Arthur Charpentier presented on actuarial pricing games at the University of Michigan in June 2017. He discussed how insurance works by pooling risks from many policyholders to cover the losses of a few. With imperfect information, insurers can segment risks by observable factors and set premiums accordingly. This allows some risks to be shared across segments while others are kept by individual policyholders or segments. In a competitive market, insurers will use different statistical models and sets of rating variables to set premiums, and policyholders will choose the insurer with the best price given their risk characteristics.
This document summarizes a presentation on testing for volatility transmission between international markets using high frequency data. It discusses using realized volatility to estimate true latent volatility processes while controlling for jumps and microstructure noise. The presentation focuses on testing for transmission of only extreme or large volatility values between markets. A quantile model is used to define extreme periods, and cross-covariances are computed to test for non-causality between markets' extreme periods using Ljung-Box statistics. Simulations are performed based on a three-regime smooth-transition model to assess the test in finite samples.
This document summarizes a presentation on data science and big data for actuaries given by Arthur Charpentier. It discusses the history of data collection and analysis. It provides an overview of big data, including definitions of volume, variety and velocity. It also covers topics like unsupervised learning techniques including principal component analysis and cluster analysis. Computational issues for large-scale data analysis using techniques like parallelization are also summarized.
1) The document discusses how statistical learning techniques from other disciplines can inform econometric modeling and central bank policymaking.
2) It covers topics like high-dimensional data analysis, nonparametric regression, causal inference challenges, and model selection methods.
3) The key message is that econometrics can benefit from adopting techniques from fields like machine learning and statistics to develop more flexible, data-driven models.
Arthur Charpentier presents a model for insurance equilibria covering natural catastrophes in heterogeneous regions. The model considers both private insurance companies with limited liability and possible government intervention. It examines a one region model with homogeneous agents and a common shock model for natural disaster risks. Finally, it develops a two region model to analyze equilibriums when considering strategic decisions between regions.
This document discusses quantile and expectile regressions. It begins by explaining the differences between the econometrics and machine learning approaches. It then introduces quantile and expectile regressions as generalizations of ordinary least squares regression that minimize different loss functions. Finally, it discusses properties of quantile and expectile regressions such as their elicitable measures and how they can be estimated.
Arthur Charpentier presented on actuarial pricing games at the University of Michigan in June 2017. He discussed how insurance works by pooling risks from many policyholders to cover the losses of a few. With imperfect information, insurers can segment risks by observable factors and set premiums accordingly. This allows some risks to be shared across segments while others are kept by individual policyholders or segments. In a competitive market, insurers will use different statistical models and sets of rating variables to set premiums, and policyholders will choose the insurer with the best price given their risk characteristics.
This document summarizes a presentation on testing for volatility transmission between international markets using high frequency data. It discusses using realized volatility to estimate true latent volatility processes while controlling for jumps and microstructure noise. The presentation focuses on testing for transmission of only extreme or large volatility values between markets. A quantile model is used to define extreme periods, and cross-covariances are computed to test for non-causality between markets' extreme periods using Ljung-Box statistics. Simulations are performed based on a three-regime smooth-transition model to assess the test in finite samples.
This document summarizes a presentation on data science and big data for actuaries given by Arthur Charpentier. It discusses the history of data collection and analysis. It provides an overview of big data, including definitions of volume, variety and velocity. It also covers topics like unsupervised learning techniques including principal component analysis and cluster analysis. Computational issues for large-scale data analysis using techniques like parallelization are also summarized.
1) The document discusses how statistical learning techniques from other disciplines can inform econometric modeling and central bank policymaking.
2) It covers topics like high-dimensional data analysis, nonparametric regression, causal inference challenges, and model selection methods.
3) The key message is that econometrics can benefit from adopting techniques from fields like machine learning and statistics to develop more flexible, data-driven models.
Arthur Charpentier presents a model for insurance equilibria covering natural catastrophes in heterogeneous regions. The model considers both private insurance companies with limited liability and possible government intervention. It examines a one region model with homogeneous agents and a common shock model for natural disaster risks. Finally, it develops a two region model to analyze equilibriums when considering strategic decisions between regions.
