustrate the predictive performance and interpretability of GAM(L)A on a regression and a classification problem. The results show that GAM(L)A outperforms parametric models augmented by quadratic\, cubic and interaction effects. Moreover\, the results also suggest that the performance of GAM(L)A is not significantly different from that of random forest and gradient boosting.\nJoint work with E. Flachaire\, G. Hacheme and S. Laurent\n\n \nOrganizers:\n\nJean-Michel ZAKOIAN  (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/sullivan-hue-marseille-university-gamla-an-econometric-model-for-interpretable-machine-learning/
CATEGORIES:Finance-Insurance,Financial Econometrics
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DTSTART;TZID=Europe/Helsinki:20220609T121500
DTEND;TZID=Europe/Helsinki:20220609T133000
DTSTAMP:20260713T000347
CREATED:20220530T105604Z
LAST-MODIFIED:20220706T102710Z
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SUMMARY:Arnaud DUFAYS (EDHEC)  "FACTOR DYNAMICS\, RISK PREMIA\, AND HIGHER MOMENTS IN MULTI-FACTOR OPTION PRICING MODELS"
DESCRIPTION:The Financial Econometrics Seminar: \nTime: 12:15 – 13.30 pm\nDate: 9th of June 2022\nRoom 3001+ zoom \nArnaud DUFAYS (EDHEC) “FACTOR DYNAMICS\, RISK PREMIA\, AND HIGHER MOMENTS IN MULTI-FACTOR OPTION PRICING MODELS” \nAbstract :The cross-section of options holds great promise for identifying return distributions and risk premiums\, but estimating dynamic option valuation models with latent state variables is challenging when using large option panels. We propose a particle MCMC framework with a novel filtering approach to solve this computational problem. Our method allows for estimating state-of-the-art models and beyond\, using large option panels. This leads to more precise estimates of model parameters and risk premiums. We illustrate our approach by extending the Heston option pricing model to a model exhibiting up to three volatility factors and jumps in returns. In terms of option pricing\, we show that a model with three volatility factors is necessary for fitting long-term maturities. Importantly\, the risk premiums of the latter model are also flexible and more in line with non-parametric estimates than alternative models we consider. \nJoint work with K. Jacobs and J. Rombouts \nOrganizers:\n\nJean-Michel ZAKOIAN  (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/arnaud-dufays-edhec-factor-dynamics-risk-premia-and-higher-moments-in-multi-factor-option-pricing-models/
CATEGORIES:Finance-Insurance,Financial Econometrics
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