[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127648-en":3,"doc-seo-127648-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127648,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Market Volatility: Can Machine Learning Methods Enhance Volatility Forecasting? - Dissertation Abstract","This dissertation tests whether machine learning techniques improve volatility forecasting accuracy and whether they can outperform the best econometric benchmark, the Heterogeneous Autoregressive model of Realized Volatility (HAR-RV). Using S&P 500 index data from May 2007 to August 2022, HAR-RV is validated against EWMA and GARCH(1,1). Artificial Neural Network models LSTM and GRU are compared across five variable sets.","Market Volatility: Can Machine Learning Methods Enhance Volatility Forecasting?  \nAfonso Batista  \nDissertation written under the supervision of Professor José Faias  \nDissertation submitted in partial fulfilment of requirements for the MSc in Finance, at Universidade Católica Portuguesa, April 2023.  \nAbstract  \nThis dissertation aims to test whether the use of machine learning (ML) techniques can improve volatility forecasting accuracy. More specifically, if it can beat the best econometric model, the Heterogeneous Autoregressive model of Realized Volatility (HAR-RV) . Using S&P 500 Index data from May-2007 to August-2022, the superiority of the HAR-RV was tested and attested against competing econometric models EWMA and GARCH(1,1) . Next, the performance of the ML Artificial Neural Network algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are compared to the performance of the econometric models. Five different variable sets are tested for the ML models. It is found that while both ML models are able to beat the EWMA and GARCH(1,1) models by a significant margin, the HAR-RV model still outperforms LSTM and GRU.  \nMoreover, an analysis is conduced on the models’ predictions on the period corresponding to the Covid-19 crisis. The results did not show any evidence suggesting that ML methods have a particular advantage at predicting during high volatility events.  \nFinally, a plausible cause that could undermine the remarkable qualities of the ML methods in the aim of volatility forecasting is discussed. It is found that the rigorous set of conditions needed to be met for the proper setup of ML models are very difficult to be met using financial data, which hinders the aptitude of ML for this purpose.  \nKeywords: Volatility Forecasting; Heterogeneous AutoRegressive model; Machine Learning; Artificial Neural Networks; Long Short-Term Memory; Gated Recurrent Unit.  \nTitle: Market Volatility: Can Machine Learning Methods Enhance Volatility Forecasting?  \nAuthor: Afonso Maria Nabeto Valentim Xavier Batista  \nResumo  \nEsta tese visa testar se o uso de técnicas de Machine Learning (ML) pode melhorar a precisãoda previsão da volatilidade. Mais especificamente, se estes algoritmos conseguem superar omelhor modelo econométrico, o Heterogeneous Autoregressive model of Realized Volatility (HAR-RV) . Usando dados do Índice S&P 500 de Maio-2007 a Agosto-2022, a superioridade do HAR-RV perante os modelos econométricos concorrentes EWMA e GARCH(1,1), foi testada e confirmada. Em seguida, o desempenho dos algoritmos ML de redes neurais artificiais de Long Short-Term Memory (LSTM) e Gated Recurrent Unit (GRU) são comparados com odesempenho dos modelos econométricos tradicionais. Cinco conjuntos diferentes de variáveissão testados para os modelos ML. Verifica-se que enquanto ambos os modelos ML são capazes de superar os modelos EWMA e GARCH(1,1) por uma margem significante, o modelo HARRV ainda tem um desempenho superior ao LSTM e ao GRU.  \nÉ ainda feita uma análise das previsões dos modelos durante o período correspondente à crisedo Covid-19. Os resultados não mostram qualquer evidência que sugira que os métodos MLtêm uma particular vantagem durante eventos de alta volatilidade.  \nFinalmente, é discutida uma possível causa que poderá debilitar as sofisticadas qualidades dos métodos ML para a finalidade de previsão de volatilidade. Verifica-se que o conjunto rigorosode condições necessárias para a correcta configuração dos modelos ML é muito difícil de secumprir utilizando series temporais de volatilidade de mercado, o que prejudica a aptidão dos modelos ML para esta finalidade.  \nPalavras-chave: Previsão de Volatilidade; Modelos Heterogéneos AutoRegressivos; Machine Learning; Redes Neurais Artificiais; Long Short-Term Memory; Gated Recurrent Unit.  \nTítulo: Market Volatility: Can Machine Learning Methods Enhance Volatility Forecasting?  \nAutor: Afonso Maria Nabeto Valentim Xavier Batista  \nTable of Contents  \n1. Introducti","cbCaibqGXckVmmu6","https://ap.wps.com/l/cbCaibqGXckVmmu6","pdf",1557078,1,48,"English","en",105,"# Introduction\n# Volatility\n## Defining and Measuring Volatility\n## Volatility Stylized Facts\n# Econometric Models\n## Heterogeneous Autoregressive for Realized Volatility (HAR-RV)\n## Competing Econometric Models\n### Exponentially Weighted Moving Average (EWMA)\n### Generalized Autoregressive Conditional Heteroskedasticity (GARCH)\n## Experimental Setup\n### Data\n### Evaluation Metrics\n# Machine Learning Models\n## Machine Learning Approaches Literature Review","[{\"question\":\"What is the main research question of the dissertation?\",\"answer\":\"The dissertation examines whether machine learning methods can enhance volatility forecasting accuracy and outperform the HAR-RV econometric model.\"},{\"question\":\"Which models are used as benchmarks and competitors?\",\"answer\":\"The benchmark is HAR-RV, compared against EWMA and GARCH(1,1), while machine learning approaches include LSTM and GRU.\"},{\"question\":\"How do machine learning models perform during the Covid-19 high-volatility period?\",\"answer\":\"The results show no evidence that machine learning methods provide a particular advantage when predicting during high-volatility events like Covid-19.\"}]","Market Volatility: Can Machine Learning Methods Enhance Volatility Forecasting? - Dissertation Abstract | PDF",1785940497,121,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"market-volatility-can-machine-learning-methods-enhance-volatility-forecasting-dissertation-abstract","",{"@graph":36,"@context":86},[37,54,69],{"@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/market-volatility-can-machine-learning-methods-enhance-volatility-forecasting-dissertation-abstract/127648/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main research question of the dissertation?","Question",{"text":76,"@type":77},"The dissertation examines whether machine learning methods can enhance volatility forecasting accuracy and outperform the HAR-RV econometric model.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which models are used as benchmarks and competitors?",{"text":81,"@type":77},"The benchmark is HAR-RV, compared against EWMA and GARCH(1,1), while machine learning approaches include LSTM and GRU.",{"name":83,"@type":74,"acceptedAnswer":84},"How do machine learning models perform during the Covid-19 high-volatility period?",{"text":85,"@type":77},"The results show no evidence that machine learning methods provide a particular advantage when predicting during high-volatility events like Covid-19.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]