{"id":279453,"date":"2026-05-04T18:10:52","date_gmt":"2026-05-04T16:10:52","guid":{"rendered":"https:\/\/omote-advisory.com\/?page_id=279453"},"modified":"2026-05-11T16:59:16","modified_gmt":"2026-05-11T14:59:16","slug":"analyse-quantitative","status":"publish","type":"page","link":"https:\/\/omote-advisory.com\/en\/analyse-quantitative\/","title":{"rendered":"Quantitative Analysis"},"content":{"rendered":"

[et_pb_section fb_built=\u00a0\u00bb1″ fullwidth=\u00a0\u00bbon\u00a0\u00bb _builder_version=\u00a0\u00bb4.24.2″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_fullwidth_header title=\u00a0\u00bbAnalyse Quantitative\u00a0\u00bb text_orientation=\u00a0\u00bbright\u00a0\u00bb _builder_version=\u00a0\u00bb4.27.6″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb title_font=\u00a0\u00bbLora|600|||||||\u00a0\u00bb title_font_size=\u00a0\u00bb48px\u00a0\u00bb background_enable_color=\u00a0\u00bboff\u00a0\u00bb use_background_color_gradient=\u00a0\u00bbon\u00a0\u00bb background_color_gradient_stops=\u00a0\u00bbrgba(0,0,0,0.5) 0%|rgba(0,0,0,0.5) 100%\u00a0\u00bb background_image=\u00a0\u00bbhttps:\/\/omote-advisory.com\/wp-content\/uploads\/2024\/12\/5_ms_illustration_of_a_network_of_nodes_interconnected_points_w_f0b34aa2-c626-4a2f-b414-87cfdc75b104.png\u00a0\u00bb custom_margin=\u00a0\u00bb||||false|false\u00a0\u00bb custom_padding=\u00a0\u00bb233px||||false|false\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][\/et_pb_fullwidth_header][\/et_pb_section][et_pb_section fb_built=\u00a0\u00bb1″ _builder_version=\u00a0\u00bb4.25.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb background_enable_color=\u00a0\u00bboff\u00a0\u00bb custom_margin=\u00a0\u00bb50px||||false|false\u00a0\u00bb custom_padding=\u00a0\u00bb||||false|false\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_row column_structure=\u00a0\u00bb1_2,1_2″ _builder_version=\u00a0\u00bb4.25.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb max_width=\u00a0\u00bb100%\u00a0\u00bb custom_margin=\u00a0\u00bb||||false|false\u00a0\u00bb custom_padding=\u00a0\u00bb||||false|false\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_column type=\u00a0\u00bb1_2″ _builder_version=\u00a0\u00bb4.25.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb custom_padding=\u00a0\u00bb||||false|false\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][et_pb_text _builder_version=\u00a0\u00bb4.27.6″ _module_preset=\u00a0\u00bb1159399b-6826-48aa-a1a9-e0994f4dcf7b\u00a0\u00bb text_font=\u00a0\u00bbLora||||||||\u00a0\u00bb text_text_color=\u00a0\u00bb#001F3F\u00a0\u00bb custom_padding=\u00a0\u00bb0px||0px||true|true\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb]<\/p>\n

L’analyse quantitative est rarement comprise pour ce qu’elle est vraiment.<\/span><\/p>\n

Pour beaucoup, c’est \u00ab\u00a0appliquer des statistiques \u00e0 la finance\u00a0\u00bb. Pour d’autres, c’est \u00ab\u00a0faire tourner Monte Carlo\u00a0\u00bb. Pour d’autres encore, c’est \u00ab\u00a0ce qu’on demande aux quants quand on ne sait pas o\u00f9 classer le sujet\u00a0\u00bb.<\/span><\/p>\n

Pour nous, c’est tout autre chose. C’est la\u00a0discipline qui consiste \u00e0 donner du sens math\u00e9matique au risque dans des conditions d’incertitude irr\u00e9ductible<\/b>, et \u00e0 le faire d’une mani\u00e8re qui r\u00e9siste au temps, \u00e0 l’audit, et au r\u00e9gulateur.<\/span><\/p>\n

