[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118327-en":3,"doc-seo-118327-105":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},118327,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Prediction of realized volatility and implied volatility indices using AI and machine learning - A review","This systematic literature review examines existing studies that predict realized volatility and implied volatility indices using artificial intelligence and machine learning. The review assesses whether proposed AI/ML methods outperform traditional econometric models, gauges how widely explainable AI is applied, and identifies directions for future research. Overall, AI/ML approaches show highly promising forecasting efficacy, often matching or improving on econometric benchmarks, with LSTM and GRU models frequently among top performers. Despite common concerns about black-box models, XAI usage remains rare, motivating broader adoption, while probabilistic ML offers opportunities to quantify forecast uncertainty.","International Review of Financial Analysis 93 (2024) 103221  \n| Review\u003Cbr>Prediction of realized volatility and implied volatility indices using AI and machine learning: A review |  |  |  |\n| --- | --- | --- | --- |\n| Elias Søvik Gunnarsson, Håkon Ramon Isern, Aristidis Kaloudis, Morten Risstad ∗, Benjamin Vigdel, Sjur Westgaard\u003Cbr>Norwegian University of Science and Technology (NTNU), Faculty of Economics and Management, Department of Industrial Economics and Technology Management, Norway |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Volatility forecasting\u003Cbr>Machine learning\u003Cbr>Explainable artificial intelligence |  | In this systematic literature review, we examine the existing studies predicting realized volatility and implied volatility indices using artificial intelligence and machine learning. We survey the literature in order to discover whether the proposed methods provide superior forecasts compared to traditional econometric models, how widespread the application of explainable AI is, and to outline potential areas for further research. Generally, we find the efficacy of AI and ML methods for volatility prediction to be highly promising, often providing comparative or better results than their econometric counterparts. Neural networks employing memory, such as Long–Short Term Memory and Gated Recurrent Units, consistently rank among the top performing models. However, traditional econometric models are still highly relevant, commonly yielding similar results as more advanced ML and AI models. In light of the success with ensemble methods, a promising area of research is the use of hybrid models, combining machine learning and econometric models. In spite of the common critique of many machine learning models being of a black-box nature, we find that very few papers apply XAIto analyze and support their empirical results. Thus, we recommend that researchers strive harder to employ XAI in future work. Similarly, we see potential for applications of probabilistic machine learning, effectively quantifying uncertainty in volatility forecasts from machine learning models. |  |\n\n1. Introduction  \nDue to significant growth in the use of artificial intelligence and machine learning techniques in the recent years, there has been an equally growing interest in applications within prediction and forecasting of volatility using these methods. Volatility is a complex and dynamic phenomenon of interest to many stakeholders within finance and economics. Reliable volatility forecasts are important tools for these stakeholders in anticipating changes in market conditions to which they can adapt accordingly. For example, accurate volatility forecasts may help a fund manager to reduce their exposure to potential markets risks, by reducing their positions in assets that are likely tobe affected by increased volatility or by hedging their positions using derivatives. Volatility is also an important factor in determining the value of such derivatives. Furthermore, risk managers may utilize accurate volatility forecasts to compute more precise Value at Risk (VaR)1 measures. Traders can use accurate forecasts to make more informed decisions about when to enter and exit positions in financial markets.  \nFor instance, the low volatility during the summer of 2022 have caused traders large losses and stresses the importance of high conviction of volatility movements (Tsekova & Popina, 2022). Finally, governments and institutions can benefit from accurate volatility forecasts to assess the volatility and its impacts on the economy to make effective policy decisions regarding monetary and regulatory policy.  \nEstimating and forecasting volatility is complex, since volatility itself is a latent variable. Realized volatility, inferred from the sum of squared intradaily high-frequency returns, have since the seminal contribution of Andersen et al. (2001a), been considered the most appropriate representation of the true,","cbCaitCsmvbxhk7M","https://ap.wps.com/l/cbCaitCsmvbxhk7M","pdf",1601486,1,20,"English","en",105,"# Introduction\n# Volatility forecasting with AI/ML\n## Realized volatility and implied volatility concepts\n## Motivation from finance and risk management","[{\"question\":\"What does the review focus on regarding volatility prediction?\",\"answer\":\"It systematically surveys studies that predict realized volatility and implied volatility indices using AI and machine learning, evaluating forecast performance and methodological practices.\"},{\"question\":\"Do AI and machine learning methods generally outperform econometric models?\",\"answer\":\"Yes. The review finds AI/ML volatility prediction to be highly promising and often provides comparative or better results than traditional econometric approaches.\"},{\"question\":\"How common is explainable AI (XAI) in the reviewed research?\",\"answer\":\"Explainable AI is rarely used. The review notes that very few papers apply XAI to analyze and support empirical findings.\"}]","Prediction of realized volatility and implied volatility indices using AI and machine learning - A review | PDF",1785683068,50,{"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},"prediction-of-realized-volatility-and-implied-volatility-indices-using-ai-and-machine-learning-a-review","",{"@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/prediction-of-realized-volatility-and-implied-volatility-indices-using-ai-and-machine-learning-a-review/118327/",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","2026-08-02",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 review focus on regarding volatility prediction?","Question",{"text":75,"@type":76},"It systematically surveys studies that predict realized volatility and implied volatility indices using AI and machine learning, evaluating forecast performance and methodological practices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Do AI and machine learning methods generally outperform econometric models?",{"text":80,"@type":76},"Yes. The review finds AI/ML volatility prediction to be highly promising and often provides comparative or better results than traditional econometric approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"How common is explainable AI (XAI) in the reviewed research?",{"text":84,"@type":76},"Explainable AI is rarely used. The review notes that very few papers apply XAI to analyze and support empirical findings.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]