{"id":8027,"date":"2019-10-17T12:29:02","date_gmt":"2019-10-17T10:29:02","guid":{"rendered":"https:\/\/www.uni.lu\/fr\/events\/factor-models-for-conditional-asset-pricing\/"},"modified":"2019-10-17T12:29:02","modified_gmt":"2019-10-17T10:29:02","slug":"factor-models-for-conditional-asset-pricing","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fr\/events\/factor-models-for-conditional-asset-pricing\/","title":{"rendered":"Factor Models for Conditional Asset Pricing"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p><strong>Abstract:<\/strong><\/p><p>This paper develops a methodology for inference on no-arbitrage conditional asset pricing models linear in latent risk factors, valid when the number of assets diverges but the time series dimension is fixed, possibly very small.\u00a0 We show that the no-arbitrage condition permits to identify the risk premia as the expectation of the latent risk factors. This result paves the way to an inferential procedure for the factors&rsquo; risk premia and for the stochastic discount factor, spanned by the latent risk factors. In our setup every feature of the asset pricing model is allowed to be time-varying including loadings, risk premia, idiosyncratic risk and the number of risk factors Several Monte Carlo experiments corroborate our theoretical findings. An empirical application based on individual asset returns data demonstrates the power of the methodology, allowing to tease out the empirical content of the time-variation stemming from asset pricing theory.<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"<p>Abstract:This paper develops a methodology for inference on no-arbitrage conditional asset pricing models linear in latent risk factors, valid when the number of assets diverges but the time series dimension is fixed, possibly very small.\u00a0 We show that the no-arbitrage condition permits to identify the risk premia as the expectation of the latent risk factors. This result paves the way to an inferential procedure for the factors&rsquo; risk premia and for the stochastic discount factor, spanned by the latent risk factors. In our setup every feature of the asset pricing model is allowed to be time-varying including loadings, risk premia, idiosyncratic risk and the number of risk factors Several Monte Carlo experiments corroborate our theoretical findings. An empirical application based on individual asset returns data demonstrates the power of the methodology, allowing to tease out the empirical content of the time-variation stemming from asset pricing theory.<\/p>\n","protected":false},"author":0,"featured_media":8028,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2019-11-07 12:30:00","event_end_date":"2019-11-07 13:45:00","event_speaker_name":"Paolo Zaffaroni \u2013 Imperial College London","event_speaker_link":"","event_is_online":false,"event_location":"University of Luxembourg\r\nMetz\/Nancy Room\r\n29 Boulevard J.-F. Kennedy\r\nL-1855 Luxembourg","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[309],"events-type":[],"organisation":[116,101,226],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Factor Models for Conditional Asset Pricing - Universit\u00e9 du Luxembourg<\/title>\n<meta name=\"description\" content=\"Abstract:This paper develops a methodology for inference on no-arbitrage conditional asset pricing models linear in latent risk factors, valid when the number of assets diverges but the time series dimension is fixed, possibly very small.\u00a0 We show that the no-arbitrage condition permits to identify the risk premia as the expectation of the latent risk factors. This result paves the way to an inferential procedure for the factors&#039; risk premia and for the stochastic discount factor, spanned by the latent risk factors. In our setup every feature of the asset pricing model is allowed to be time-varying including loadings, risk premia, idiosyncratic risk and the number of risk factors Several Monte Carlo experiments corroborate our theoretical findings. An empirical application based on individual asset returns data demonstrates the power of the methodology, allowing to tease out the empirical content of the time-variation stemming from asset pricing theory.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.uni.lu\/fr\/events\/factor-models-for-conditional-asset-pricing\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Factor Models for Conditional Asset Pricing\" \/>\n<meta property=\"og:description\" content=\"Abstract:This paper develops a methodology for inference on no-arbitrage conditional asset pricing models linear in latent risk factors, valid when the number of assets diverges but the time series dimension is fixed, possibly very small.\u00a0 We show that the no-arbitrage condition permits to identify the risk premia as the expectation of the latent risk factors. This result paves the way to an inferential procedure for the factors&#039; risk premia and for the stochastic discount factor, spanned by the latent risk factors. In our setup every feature of the asset pricing model is allowed to be time-varying including loadings, risk premia, idiosyncratic risk and the number of risk factors Several Monte Carlo experiments corroborate our theoretical findings. 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