This document discusses quantile and expectile regressions. It begins by explaining the differences between the econometrics and machine learning approaches. It then introduces quantile and expectile regressions as generalizations of ordinary least squares regression that minimize different loss functions. Finally, it discusses properties of quantile and expectile regressions such as their elicitable measures and how they can be estimated.
This document discusses how family history can impact life insurance premiums. It reviews existing literature on relationships between family members' lifespans, such as husbands and wives or parents and children. Genealogical data is used to analyze dependencies between generations, like grandchildren and grandparents. Quantities important for life insurance are calculated based on family information, showing how premiums may differ depending on how many family members are still alive. The goal is to better understand how family history can influence longevity and mortality risk factors used in life insurance underwriting.
Family History and Life Insurance (UConn actuarial seminar)Arthur Charpentier
This document discusses how family history can impact life insurance premiums. It reviews existing literature on relationships between family members' lifespans. The document analyzes a genealogical dataset to study dependencies between husbands and wives, parents and children, and grandparents and grandchildren. It finds modest but robust correlations between related individuals' lifespans. This dependency is quantified for various life insurance metrics like annuities and whole life insurance, showing family history can impact premiums.
Talk at the modcov19 CNRS workshop, en France, to present our article COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability
The document discusses research on the relationship between family history and life insurance. It summarizes existing literature showing modest but robust connections between the lifespans of family members like spouses, parents and children, and grandparents and grandchildren. The document then presents analyses using a genealogical dataset, finding correlations between related individuals' lifespans. It explores how these family dependencies could impact life insurance premiums and quantities like annuities, widow's pensions, and life expectancies.
This document discusses the use of machine learning techniques in actuarial science and insurance. It begins with an overview of predictive modeling applications in insurance such as fraud detection, premium computation, and claims reserving. It then covers traditional econometric techniques like Poisson and gamma regression models and how machine learning is emerging as an alternative. The document emphasizes evaluating model goodness of fit and uncertainty, and addresses issues like price discrimination and fairness.
This document summarizes a paper on reinforcement learning in economics and finance. It introduces reinforcement learning concepts like agents, environments, actions, rewards, and states. It then discusses applications of reinforcement learning frameworks in economic problems like inventory management, consumption and income dynamics, and experiments. Finally, it notes connections between reinforcement learning and other fields like operations research, stochastic games, and finance.
This document models the COVID-19 pandemic using a compartmental SIDUHR+/- model that divides the population into susceptible (S), infected asymptomatic (I-), infected symptomatic (I+), recovered asymptomatic (R-), recovered symptomatic (R+), hospitalized (H), ICU (U), and dead (D) categories. Optimal lockdown policies are determined by minimizing costs related to deaths, economic impact, testing needs, and immunity while ensuring ICU sustainability. Increasing ICU capacity allows less stringent lockdown policies while achieving similar outcomes. Faster detection of asymptomatic cases through increased testing also enables more flexible lockdown policies.
The document summarizes research on using genealogical data to model dependencies in life spans between family members and quantify the impact on insurance premiums. It presents analysis of husband-wife, parent-child, and grandparent-grandchild relationships, showing dependencies exist. Mortality rates, life expectancies, and insurance quantities like annuities are estimated conditionally based on family history information.
The document discusses natural language processing techniques including word embeddings, text classification using naive Bayes classifiers, and probabilistic language models. It provides examples of part-of-speech tagging and analyzing sentiment. Key concepts covered include the bag-of-words assumption, n-gram models, and maximum likelihood estimation. Various papers on related topics are cited throughout.
This document discusses network representation and analysis. It defines networks as consisting of nodes (vertices) and edges, and describes different ways to represent networks mathematically using adjacency matrices, incidence matrices, and Laplacian matrices. It also discusses visualizing networks using multidimensional scaling and plotting them in R. Special types of networks like complete graphs and random graphs are briefly introduced.
The document discusses various techniques for classifying pictures using neural networks, including convolutional neural networks. It describes how convolutional neural networks can be used to classify images by breaking them into overlapping tiles, applying small neural networks to each tile, and pooling the results. The document also discusses using recurrent neural networks to classify videos by treating them as higher-dimensional tensors.
The document discusses using unusual data sources in insurance. It provides examples of using pictures, text, social media data, telematics, and satellite imagery in insurance. It also discusses challenges in analyzing complex and high-dimensional data from these sources and introduces machine learning tools like PCA, generalized linear models, and evaluating models using loss, risk, and cross-validation.
