[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127211-en":3,"doc-seo-127211-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127211,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Forecasting Implied Volatility Returns for At-The-Money Currency Options Using Machine Learning - Master Thesis","This thesis adds to the growing research on financial forecasting using machine learning. The study examines how ML models predict returns on implied volatility for at-the-money (ATM) currency options, building on the Kelly et al. (2020, RFS) framework. Results show no meaningful improvement over simple ordinary least squares (OLS) regressions. The findings highlight limits in ML’s ability to capture the predictive content of relevant covariates, reducing forecasting accuracy. The comparison emphasizes careful model selection to improve implied-volatility return forecasts, while expanding understanding of which forecasting approaches work best.","WISEflow Europe/Oslo(CEST)  \n29 Jun 2023  \nHandelsh0ysllolen Bl GRA 19703 Master Thesis  \nThesis Master of Science 100% - B  \nPredefinert informasjon  \nStartdato:  \nSluttdato:  \nEllsamensform:  \nFlowkode:  \nIntern sensor:  \nDelta􀁿er  \nNavn:  \n09-01-2023 09:00 CET  \n03-07-2023 12:00 CEST  \nT 202310ll11378IIINOOIIBIIT (Anonymisert)  \nTermin:  \nVurderingsform:  \n202310  \nNorsk 6-trinns sllala (A-F)  \nPhilip Sigurd Risgaard Kanavin og William Gunnholt Hvalbye  \nlnformasjon fra delta􀁿er  \nTittel •: Forecasting Implied Volatility Returns for At-The-Money Currency Options Using Machine Leaming  \nNaun pli ueileder •: Stephen Walter Szaura  \nlnneholder besuarelsen Nei konfidensielt  \nmateriale7:  \nKan besuarelsenoffentliggj•res?:  \nJa  \nGruppe  \nljruppenaun: (Anonymisert)  \nljruppenummer: 9  \nAndre medlemmer igruppen:  \n􀀔  \nForecasting Implied Volatility Returns for At-The-Money Currency  \nOptions Using Machine Learning  \nMaster Thesis  \nBy  \nWilliam Hvalbye  \nPhilip Kanavin  \nMSc in Business – Major in Finance  \nBergen, June 30, 2023  \nSupervisor:  \nStephen Walter Szaura, Associate Professor  \nDepartment of Finance, BI Norwegian Business School  \nAcknowledgments  \nWe would sincerely like to thank our supervisor Stephen Walter Szaura for his guidance and flexible supervision through quick and thorough feedback. We would also like to thank the library staff at BI campus Bergen for their support and kindness in our search for literature.  \nAbstract  \nThis thesis contributes to the extensive and expanding literature on financial forecasting through machine learning techniques. Our investigation focuses on the predictive capacity of machine learning (ML) models in forecasting the returns of implied volatility (IV) for at-the-money (ATM) currency options, leveraging the established methodology outlined in Kelly et al. (2020, RFS) . In contrast to prior empirical evidence pertaining to equity options prediction, our findings reveal that machine learning techniques do not exhibit superior performance when compared to the straightforward ordinary least squares (OLS) regressions. This research shedslight on the inherent limitations of machine learning models in effectively capturing the predictive power of relevant covariates or variables, thereby resulting in diminished forecasting accuracy. Consequently, our study underscores the significance of exploring alternative approaches and adopting meticulous modelselection strategies to enhance the precision of financial forecasting for implied volatility returns in ATM options. Through the comparative analysis of machine learning techniques and traditional regression models, our study contributes to amore comprehensive understanding of the efficacy of diverse forecasting methodologies within the financial domain.  \nContents  \nACKNOWLEDGMENTS ............................................................................................................... I  \nABSTRACT ..................................................................................................................................... II  \n1. INTRODUCTION AND MOTIVATION ............................................................................ 1  \n2. LITERATURE REVIEW............................................................................................................ 2  \n2.1 CURRENCY OPTION MARKET .................................................................................................... 2  \n2.2 IMPLIED VOLATILITY ............................................................................................................... 5  \n2.3 MACHINE LEARNING IN FINANCE.............................................................................................. 5  \n2.3.1 Machine Learning in financial forecasting ..................................................................... 6  \n2.3.2 Challenges and Limitations in financial forecasting ....................................................... 7  \n2.4 EXISTING STUDIES ON ML FO","cbCaivBw0SthtO1M","https://ap.wps.com/l/cbCaivBw0SthtO1M","pdf",1483868,1,47,"English","en",105,"# Acknowledgments\n# Abstract\n# 1. Introduction and Motivation\n# 2. Literature Review\n## 2.1 Currency Option Market\n## 2.2 Implied Volatility\n## 2.3 Machine Learning in Finance\n## 3. Data\n## 4. Methodology\n## 5. Results","[{\"question\":\"What does the thesis investigate about ATM currency options?\",\"answer\":\"It evaluates whether machine learning models can forecast the returns of implied volatility (IV) for at-the-money currency options.\"},{\"question\":\"How do machine learning results compare with ordinary least squares (OLS)?\",\"answer\":\"The thesis finds ML techniques do not outperform straightforward OLS regressions in forecasting accuracy.\"},{\"question\":\"Why might machine learning underperform in this setting?\",\"answer\":\"The study points to inherent limitations in ML models’ ability to capture the predictive power of relevant covariates, which can lower forecasting performance.\"}]","Forecasting Implied Volatility Returns for At-The-Money Currency Options Using Machine Learning - Master Thesis | PDF",1785937564,118,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forecasting-implied-volatility-returns-for-at-the-money-currency-options-using-machine-learning-master-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-implied-volatility-returns-for-at-the-money-currency-options-using-machine-learning-master-thesis/127211/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the thesis investigate about ATM currency options?","Question",{"text":75,"@type":76},"It evaluates whether machine learning models can forecast the returns of implied volatility (IV) for at-the-money currency options.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do machine learning results compare with ordinary least squares (OLS)?",{"text":80,"@type":76},"The thesis finds ML techniques do not outperform straightforward OLS regressions in forecasting accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"Why might machine learning underperform in this setting?",{"text":84,"@type":76},"The study points to inherent limitations in ML models’ ability to capture the predictive power of relevant covariates, which can lower forecasting performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]