Stylised facts of financial time series and hidden Markov models in continuous time

DOI10.1080/14697688.2015.1004801zbMath1398.91691OpenAlexW2015095252WikidataQ60398281 ScholiaQ60398281MaRDI QIDQ4683084

Peter Nystrup, Erik Lindström, Henrik Madsen

Publication date: 19 September 2018

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

Full work available at URL: https://backend.orbit.dtu.dk/ws/files/106894229/Stylised_facts_of_financial_time_series_and_hidden_Markov_models_in_continuous_time.pdf

zbMATH Keywords

long memoryhidden Markov modelscontinuous timevolatility clusteringleptokurtosisdaily returns

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Statistical methods; risk measures (91G70) Applications of continuous-time Markov processes on discrete state spaces (60J28)

Related Items

Dynamic portfolio optimization across hidden market regimes ⋮ Non-stationary data segmentation with hidden evidential semi-Markov chains ⋮ Dynamic portfolio optimization across hidden market regimes ⋮ Multi-period portfolio selection with drawdown control ⋮ BENCHOP – The BENCHmarking project in option pricing

Uses Software

• hsmm
• R
• msm

Cites Work

• Hidden Markov models with arbitrary state dwell-time distributions
• hsmm -- an R package for analyzing hidden semi-Markov models
• Stylized facts of financial time series and hidden semi-Markov models
• A comparison of some criteria for states selection in the latent Markov model for longitudinal data
• Inference in hidden Markov models.
• Finite mixture and Markov switching models.
• A Pricing Process with Stochastic Volatility Controlled by a Semi-Markov Process
• Non‐trading day effects in asymmetric conditional and stochastic volatility models
• Empirical properties of asset returns: stylized facts and statistical issues
• Hidden Markov Models for Time Series