[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120187-en":3,"doc-seo-120187-105":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},120187,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting VIX with Adaptive Machine Learning","This paper investigates the predictability of the CBOE Volatility Index (VIX) and identifies the sources behind its forecastable patterns. Using machine learning with dynamic training and nonlinear methods, it builds models that predict the next-day directional movement of VIX with accuracy higher than prior studies. A large set of economic variables improves explanatory power, while US weekly jobless claims emerge as the most influential predictor. Although spikes remain difficult, adaptive algorithms show strong robustness to new data and support more resilient quantitative trading and risk management.","Predicting VIX with Adaptive Machine Learning  \nYunfei Bai* and Charlie X. Cai**  \nThis draft: Oct 2024  \nAbstract  \nThis paper investigates the predictability of the CBOE Volatility Index (VIX) and explores the sources of its predictability using machine learning (ML) techniques. We establish that daily VIX can be predicted with higher accuracy than previously documented, yielding forecasts of significant economic value. Our analysis underscores the efficacy of dynamic training, nonlinear methods and a comprehensive set of economic variables in predicting VIX trends. We identify the weekly jobless claim data as a pivotal variable, revealing its substantial influence on market volatility, an area not extensively explored in prior research. While accurately forecasting VIX spikes poses a challenge, our algorithms demonstrate remarkable adaptability to new data, thereby significantly enhancing the resilience of trading strategies. This research not only contributes to the understanding of VIX predictability but also offers valuable insights for the development of more robust quantitative investment and risk management strategies.  \nKeywords: Machine Learning, AutoML, Explainable AI, VIX, Predictability, Forecasting, Quantitative Trading, Big Data, S&P 500, Futures, US markets GEL codes: G0, G17, C52, C55, C58,  \n* PhD, AI/ML and Big Data Consultant.  \n** Corresponding Author, Professor of Finance, Liverpool University School of Management, University of Liverpool, Liverpool, UK. Email: [busxc@liverpool.ac.uk. Website: www.CharliexCai.info](busxc@liverpool.ac.uk. Website: www.CharliexCai.info)  \nAcknowledgement:  \nWe thank the associate editor and referee for their constructive advice and Guofu Zhou for his insightful comments on an early draft of this paper. We also thank Giuliano De Rossi for sharing his working paper with us in the early stages ofthe project. All errors are our own.  \n1 Introduction  \nThe CBOE Volatility Index (VIX), often referred to as the 'fear index' (Whaley, 2000), is a key forward-looking indicator for market participants and policymakers. Despite its prominence, VIX predictability remains challenging due to traditional reliance on limited predictors and linear models. This paper leverages recent Machine Learning (ML) advancements to address these limitations by exploring the predictability of the VIX using a wide range of economic indicators, focusing on forecasting accuracy and economic relevance, and examining the sources and constraints of this predictability.  \nWhile finance-ML literature has largely focused on cross-sectional return forecasting, research on time series volatility forecasting remains limited. Our study bridges this gap by predicting the VIX’s directional movement for the following day, aligning with binary investment decisions (long or short). We compile 278 features across 14 categories from Bloomberg, including global markets, macroeconomic data, and seasonality factors, ensuring real-time data availability without look-back bias.  \nWe employ various ML algorithms, ranging from Naïve Bayes and Logistic Regression to Decision Trees, Random Forests, Adaptive Boosting (AB), and Multi-Layer Perceptron, alongside an ensemble model. A novel crossvalidation strategy maintains the time-series integrity of VIX data (Bergmeir et al., 2018) . Our study segments the data into three periods: In-Sample (pre- 2009) for training, Out-of-Sample (late 2009) for validation, and Implementation (2010-2020) for testing.  \nKey findings reveal that Adaptive Boosting (AB) achieved the highest validation accuracy (68.2%), outperforming other models and demonstrating resilience against overfitting. Moreover, AB’s forecasts delivered an annualized return of 225% in a simulated long/short VIX investment strategy, with a Sharpe ratio of 1.7, underscoring its practical value. Our economic evaluation shows that VIX forecasts have significant applications in valuation and risk management models. Additionally, the","cbCaik4G3xc46G27","https://ap.wps.com/l/cbCaik4G3xc46G27","pdf",1296298,1,62,"English","en",105,"# Introduction\n## VIX as a fear index and why predictability is difficult\n## Data construction and feature design\n## Machine learning models and cross-validation\n## Key findings and economic evaluation\n## Variable importance and further tests","[{\"question\":\"How does the paper define the prediction task for VIX?\",\"answer\":\"It predicts VIX’s directional movement for the following day, mapping forecasts to binary investment decisions such as long or short.\"},{\"question\":\"Which variable is identified as the most influential for VIX predictability?\",\"answer\":\"The US weekly jobless claim data is highlighted as the pivotal variable, followed by seasonality-related factors like days until VIX futures expiration.\"},{\"question\":\"How do the best-performing models handle unexpected market events?\",\"answer\":\"They demonstrate adaptability to new data, recovering losses more efficiently than non-model strategies during events such as the 2010 Flash Crash and the 2016 Brexit referendum.\"}]","Predicting VIX with Adaptive Machine Learning | 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does the paper define the prediction task for VIX?","Question",{"text":75,"@type":76},"It predicts VIX’s directional movement for the following day, mapping forecasts to binary investment decisions such as long or short.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which variable is identified as the most influential for VIX predictability?",{"text":80,"@type":76},"The US weekly jobless claim data is highlighted as the pivotal variable, followed by seasonality-related factors like days until VIX futures expiration.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the best-performing models handle unexpected market events?",{"text":84,"@type":76},"They demonstrate adaptability to new data, recovering losses more efficiently than non-model strategies during events such as the 2010 Flash Crash and the 2016 Brexit 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