Modeling high frequency stock market data by using stochastic models

DOI10.1080/07362994.2021.1942046zbMath1489.91314OpenAlexW3175171800MaRDI QIDQ5085210

Osei K. Tweneboah, Maria Christina Mariani

Publication date: 27 June 2022

Published in: Stochastic Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07362994.2021.1942046

zbMATH Keywords

stochastic differential equationstock marketLévy processfinancial time seriesOrnstein-Uhlenbeck processeshigh frequency

Mathematics Subject Classification ID

Financial applications of other theories (91G80) Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) (60J70)

Cites Work

• Stochastic differential equations applied to the study of geophysical and financial time series
• Analysis of the Lehman Brothers collapse and the flash crash event by applying wavelets methodologies
• Estimation of stochastic volatility by using Ornstein-Uhlenbeck type models
• Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics
• Detecting market crashes by analysing long-memory effects using high-frequency data
• Integrated OU Processes and Non‐Gaussian OU‐based Stochastic Volatility Models
• Sequential Hypothesis Testing in Machine Learning, and Crude Oil Price Jump Size Detection

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