This document discusses classification and goodness of fit in machine learning. It introduces concepts like confusion matrices, ROC curves, and measures like sensitivity, specificity, and AUC. ROC curves are constructed by plotting the true positive rate vs. false positive rate for different classification thresholds. The AUC can measure classifier performance, with higher values indicating better classification. Chi-square tests and bootstrapping are also discussed for evaluating goodness of fit.
Formation M2i - Onboarding réussi - les clés pour intégrer efficacement vos n...M2i Formation
Améliorez l'intégration de vos nouveaux collaborateurs grâce à notre formation flash sur l'onboarding. Découvrez des stratégies éprouvées et des outils pratiques pour transformer l'intégration en une expérience fluide et efficace, et faire de chaque nouvelle recrue un atout pour vos équipes.
Les points abordés lors de la formation :
- Les fondamentaux d'un onboarding réussi
- Les outils et stratégies pour un onboarding efficace
- L'engagement et la culture d'entreprise
- L'onboarding continu et l'amélioration continue
Formation offerte animée à distance avec notre expert Eric Collin
This document discusses how family history can impact life insurance premiums. It reviews existing literature on relationships between family members' lifespans, such as husbands and wives or parents and children. Genealogical data is used to analyze dependencies between generations, like grandchildren and grandparents. Quantities important for life insurance are calculated based on family information, showing how premiums may differ depending on how many family members are still alive. The goal is to better understand how family history can influence longevity and mortality risk factors used in life insurance underwriting.
Family History and Life Insurance (UConn actuarial seminar)Arthur Charpentier
This document discusses how family history can impact life insurance premiums. It reviews existing literature on relationships between family members' lifespans. The document analyzes a genealogical dataset to study dependencies between husbands and wives, parents and children, and grandparents and grandchildren. It finds modest but robust correlations between related individuals' lifespans. This dependency is quantified for various life insurance metrics like annuities and whole life insurance, showing family history can impact premiums.
Talk at the modcov19 CNRS workshop, en France, to present our article COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability
The document discusses research on the relationship between family history and life insurance. It summarizes existing literature showing modest but robust connections between the lifespans of family members like spouses, parents and children, and grandparents and grandchildren. The document then presents analyses using a genealogical dataset, finding correlations between related individuals' lifespans. It explores how these family dependencies could impact life insurance premiums and quantities like annuities, widow's pensions, and life expectancies.
This document discusses the use of machine learning techniques in actuarial science and insurance. It begins with an overview of predictive modeling applications in insurance such as fraud detection, premium computation, and claims reserving. It then covers traditional econometric techniques like Poisson and gamma regression models and how machine learning is emerging as an alternative. The document emphasizes evaluating model goodness of fit and uncertainty, and addresses issues like price discrimination and fairness.
This document summarizes a paper on reinforcement learning in economics and finance. It introduces reinforcement learning concepts like agents, environments, actions, rewards, and states. It then discusses applications of reinforcement learning frameworks in economic problems like inventory management, consumption and income dynamics, and experiments. Finally, it notes connections between reinforcement learning and other fields like operations research, stochastic games, and finance.
This document models the COVID-19 pandemic using a compartmental SIDUHR+/- model that divides the population into susceptible (S), infected asymptomatic (I-), infected symptomatic (I+), recovered asymptomatic (R-), recovered symptomatic (R+), hospitalized (H), ICU (U), and dead (D) categories. Optimal lockdown policies are determined by minimizing costs related to deaths, economic impact, testing needs, and immunity while ensuring ICU sustainability. Increasing ICU capacity allows less stringent lockdown policies while achieving similar outcomes. Faster detection of asymptomatic cases through increased testing also enables more flexible lockdown policies.
The document summarizes research on using genealogical data to model dependencies in life spans between family members and quantify the impact on insurance premiums. It presents analysis of husband-wife, parent-child, and grandparent-grandchild relationships, showing dependencies exist. Mortality rates, life expectancies, and insurance quantities like annuities are estimated conditionally based on family history information.
The document discusses natural language processing techniques including word embeddings, text classification using naive Bayes classifiers, and probabilistic language models. It provides examples of part-of-speech tagging and analyzing sentiment. Key concepts covered include the bag-of-words assumption, n-gram models, and maximum likelihood estimation. Various papers on related topics are cited throughout.
