Forecasting the crude oil prices based on econophysics and Bayesian approach

DOI10.1016/j.physa.2020.124663OpenAlexW3022628943MaRDI QIDQ2139336

Na Leng, Jiang-Cheng Li

Publication date: 17 May 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2020.124663

zbMATH Keywords

entropyBayesian methodmaximum likelihood methodHeston modeleconophysicscrude oil market

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Related Items (5)

Applying spline-based phase analysis to macroeconomic dynamics ⋮ Dynamic forecasting performance and liquidity evaluation of financial market by econophysics and Bayesian methods ⋮ Forecast the role of GCC financial stress on oil market and GCC financial markets using convolutional neural networks ⋮ Forecasting price of financial market crash via a new nonlinear potential GARCH model ⋮ Advanced strategies of portfolio management in the Heston market model

Cites Work

• Unnamed Item
• A Mathematical Theory of Communication
• Role of noise in a market model with stochastic volatility
• Comparison between the probability distribution of returns in the Heston model and empirical data for stock indexes
• An approach for measuring corporation financial stability by econophysics and Bayesian method
• Coherence resonance-like and efficiency of financial market
• VOLATILITY EFFECTS ON THE ESCAPE TIME IN FINANCIAL MARKET MODELS
• Probability distribution of returns in the Heston model with stochastic volatility*
• Gibbs vs Boltzmann Entropies
• A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
• Introduction to Econophysics
• Irregularity, volatility, risk, and financial market time series

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