Stock volatility as an anomalous diffusion process
Anomalous diffusion (AD) describes transport phenomena where the mean-square displacement (MSD) of a particle does not scale linearly with time, deviating from classical diffusion. This behavior, often linked to non-equilibrium phenomena, sheds light on the underlying mechanisms in various systems,...
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| Language: | English |
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AIMS Press
2024-12-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20241663 |
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| author | Rubén V. Arévalo J. Alberto Conejero Òscar Garibo-i-Orts Alfred Peris |
| author_facet | Rubén V. Arévalo J. Alberto Conejero Òscar Garibo-i-Orts Alfred Peris |
| author_sort | Rubén V. Arévalo |
| collection | DOAJ |
| description | Anomalous diffusion (AD) describes transport phenomena where the mean-square displacement (MSD) of a particle does not scale linearly with time, deviating from classical diffusion. This behavior, often linked to non-equilibrium phenomena, sheds light on the underlying mechanisms in various systems, including biological and financial domains.Integrating insights from anomalous diffusion into financial analysis could significantly improve our understanding of market behaviors, similar to their impacts on biological systems. In financial markets, accurately estimating asset volatility—whether historical or implied—is vital for investors.We introduce a novel methodology to estimate the volatility of stocks and similar assets, combining anomalous diffusion principles with machine learning. Our architecture combines convolutional and recurrent neural networks (bidirectional long short-term memory units). Our model computes the diffusion exponent of a financial time series to measure its volatility and it categorizes market movements into five diffusion models: annealed transit time motion (ATTM), continuous time random walk (CTRW), fractional Brownian motion (FBM), Lévy walk (LW), and scaled Brownian motion (SBM).Our findings suggest that the diffusion exponent derived from anomalous diffusion processes provides insightful and novel perspectives on stock market volatility. By differentiating between subdiffusion, superdiffusion, and normal diffusion, our methodology offers a more nuanced understanding of market dynamics than traditional volatility metrics. |
| format | Article |
| id | doaj-art-2467e082f6b84d96b35cf1af81c427d4 |
| institution | Kabale University |
| issn | 2473-6988 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj-art-2467e082f6b84d96b35cf1af81c427d42025-01-23T07:53:25ZengAIMS PressAIMS Mathematics2473-69882024-12-01912349473496510.3934/math.20241663Stock volatility as an anomalous diffusion processRubén V. Arévalo0J. Alberto Conejero1Òscar Garibo-i-Orts2Alfred Peris3I.U. de Matemàtica Pura i Aplicada, Universitat Politècnica de València, 46022 Valencia, Spain; rubenvicentearevalo@gmail.com, aconejero@upv.es, aperis@mat.upv.esI.U. de Matemàtica Pura i Aplicada, Universitat Politècnica de València, 46022 Valencia, Spain; rubenvicentearevalo@gmail.com, aconejero@upv.es, aperis@mat.upv.esI.U. de Matemàtica Pura i Aplicada, Universitat Politècnica de València, 46022 Valencia, Spain; rubenvicentearevalo@gmail.com, aconejero@upv.es, aperis@mat.upv.esI.U. de Matemàtica Pura i Aplicada, Universitat Politècnica de València, 46022 Valencia, Spain; rubenvicentearevalo@gmail.com, aconejero@upv.es, aperis@mat.upv.esAnomalous diffusion (AD) describes transport phenomena where the mean-square displacement (MSD) of a particle does not scale linearly with time, deviating from classical diffusion. This behavior, often linked to non-equilibrium phenomena, sheds light on the underlying mechanisms in various systems, including biological and financial domains.Integrating insights from anomalous diffusion into financial analysis could significantly improve our understanding of market behaviors, similar to their impacts on biological systems. In financial markets, accurately estimating asset volatility—whether historical or implied—is vital for investors.We introduce a novel methodology to estimate the volatility of stocks and similar assets, combining anomalous diffusion principles with machine learning. Our architecture combines convolutional and recurrent neural networks (bidirectional long short-term memory units). Our model computes the diffusion exponent of a financial time series to measure its volatility and it categorizes market movements into five diffusion models: annealed transit time motion (ATTM), continuous time random walk (CTRW), fractional Brownian motion (FBM), Lévy walk (LW), and scaled Brownian motion (SBM).Our findings suggest that the diffusion exponent derived from anomalous diffusion processes provides insightful and novel perspectives on stock market volatility. By differentiating between subdiffusion, superdiffusion, and normal diffusion, our methodology offers a more nuanced understanding of market dynamics than traditional volatility metrics.https://www.aimspress.com/article/doi/10.3934/math.20241663volatilityanomalous diffusionrecurrent neural networkstock marketsandi-challenge |
| spellingShingle | Rubén V. Arévalo J. Alberto Conejero Òscar Garibo-i-Orts Alfred Peris Stock volatility as an anomalous diffusion process AIMS Mathematics volatility anomalous diffusion recurrent neural network stock markets andi-challenge |
| title | Stock volatility as an anomalous diffusion process |
| title_full | Stock volatility as an anomalous diffusion process |
| title_fullStr | Stock volatility as an anomalous diffusion process |
| title_full_unstemmed | Stock volatility as an anomalous diffusion process |
| title_short | Stock volatility as an anomalous diffusion process |
| title_sort | stock volatility as an anomalous diffusion process |
| topic | volatility anomalous diffusion recurrent neural network stock markets andi-challenge |
| url | https://www.aimspress.com/article/doi/10.3934/math.20241663 |
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