This document discusses network representation and analysis. It defines networks as consisting of nodes (vertices) and edges, and describes different ways to represent networks mathematically using adjacency matrices, incidence matrices, and Laplacian matrices. It also discusses visualizing networks using multidimensional scaling and plotting them in R. Special types of networks like complete graphs and random graphs are briefly introduced.
The document discusses various techniques for classifying pictures using neural networks, including convolutional neural networks. It describes how convolutional neural networks can be used to classify images by breaking them into overlapping tiles, applying small neural networks to each tile, and pooling the results. The document also discusses using recurrent neural networks to classify videos by treating them as higher-dimensional tensors.
The document discusses using unusual data sources in insurance. It provides examples of using pictures, text, social media data, telematics, and satellite imagery in insurance. It also discusses challenges in analyzing complex and high-dimensional data from these sources and introduces machine learning tools like PCA, generalized linear models, and evaluating models using loss, risk, and cross-validation.
This document discusses classification and goodness of fit in machine learning. It introduces concepts like confusion matrices, ROC curves, and measures like sensitivity, specificity, and AUC. ROC curves are constructed by plotting the true positive rate vs. false positive rate for different classification thresholds. The AUC can measure classifier performance, with higher values indicating better classification. Chi-square tests and bootstrapping are also discussed for evaluating goodness of fit.
Formation M2i - Onboarding réussi - les clés pour intégrer efficacement vos n...M2i Formation
Améliorez l'intégration de vos nouveaux collaborateurs grâce à notre formation flash sur l'onboarding. Découvrez des stratégies éprouvées et des outils pratiques pour transformer l'intégration en une expérience fluide et efficace, et faire de chaque nouvelle recrue un atout pour vos équipes.
Les points abordés lors de la formation :
- Les fondamentaux d'un onboarding réussi
- Les outils et stratégies pour un onboarding efficace
- L'engagement et la culture d'entreprise
- L'onboarding continu et l'amélioration continue
Formation offerte animée à distance avec notre expert Eric Collin
Conseils pour Les Jeunes | Conseils de La Vie| Conseil de La JeunesseOscar Smith
Besoin des conseils pour les Jeunes ? Le document suivant est plein des conseils de la Vie ! C’est vraiment un document conseil de la jeunesse que tout jeune devrait consulter.
Voir version video:
➡https://youtu.be/7ED4uTW0x1I
Sur la chaine:👇
👉https://youtube.com/@kbgestiondeprojets
Aimeriez-vous donc…
-réussir quand on est jeune ?
-avoir de meilleurs conseils pour réussir jeune ?
- qu’on vous offre des conseils de la vie ?
Ce document est une ressource qui met en évidence deux obstacles qui empêchent les jeunes de mener une vie épanouie : l'inaction et le pessimisme.
1) Découvrez comment l'inaction, c'est-à-dire le fait de ne pas agir ou d'agir alors qu'on le devrait ou qu'on est censé le faire, est un obstacle à une vie épanouie ;
> Comment l'inaction affecte-t-elle l'avenir du jeune ? Que devraient plutôt faire les jeunes pour se racheter et récupérer ce qui leur appartient ? A découvrir dans le document ;
2) Le pessimisme, c'est douter de tout ! Les jeunes doutent que la génération plus âgée ne soit jamais orientée vers la bonne volonté. Les jeunes se sentent toujours mal à l'aise face à la ruse et la volonté politique de la génération plus âgée ! Cet état de doute extrême empêche les jeunes de découvrir les opportunités offertes par les politiques et les dispositifs en faveur de la jeunesse. Voulez-vous en savoir plus sur ces opportunités que la plupart des jeunes ne découvrent pas à cause de leur pessimisme ? Consultez cette ressource gratuite et profitez-en !
En rapport avec les " conseils pour les jeunes, " cette ressource peut aussi aider les internautes cherchant :
➡les conseils pratiques pour les jeunes
➡conseils pour réussir
➡jeune investisseur conseil
➡comment investir son argent quand on est jeune
➡conseils d'écriture jeunes auteurs
➡conseils pour les jeunes auteurs
➡comment aller vers les jeunes
➡conseil des jeunes citoyens
➡les conseils municipaux des jeunes
➡conseils municipaux des jeunes
➡conseil des jeunes en mairie
➡qui sont les jeunes
➡projet pour les jeunes
➡conseil des jeunes paris
➡infos pour les jeunes
➡conseils pour les jeunes
➡Quels sont les bienfaits de la jeunesse ?
