Multivariate Forecasting of Bitcoin Volatility with Gradient Boosting: Deterministic, Probabilistic, and Feature Importance Perspectives
Faculty of Electrical Engineering, Czestochowa University of Technology（电子工程学院，捷斯托霍瓦技术大学）；Faculty of Computer Science and Artificial Intelligence, Czestochowa University of Technology（计算机科学与人工智能学院，捷斯托霍瓦技术大学）
This study investigates the application of the Light Gradient Boosting Machine (LGBM) model for both deterministic and probabilistic forecasting of Bitcoin realized volatility. Utilizing a comprehensive set of 69 predictors -- encompassing market, behavioral, and macroeconomic indicators -- we evaluate the performance of LGBM-based models and compare them with both econometric and machine learning baselines. For probabilistic forecasting, we explore two quantile-based approaches: direct quantile regression using the pinball loss function, and a residual simulation method that transforms point forecasts into predictive distributions. To identify the main drivers of volatility, we employ gain-based and permutation feature importance techniques, consistently highlighting the significance of trading volume, lagged volatility measures, investor attention, and market capitalization. The results demonstrate that LGBM models effectively capture the nonlinear and high-variance characteristics of cryptocurrency markets while providing interpretable insights into the underlying volatility dynamics.
In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market. We use the data of realized volatility collected from one of the largest Bitcoin digital trading offices in 2016 and 2017 as well as order information. Experiments are performed to evaluate a variety of statistical and machine learning approaches.
Understanding the variations in trading price (volatility), and its response to exogenous information, is a well-researched topic in finance. In this study, we focus on finding stable and accurate volatility predictors for a relatively new asset class of cryptocurrencies, in particular Bitcoin, using deep learning representations of public social media data obtained from Twitter. For our experiments, we extracted semantic information and user statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute frequency price data over a horizon of 144 days. Using this data, w…
Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks. The high volatility of Bitcoin and the complex factors affecting them make the study of effective price forecasting methods of great practical importance to financial investors and researchers worldwide. In this paper, we propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. Specifically, the Multi-scale…
Southeast University（东南大学）；National University of Singapore（新加坡国立大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
A deep-learning-based hybrid strategy for short-term load forecasting is presented. The strategy proposes a novel tree-based ensemble method Warm-start Gradient Tree Boosting (WGTB). Current strategies either ensemble submodels of a single type, which fail to take advantage of the statistical strengths of different inference models. Or they simply sum the outputs from completely different inference models, which doesn't maximize the potential of ensemble. Inspired by the bias-variance trade-off, WGTB is proposed and tailored to the great disparity among different inference models on accuracy, …
Semi-strong Efficient Market of Bitcoin and Twitter: an Analysis of Semantic Vector Spaces of Extracted Keywords and Light Gradient Boosting Machine Models
Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators
ESISA Analytica, Higher School of Engineering in Applied Sciences（ESISA分析与应用科学高等工程学院）；GAIA, Ericsson（GAIA、爱立信）；Fordham University（福特汉姆大学）；Penn State University（宾夕法尼亚州立大学）
Echo State Networks for Bitcoin Time Series Prediction
DFKI, Saarland Informatics Campus, Germany（德国达姆施塔特研究所，萨尔兰信息技术校园）；Politecnico di Milano, Italy（意大利米兰理工学院）；University of Stirling, UK（英国斯特林大学）；National Institute of Informatics, Japan（日本信息学研究院）
MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting
School of Art and Science University of Rochester（罗切斯特大学艺术与科学学院）；AGI Lab Beijing Institute of Mathematical Sciences and Applications（北京数学科学与应用研究院AGI实验室）
Humboldt University of Berlin（柏林洪堡大学）；University College London（伦敦大学学院）
The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models. Yet, existing methodologies fail to cope with the volatile nature of the emerging DAs. Many models have been proposed to address the unorthodox market dynamics and frequent disruptions in the microstructure caused by the non-stationarity, and peculiar statistics, in DA markets. However, they are either prone to the curse of dimensionality, as additional complexity is required to employ traditional theories, or they overfit historical patterns that may never repeat. Instead…
Probabilistic Forecasting Cryptocurrencies Volatility: From Point to Quantile Forecasts
Faculty of Electrical Engineering Częstochowa University of Technology（电气工程学院切斯托楚瓦技术大学）；Faculty of Economic Sciences（经济科学学院）；Nicolaus Copernicus University in Toruń（托伦尼科拉乌斯·哥白尼大学）；Prague University of Economics（布拉格经济大学）
Learning to Predict Short-Term Volatility with Order Flow Image Representation
School of Engineering；Technology University of New South Wales Canberra, Australia；School of Computing Australian National University Canberra, Australia
Risk-Aware Financial Forecasting Enhanced by Machine Learning and Intuitionistic Fuzzy Multi-Criteria Decision-Making
Sakarya University（萨卡里亚大学）；University of East Sarajevo（东萨拉热窝大学）；Korea University（高丽大学）；Necmettin Erbakan University（内吉梅丁·埃尔巴坎大学）；Singapore University of Social Sciences（新加坡社会科学大学）
TFT-ACB-XML: Decision-Level Integration of Customized Temporal Fusion Transformer and Attention-BiLSTM with XGBoost Meta-Learner for BTC Price Forecasting
University of Engineering and Applied Sciences (UEAS)（工程与应用科学大学（UEAS））；King Fahad University of Petroleum and Minerals (KFUPM)（法赫德国王石油与矿产大学（KFUPM））
