[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126680-en":3,"doc-seo-126680-105":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},126680,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine learning approaches to forecasting cryptocurrency volatility - Considering internal and external determinants","Given the inherently volatile nature of cryptocurrencies, accurate forecasting of cryptocurrency volatility and identification of its determinants are essential. The study applies machine learning techniques to predict volatility using internal determinants (such as lagged volatility and prior trading information) and external determinants (including technology, financial, and policy uncertainty factors). Random Forest and LSTM outperform traditional models like GARCH. Hyper-parameters of LSTM are tuned via Genetic Algorithm and Artificial Bee Colony, and SHAP highlights the dominant role of internal determinants; multi-cryptocurrency training improves performance.","Edinburgh Research Explorer  \nMachine learning approaches to forecasting cryptocurrency volatility  \nConsidering internal and external determinants  \nCitation for published version:  \nWang, Y, Andreeva, G & Martin-Barragan, B 2023, 'Machine learning approaches to forecasting  \ncryptocurrency volatility: Considering internal and external determinants', International Review of Financial  \nAnalysis, vol. 90, 102914, pp. 1-21. [https://doi.org/10.1016/j.irfa.2023.102914](https://doi.org/10.1016/j.irfa.2023.102914)  \nDigital Object Identifier (DOI):  \n10.1016/j.irfa.2023.102914  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nInternational Review of Financial Analysis  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 09. Jan. 2026  \nInternational Review of Financial Analysis 90 (2023) 102914  \n| Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants\u003Cbr>Yijun Wang ∗, Galina Andreeva, Belen Martin-Barragan\u003Cbr>Business School, University of Edinburgh, 29 Buccleuch Place, Edinburgh, EH8 9JS, United Kingdom |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Time-series forecasting Cryptocurrency volatility forecasting Machine learning techniques\u003Cbr>Deep learning techniques Determinants |  | Given the volatile nature of cryptocurrencies, accurately forecasting cryptocurrency volatility and understanding its determinants are crucial. This paper applies machine learning (ML) techniques to forecast cryptocurrency volatility using internal determinants (e.g., lagged volatility, previous trading information) and external determinants (e.g., technology, financial, and policy uncertainty factors). Both Random Forest and Long Short-Term Memory (LSTM) networks significantly outperform traditional volatility models such as GARCH. Furthermore, we explore two optimization models—Genetic Algorithm and Artificial Bee Colony—to tune the hyper-parameters of LSTM. Our results indicate that the application of these optimization models substantially improves forecasting performance. Moreover, using SHapley Additive exPlanations, an interpretation method, we find that internal determinants play the most important roles in volatility forecasts. Finally, our results show that models trained with determinants from multiple cryptocurrencies outperform those trained with determinants from a single cryptocurrency, suggesting that considering a broader range of determinants can capture the complex dynamics in the cryptocurrency market. |\n\n1. Introduction  \nBlockchain technology, hailed as a transformative innovation, is revolutionizing the financial industry. A significant by-product of this technology is cryptocurrency, which is rapidly gaining acceptance among consumers, businesses, and even governments. Major FinTech companies such as Revolut and PayPal have embraced this trend by facilitating access to the cryptocurrency market. In December 2021, Visa, a global leader in digital payments, further substantiated this shift by launching the Crypto Advisory. The number of transactions continues to grow annually, with nearly 20,000 cryptocurrencies currently in circulation and a cumulative market value of","cbCaibnE0aMc5eOS","https://ap.wps.com/l/cbCaibnE0aMc5eOS","pdf",4449683,1,22,"English","en",105,"# Introduction\n## Research gap and objectives\n## Forecasting variables: internal vs external determinants\n## Methods and evaluation metrics\n## Optimization and interpretability (SHAP)","[{\"question\":\"What forecasting inputs does the paper use for cryptocurrency volatility?\",\"answer\":\"It uses internal determinants such as lagged volatility and previous trading information, and external determinants such as technology, financial uncertainty, and policy uncertainty factors.\"},{\"question\":\"Which models are compared against traditional volatility models?\",\"answer\":\"Random Forest and LSTM are compared, and they significantly outperform traditional volatility modeling approaches such as GARCH.\"},{\"question\":\"How does the paper improve LSTM forecasting performance?\",\"answer\":\"It tunes LSTM hyper-parameters using optimization methods including Genetic Algorithm and Artificial Bee Colony, which substantially improves forecasting performance.\"}]","Machine learning approaches to forecasting cryptocurrency volatility - Considering internal and external determinants | PDF",1785934196,55,{"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},"machine-learning-approaches-to-forecasting-cryptocurrency-volatility-considering-internal-and-external-determinants","",{"@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/machine-learning-approaches-to-forecasting-cryptocurrency-volatility-considering-internal-and-external-determinants/126680/",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 forecasting inputs does the paper use for cryptocurrency volatility?","Question",{"text":75,"@type":76},"It uses internal determinants such as lagged volatility and previous trading information, and external determinants such as technology, financial uncertainty, and policy uncertainty factors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models are compared against traditional volatility models?",{"text":80,"@type":76},"Random Forest and LSTM are compared, and they significantly outperform traditional volatility modeling approaches such as GARCH.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper improve LSTM forecasting performance?",{"text":84,"@type":76},"It tunes LSTM hyper-parameters using optimization methods including Genetic Algorithm and Artificial Bee Colony, which substantially improves 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"]