➡Quels sont les 3 qualités de la jeunesse ?
➡Comment gérer les problèmes des adolescents ?
➡les conseils de jeunes
➡guide de conseils de jeunes
Impact des Critères Environnementaux, Sociaux et de Gouvernance (ESG) sur les...mrelmejri
J'ai réalisé ce projet pour obtenir mon diplôme en licence en sciences de gestion, spécialité management, à l'ISCAE Manouba. Au cours de mon stage chez Attijari Bank, j'ai été particulièrement intéressé par l'impact des critères Environnementaux, Sociaux et de Gouvernance (ESG) sur les décisions d'investissement dans le secteur bancaire. Cette étude explore comment ces critères influencent les stratégies et les choix d'investissement des banques.
Cycle de Formation Théâtrale 2024 / 2025Billy DEYLORD
Pour la Saison 2024 / 2025, l'association « Le Bateau Ivre » propose un Cycle de formation théâtrale pour particuliers amateurs et professionnels des arts de la scène enfants, adolescents et adultes à l'Espace Saint-Jean de Melun (77). 108 heures de formation, d’octobre 2024 à juin 2025, à travers trois cours hebdomadaires (« Pierrot ou la science de la Scène », « Montage de spectacles », « Le Mime et son Répertoire ») et un stage annuel « Tournez dans un film de cinéma muet ».
Newsletter SPW Agriculture en province du Luxembourg du 12-06-24BenotGeorges3
Les informations et évènements agricoles en province du Luxembourg et en Wallonie susceptibles de vous intéresser et diffusés par le SPW Agriculture, Direction de la Recherche et du Développement, Service extérieur de Libramont.
Le fichier :
Les newsletters : https://agriculture.wallonie.be/home/recherche-developpement/acteurs-du-developpement-et-de-la-vulgarisation/les-services-exterieurs-de-la-direction-de-la-recherche-et-du-developpement/newsletters-des-services-exterieurs-de-la-vulgarisation/newsletters-du-se-de-libramont.html
Bonne lecture et bienvenue aux activités proposées.
#Agriculture #Wallonie #Newsletter #Recherche #Développement #Vulgarisation #Evènement #Information #Formation #Innovation #Législation #PAC #SPW #ServicepublicdeWallonie
3. Arthur Charpentier, Mathématiques & Économie
Science Économique ? (la forme)
Source Black & Scholes (1973) et Tirole (2006)
“Economics has a reputation for producing rigourous nonsense ” Harford (2016)
@freakonometrics 3
7. Arthur Charpentier, Mathématiques & Économie
Valorisation sur les Marchés d’Assurance
Utilisation des probabilités (historiques) d’évènements,
et mutualisation par la loi des grands nombres.
@freakonometrics 7
8. Arthur Charpentier, Mathématiques & Économie
Valorisation sur les Marchés Financiers
No Free Lunch: il est impossible de gagner de l’argent sans prendre de risque.
Deux produits qui ont les mêmes payoff ont forcément le même prix (pas
d’arbitrage possible).
@freakonometrics 8
9. Arthur Charpentier, Mathématiques & Économie
Valorisation sur les Marchés Financiers
Finance algorithmique / haute fréquence, source New York Times
@freakonometrics 9
10. Arthur Charpentier, Mathématiques & Économie
Économétrie
L’économétrie est une branche de la science économique qui a pour objectif
d’estimer et de tester les modèles économiques, à partir de données issues de
l’observation du fonctionnement réel de l’économie ou provenant d’expériences
contrôlées.
Source Cowles Commission
@freakonometrics 10
11. Arthur Charpentier, Mathématiques & Économie
Conclusion, la notion de Mathiness
“the full understanding of a problem required no
compromise whatsoever with rigor ” Debreu
Le travail de l’économiste consiste à dériver des “op-
erationally meaningful theorems ” Samuelson
Les modèles mathématiques sont devenu le
novlangue de l’économie, Romer (2015)
@freakonometrics 11