Cela tient en trois exigences simultan\u00e9es qui ne se laissent pas r\u00e9concilier facilement.<\/span><\/p>\n

Une\u00a0rigueur math\u00e9matique<\/b>\u00a0suffisante pour que le mod\u00e8le dise quelque chose de vrai sur le monde \u2014 dans les r\u00e9gimes o\u00f9 on l’utilise comme dans ceux qu’il extrapole.<\/span><\/p>\n

Une\u00a0prudence op\u00e9rationnelle<\/b>\u00a0suffisante pour que le mod\u00e8le reste utilisable par des \u00e9quipes qui ne sont pas toutes math\u00e9maticiennes, et qui doivent le d\u00e9fendre devant un comit\u00e9, un auditeur, ou un superviseur.<\/span><\/p>\n

Une\u00a0conscience des limites<\/b>\u00a0suffisante pour savoir o\u00f9 le mod\u00e8le cesse de dire la v\u00e9rit\u00e9 \u2014 et pour que cela soit explicite dans la d\u00e9cision qui en d\u00e9coule, pas cach\u00e9 dans un appendice.<\/span><\/p>\n

Cette intersection \u2014 rigueur, pragmatisme, lucidit\u00e9 \u2014 est rare. C’est elle qui d\u00e9finit nos missions d’analyse quantitative.<\/span><\/p>\n

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Notre approche<\/span><\/h2>\n

L’analyse quantitative chez OMOTE n’est pas un service satellite : c’est une expertise transversale, qui irrigue toutes les autres.<\/span><\/p>\n

Quand nous accompagnons un client sur l’ALM, c’est notre expertise quantitative qui rend les mod\u00e8les d’\u00e9coulement cr\u00e9dibles. Quand nous travaillons sur le risque de march\u00e9, c’est elle qui structure le calcul VaR et la mesure de queue. Quand nous validons un mod\u00e8le interne, c’est elle qui sait poser les bonnes questions adversariales.<\/span><\/p>\n

Nos \u00e9quipes quantitatives combinent trois profils compl\u00e9mentaires : des\u00a0financial engineers<\/b>\u00a0issus d’\u00e9coles d’ing\u00e9nieur ou de finance quantitative, des\u00a0statisticiens<\/b>\u00a0rompus \u00e0 la mod\u00e9lisation \u00e9conom\u00e9trique et \u00e0 l’inf\u00e9rence en r\u00e9gimes non-stationnaires, et des\u00a0math\u00e9maticiens<\/b>\u00a0capables d’aller dans le d\u00e9tail des techniques d’optimisation, de simulation, et d’estimation. Cette diversit\u00e9 est le seul moyen s\u00e9rieux d’attaquer les probl\u00e8mes financiers d’aujourd’hui : aucune des trois disciplines, prise seule, n’y suffit.<\/span><\/p>\n

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L’enjeu de l’IA sp\u00e9cialis\u00e9e<\/span><\/h2>\n

<\/span><\/p>\n

L’IA est en train de transformer l’analyse quantitative \u2014 moins par les algorithmes eux-m\u00eames, qui existent depuis longtemps, que par leur\u00a0mise \u00e0 l’\u00e9chelle<\/b>\u00a0et leur\u00a0int\u00e9gration<\/b>\u00a0dans des cha\u00eenes de d\u00e9cision op\u00e9rationnelles.<\/span><\/p>\n

C’est pr\u00e9cis\u00e9ment ce sujet qui anime notre R&D actuelle, et c’est l’objet principal de\u00a0RIALTO KERNEL<\/b>\u00a0\u2014 la plateforme analytique que nous avons d\u00e9velopp\u00e9e pour industrialiser ces capacit\u00e9s. RIALTO est, \u00e0 proprement parler,\u00a0specialised AI for risk and ALM<\/i>\u00a0: non pas de l’IA g\u00e9n\u00e9rale appliqu\u00e9e tardivement \u00e0 la finance, mais de l’intelligence calibr\u00e9e par construction pour les exigences sp\u00e9cifiques du risque financier \u2014 auditabilit\u00e9, tra\u00e7abilit\u00e9, conformit\u00e9, et possibilit\u00e9 pour les \u00e9quipes internes de comprendre ce que la machine fait.<\/span><\/p>\n

