Discovering stock dynamics through multidimensional volatility phases

DOI10.1080/14697681003743040zbMath1241.91141OpenAlexW1991837602MaRDI QIDQ2893204

Chii-Ruey Hwang, Hsieh Fushing, Shu-Chun Chen

Publication date: 26 June 2012

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697681003743040

zbMATH Keywords

pattern recognitionstochastic modelsmarket dynamicsdynamic modelscomputational financefinancial times seriesinvestor behaviour

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Numerical methods (including Monte Carlo methods) (91G60) Statistical methods; risk measures (91G70)

Related Items (4)

Unraveling S&P500 stock volatility and networks – an encoding-and-decoding approach ⋮ Empirical scaling laws and the aggregation of non-stationary data ⋮ Discovering focal regions of slightly-aggregated sparse signals ⋮ A note on testing regime switching assumption based on recurrence times

Cites Work

• The Pricing of Options and Corporate Liabilities
• Estimating the dimension of a model
• Lévy flights and related topics in physics. Proceedings of the international workshop, held at Nice, France, 27-30 June, 1994
• Relation between bid–ask spread, impact and volatility in order-driven markets
• Sampling Returns for Realized Variance Calculations: Tick Time or Transaction Time?
• The Price Variability-Volume Relationship on Speculative Markets

This page was built for publication: Discovering stock dynamics through multidimensional volatility phases