Copertina

Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chinazzo, Cristina, Jeleskovic, Vahidin
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Trading and Market Microstructure
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

An Empirical Analysis of Scam Tokens on Ethereum Blockchain
di: Jeleskovic, Vahidin
Pubblicazione: (2024)

Electricity Spot Prices Forecasting Using Stochastic Volatility Models
di: Batyrov, Andrei Renatovich
Pubblicazione: (2024)

Bitcoin ETF: Opportunities and risk
di: Wu, Di
Pubblicazione: (2024)

The Subtle Interplay between Square-root Impact, Order Imbalance & Volatility II: An Artificial Market Generator
di: Maitrier, Guillaume, et al.
Pubblicazione: (2025)

Scaling Laws And Statistical Properties of The Transaction Flows And Holding Times of Bitcoin
di: Sornette, Didier, et al.
Pubblicazione: (2024)

Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter
di: Tang, Siu Hin, et al.
Pubblicazione: (2023)

Volatility-based strategy on Chinese equity index ETF options
di: Yifeng, Peng
Pubblicazione: (2024)

Deep Learning for Short Term Equity Trend Forecasting: A Behavior Driven Multi Factor Approach
di: Luan, Yuqi
Pubblicazione: (2025)

Beta-Dependent Gamma Feedback and Endogenous Volatility Amplification in Option Markets
di: Dai, Haoying
Pubblicazione: (2025)

The Subtle Interplay between Square-root Impact, Order Imbalance & Volatility: A Unifying Framework
di: Maitrier, Guillaume, et al.
Pubblicazione: (2025)

Forecasting High Frequency Order Flow Imbalance
di: Anantha, Aditya Nittur, et al.
Pubblicazione: (2024)

E-TRENDS: Enhanced LSTM Trend Forecasting for Equities
di: Buchanan, Harris, et al.
Pubblicazione: (2026)

Hybrid Models for Financial Forecasting: Combining Econometric, Machine Learning, and Deep Learning Models
di: Stempień, Dominik, et al.
Pubblicazione: (2025)

FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics
di: Hossain, Mabsur Fatin Bin, et al.
Pubblicazione: (2024)

A Novel Approach to Queue-Reactive Models: The Importance of Order Sizes
di: Bodor, Hamza, et al.
Pubblicazione: (2024)

DEX Specs: A Mean Field Approach to DeFi Currency Exchanges
di: Bayraktar, Erhan, et al.
Pubblicazione: (2024)

The Impact of Designated Market Makers on Market Liquidity and Competition: A Simulation Approach
di: Zhou, Cong
Pubblicazione: (2024)

LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies
di: Kashif, Kamil, et al.
Pubblicazione: (2024)

The leverage effect and other stylized facts displayed by Bitcoin returns
di: Filho, F. N. M. de Sousa, et al.
Pubblicazione: (2020)

A New Traders' Game? -- Empirical Analysis of Response Functions in a Historical Perspective
di: Schuhmann, Cedric, et al.
Pubblicazione: (2025)

Arbitrage Analysis in Polymarket NBA Markets
di: Cheng, Guang, et al.
Pubblicazione: (2026)

Can market volumes reveal traders' rationality and a new risk premium?
di: Mariani, Francesca, et al.
Pubblicazione: (2024)

Liquidity-adjusted Return and Volatility, and Autoregressive Models
di: Deng, Qi, et al.
Pubblicazione: (2025)

High-Frequency Trading Liquidity Analysis | Application of Machine Learning Classification
di: Bhatia, Sid, et al.
Pubblicazione: (2024)

Advancing DeFi Analytics: Efficiency Analysis with Decentralized Exchanges Comparison Service
di: Onishchuk, Evgenii, et al.
Pubblicazione: (2024)

ESG driven pairs algorithm for sustainable trading: Analysis from the Indian market
di: Dutta, Eeshaan, et al.
Pubblicazione: (2024)

Ultrafast Extreme Events: Empirical Analysis of Mechanisms and Recovery in a Historical Perspective
di: Henrichs, Luca, et al.
Pubblicazione: (2025)

Time-Inhomogeneous Volatility Aversion for Financial Applications of Reinforcement Learning
di: Cacciamani, Federico, et al.
Pubblicazione: (2026)

A Modern Paradigm for Algorithmic Trading
di: Glattfelder, James B., et al.
Pubblicazione: (2025)

A Simple Strategy to Deal with Toxic Flow
di: Cartea, Álvaro, et al.
Pubblicazione: (2025)

Calculating Profits and Losses for Algorithmic Trading Strategies: A Short Guide
di: Glattfelder, James B., et al.
Pubblicazione: (2024)

Institutional Backing and Crypto Volatility: A Hybrid Framework for DeFi Stabilization
di: Sovbetov, Ihlas
Pubblicazione: (2025)

Learning to Predict Short-Term Volatility with Order Flow Image Representation
di: Lensky, Artem, et al.
Pubblicazione: (2023)

A Validated Volatility-Volume-Gap Classifier for Regime Identification in MNQ Intraday Data
di: Mesfin, Mathias
Pubblicazione: (2026)

Nash Equilibrium between Brokers and Traders
di: Cartea, Álvaro, et al.
Pubblicazione: (2024)

Strategic Learning and Trading in Broker-Mediated Markets
di: Aqsha, Alif, et al.
Pubblicazione: (2024)

Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying
di: Macrì, Andrea, et al.
Pubblicazione: (2024)

Deep reinforcement learning with positional context for intraday trading
di: Goluža, Sven, et al.
Pubblicazione: (2024)

Stochastic Gradient Descent in the Optimal Control of Execution Costs
di: Kolev, Simeon
Pubblicazione: (2024)

An Application of the Ornstein-Uhlenbeck Process to Pairs Trading
di: Suchato, Jirat, et al.
Pubblicazione: (2024)