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Une discipline de service, pas de spectacle<\/span><\/h2>\n

<\/span><\/p>\n

Une derni\u00e8re chose, parce que c’est le pi\u00e8ge classique de l’analyse quantitative : nous ne croyons pas que la sophistication math\u00e9matique soit un objectif en soi.<\/span><\/p>\n

La sophistication n’est utile que dans la mesure o\u00f9 elle rend la d\u00e9cision plus juste, ou la mesure plus fiable. Un mod\u00e8le simple bien valid\u00e9 bat presque toujours un mod\u00e8le complexe mal compris. Cette discipline-l\u00e0 \u2014 savoir quand \u00eatre simple et quand \u00eatre sophistiqu\u00e9 \u2014 est ce qui distingue l’expertise quantitative authentique du bricolage comp\u00e9tent. C’est ce que nos clients attendent de nous, et c’est ce sur quoi nous nous mesurons.<\/span><\/p>\n

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Mod\u00e9lisation financi\u00e8re avanc\u00e9e<\/strong><\/p>\n

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Mod\u00e8les de pricing pour produits d\u00e9riv\u00e9s (options exotiques, structur\u00e9s, sensibilit\u00e9s d’ordre sup\u00e9rieur). Mod\u00e9lisation factorielle de la structure par terme des taux. Mod\u00e8les de d\u00e9faut, de corr\u00e9lation de cr\u00e9dit, et de copules. Calibration sur donn\u00e9es de march\u00e9 et back-testing en r\u00e9gimes historiques.<\/span><\/p>\n

[\/et_pb_text][et_pb_divider color=\u00a0\u00bb#001F3F\u00a0\u00bb divider_position=\u00a0\u00bbcenter\u00a0\u00bb _builder_version=\u00a0\u00bb4.25.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb width=\u00a0\u00bb82%\u00a0\u00bb module_alignment=\u00a0\u00bbcenter\u00a0\u00bb custom_margin=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb custom_padding=\u00a0\u00bb||||false|false\u00a0\u00bb animation_style=\u00a0\u00bbfade\u00a0\u00bb animation_speed_curve=\u00a0\u00bbease-out\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][\/et_pb_divider][et_pb_blurb use_icon=\u00a0\u00bbon\u00a0\u00bb font_icon=\u00a0\u00bb$||divi||400″ icon_color=\u00a0\u00bb#001F3F\u00a0\u00bb image_icon_background_color=\u00a0\u00bb#FDFEFE\u00a0\u00bb icon_placement=\u00a0\u00bbleft\u00a0\u00bb image_icon_width=\u00a0\u00bb100%\u00a0\u00bb _builder_version=\u00a0\u00bb4.27.6″ _module_preset=\u00a0\u00bb2a091536-9291-4618-a437-983972988076″ header_font=\u00a0\u00bb|600|||||||\u00a0\u00bb header_text_align=\u00a0\u00bbleft\u00a0\u00bb header_text_color=\u00a0\u00bb#001F3F\u00a0\u00bb body_font=\u00a0\u00bb||||||||\u00a0\u00bb body_text_align=\u00a0\u00bbleft\u00a0\u00bb body_text_color=\u00a0\u00bb#001F3F\u00a0\u00bb body_font_size=\u00a0\u00bb20px\u00a0\u00bb background_enable_color=\u00a0\u00bboff\u00a0\u00bb image_icon_custom_margin=\u00a0\u00bb||||false|true\u00a0\u00bb image_icon_custom_padding=\u00a0\u00bb5px|5px|5px|5px|true|true\u00a0\u00bb custom_margin=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb custom_padding=\u00a0\u00bb0%||0%||false|false\u00a0\u00bb animation_style=\u00a0\u00bbfade\u00a0\u00bb animation_speed_curve=\u00a0\u00bbease-in\u00a0\u00bb border_radii_image=\u00a0\u00bbon|100%|100%|100%|100%\u00a0\u00bb border_color_all_image=\u00a0\u00bbRGBA(255,255,255,0)\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb]<\/p>\n

Analyse de risques quantitatifs<\/strong><\/p>\n

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Mesure de risques de march\u00e9 (VaR, expected shortfall, IRC) et de cr\u00e9dit (PD, LGD, EAD). Conception et conduite d’exercices Monte Carlo \u00e0 grande \u00e9chelle. Mod\u00e8les de stress test internes \u2014 historiques, hypoth\u00e9tiques, conjoints multi-risques. Estimation de queue et th\u00e9orie des valeurs extr\u00eames.<\/span><\/p>\n

[\/et_pb_text][et_pb_divider color=\u00a0\u00bb#001F3F\u00a0\u00bb divider_position=\u00a0\u00bbcenter\u00a0\u00bb _builder_version=\u00a0\u00bb4.25.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb width=\u00a0\u00bb82%\u00a0\u00bb module_alignment=\u00a0\u00bbcenter\u00a0\u00bb custom_margin=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb custom_padding=\u00a0\u00bb||||false|false\u00a0\u00bb animation_style=\u00a0\u00bbfade\u00a0\u00bb animation_speed_curve=\u00a0\u00bbease-out\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][\/et_pb_divider][et_pb_blurb use_icon=\u00a0\u00bbon\u00a0\u00bb font_icon=\u00a0\u00bb$||divi||400″ icon_color=\u00a0\u00bb#001F3F\u00a0\u00bb image_icon_background_color=\u00a0\u00bb#FDFEFE\u00a0\u00bb icon_placement=\u00a0\u00bbleft\u00a0\u00bb image_icon_width=\u00a0\u00bb100%\u00a0\u00bb _builder_version=\u00a0\u00bb4.27.6″ _module_preset=\u00a0\u00bb2a091536-9291-4618-a437-983972988076″ header_font=\u00a0\u00bb|600|||||||\u00a0\u00bb header_text_align=\u00a0\u00bbleft\u00a0\u00bb header_text_color=\u00a0\u00bb#001F3F\u00a0\u00bb body_font=\u00a0\u00bb||||||||\u00a0\u00bb body_text_align=\u00a0\u00bbleft\u00a0\u00bb body_text_color=\u00a0\u00bb#001F3F\u00a0\u00bb body_font_size=\u00a0\u00bb20px\u00a0\u00bb background_enable_color=\u00a0\u00bboff\u00a0\u00bb image_icon_custom_margin=\u00a0\u00bb||||false|true\u00a0\u00bb image_icon_custom_padding=\u00a0\u00bb5px|5px|5px|5px|true|true\u00a0\u00bb custom_margin=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb custom_padding=\u00a0\u00bb0%||0%||false|false\u00a0\u00bb animation_style=\u00a0\u00bbfade\u00a0\u00bb animation_speed_curve=\u00a0\u00bbease-in\u00a0\u00bb border_radii_image=\u00a0\u00bbon|100%|100%|100%|100%\u00a0\u00bb border_color_all_image=\u00a0\u00bbRGBA(255,255,255,0)\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb]<\/p>\n

Mod\u00e9lisation comportementale<\/strong><\/p>\n

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Mod\u00e8les d’\u00e9coulement des d\u00e9p\u00f4ts \u00e0 vue (DAV), des comptes d’\u00e9pargne (PEL, CEL, etc), et des produits sans \u00e9ch\u00e9ance contractuelle (livret A, livret ordinaire, etc), mod\u00e8les de remboursement anticip\u00e9 et de comportement client\u00e8le (RA\/RN), mod\u00e8les d’attrition, …<\/span><\/p>\n

Au au c\u0153ur de l’ALM moderne, cette discipline est celle o\u00f9 l’analyse quantitative bascule de la math\u00e9matique pure vers la science du comportement humain math\u00e9matis\u00e9. C’est elle qui anime, dans RIALTO KERNEL<\/b>, le moteur\u00a0Flow Insight Core<\/b>\u00a0: un syst\u00e8me de mod\u00e9lisation comportementale propri\u00e9taire qui produit, pour chaque ligne du bilan, une repr\u00e9sentation coh\u00e9rente de la dynamique des flux dans le temps.<\/span><\/p>\n

[\/et_pb_text][et_pb_divider color=\u00a0\u00bb#001F3F\u00a0\u00bb divider_position=\u00a0\u00bbcenter\u00a0\u00bb _builder_version=\u00a0\u00bb4.25.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb width=\u00a0\u00bb82%\u00a0\u00bb module_alignment=\u00a0\u00bbcenter\u00a0\u00bb custom_margin=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb custom_padding=\u00a0\u00bb||||false|false\u00a0\u00bb animation_style=\u00a0\u00bbfade\u00a0\u00bb animation_speed_curve=\u00a0\u00bbease-out\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][\/et_pb_divider][et_pb_blurb use_icon=\u00a0\u00bbon\u00a0\u00bb font_icon=\u00a0\u00bb$||divi||400″ icon_color=\u00a0\u00bb#001F3F\u00a0\u00bb image_icon_background_color=\u00a0\u00bb#FDFEFE\u00a0\u00bb icon_placement=\u00a0\u00bbleft\u00a0\u00bb image_icon_width=\u00a0\u00bb100%\u00a0\u00bb _builder_version=\u00a0\u00bb4.27.6″ _module_preset=\u00a0\u00bb2a091536-9291-4618-a437-983972988076″ header_font=\u00a0\u00bb|600|||||||\u00a0\u00bb header_text_align=\u00a0\u00bbleft\u00a0\u00bb header_text_color=\u00a0\u00bb#001F3F\u00a0\u00bb body_font=\u00a0\u00bb||||||||\u00a0\u00bb body_text_align=\u00a0\u00bbleft\u00a0\u00bb body_text_color=\u00a0\u00bb#001F3F\u00a0\u00bb body_font_size=\u00a0\u00bb20px\u00a0\u00bb background_enable_color=\u00a0\u00bboff\u00a0\u00bb image_icon_custom_margin=\u00a0\u00bb||||false|true\u00a0\u00bb image_icon_custom_padding=\u00a0\u00bb5px|5px|5px|5px|true|true\u00a0\u00bb custom_margin=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb custom_padding=\u00a0\u00bb0%||0%||false|false\u00a0\u00bb animation_style=\u00a0\u00bbfade\u00a0\u00bb animation_speed_curve=\u00a0\u00bbease-in\u00a0\u00bb border_radii_image=\u00a0\u00bbon|100%|100%|100%|100%\u00a0\u00bb border_color_all_image=\u00a0\u00bbRGBA(255,255,255,0)\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb]<\/p>\n

Validation et gouvernance des mod\u00e8les<\/strong><\/p>\n

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Mise en place de dispositifs de validation ind\u00e9pendante selon les principes SR 11-7 et TRIM. Documentation, tra\u00e7abilit\u00e9, audit. Suivi de performance dans le temps, d\u00e9tection de d\u00e9rive (drift), proc\u00e9dures de rem\u00e9diation.<\/span><\/p>\n

Cette discipline est devenue centrale \u00e0 l’\u00e8re de l’IA : un mod\u00e8le qui ne peut pas \u00eatre valid\u00e9, f\u00fbt-il sophistiqu\u00e9,\u00a0 n’a pas vocation \u00e0 entrer en production.<\/span><\/p>\n

[\/et_pb_text][et_pb_divider color=\u00a0\u00bb#001F3F\u00a0\u00bb divider_position=\u00a0\u00bbcenter\u00a0\u00bb _builder_version=\u00a0\u00bb4.25.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb width=\u00a0\u00bb82%\u00a0\u00bb module_alignment=\u00a0\u00bbcenter\u00a0\u00bb custom_margin=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb custom_padding=\u00a0\u00bb||||false|false\u00a0\u00bb animation_style=\u00a0\u00bbfade\u00a0\u00bb animation_speed_curve=\u00a0\u00bbease-out\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb][\/et_pb_divider][et_pb_blurb use_icon=\u00a0\u00bbon\u00a0\u00bb font_icon=\u00a0\u00bb$||divi||400″ icon_color=\u00a0\u00bb#001F3F\u00a0\u00bb image_icon_background_color=\u00a0\u00bb#FDFEFE\u00a0\u00bb icon_placement=\u00a0\u00bbleft\u00a0\u00bb image_icon_width=\u00a0\u00bb100%\u00a0\u00bb _builder_version=\u00a0\u00bb4.27.6″ _module_preset=\u00a0\u00bb2a091536-9291-4618-a437-983972988076″ header_font=\u00a0\u00bb|600|||||||\u00a0\u00bb header_text_align=\u00a0\u00bbleft\u00a0\u00bb header_text_color=\u00a0\u00bb#001F3F\u00a0\u00bb body_font=\u00a0\u00bb||||||||\u00a0\u00bb body_text_align=\u00a0\u00bbleft\u00a0\u00bb body_text_color=\u00a0\u00bb#001F3F\u00a0\u00bb body_font_size=\u00a0\u00bb20px\u00a0\u00bb background_enable_color=\u00a0\u00bboff\u00a0\u00bb image_icon_custom_margin=\u00a0\u00bb||||false|true\u00a0\u00bb image_icon_custom_padding=\u00a0\u00bb5px|5px|5px|5px|true|true\u00a0\u00bb custom_margin=\u00a0\u00bb0px||0px||true|false\u00a0\u00bb custom_padding=\u00a0\u00bb0%||0%||false|false\u00a0\u00bb animation_style=\u00a0\u00bbfade\u00a0\u00bb animation_speed_curve=\u00a0\u00bbease-in\u00a0\u00bb border_radii_image=\u00a0\u00bbon|100%|100%|100%|100%\u00a0\u00bb border_color_all_image=\u00a0\u00bbRGBA(255,255,255,0)\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb]<\/p>\n

Recherche & D\u00e9veloppement<\/strong><\/p>\n

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Veille scientifique sur les techniques d’optimisation, de simulation, et d’estimation. Exp\u00e9rimentation des approches issues de la recherche acad\u00e9mique (apprentissage profond appliqu\u00e9 \u00e0 la finance, processus stochastiques avanc\u00e9s, m\u00e9thodes bay\u00e9siennes). \u00c9valuation critique de leur applicabilit\u00e9 r\u00e9elle au contexte r\u00e9gul\u00e9 du risque financier.<\/span><\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

L’analyse quantitative est rarement comprise pour ce qu’elle est vraiment. Pour beaucoup, c’est \u00ab\u00a0appliquer des statistiques \u00e0 la finance\u00a0\u00bb. Pour d’autres, c’est \u00ab\u00a0faire tourner Monte Carlo\u00a0\u00bb. Pour d’autres encore, c’est \u00ab\u00a0ce qu’on demande aux quants quand on ne sait pas o\u00f9 classer le sujet\u00a0\u00bb. Pour nous, c’est tout autre chose. C’est la\u00a0discipline qui consiste \u00e0 […]<\/p>\n","protected":false},"author":6,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-279453","page","type-page","status-publish","hentry"],"yoast_head":"\nAnalyse Quantitative - OMOTE ADVISORY<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/omote-advisory.com\/en\/analyse-quantitative\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Analyse Quantitative - OMOTE ADVISORY\" \/>\n<meta property=\"og:description\" content=\"OMOTE Advisory est un cabinet de conseil expert, sp\u00e9cialis\u00e9 en ALM et en gestion des risques financiers. 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