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Clustering financial time series: New insights from an extended hidden Markov model · Study

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ArticleClustering financial time series: New insights from an extended hidden Markov model

José G. Dias2014

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Articlegreen2014

Clustering financial time series: New insights from an extended hidden Markov model

José G. Dias · Jeroen Kornelis Vermunt · Sofía B. Ramos

103 citations·0/0 files stored·European Journal of Operational Research

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Financial Risk and Volatility Modeling Stock Market Forecasting Methods Bayesian Methods and Mixture Models

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References

1989A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 1965The Behavior of Stock-Market Prices 1963The Variation of Certain Speculative Prices 1997The variation of certain speculative prices 1999Hierarchical structure in financial markets 1994Autoregressive conditional heteroskedasticity and changes in regime 2002International Asset Allocation With Regime Shifts

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2021Multi-period portfolio optimization using model predictive control with mean-variance and risk parity frameworks 2017Volatility forecasting of crude oil market: A new hybrid method 2023Forecasting movements of stock time series based on hidden state guided deep learning approach 2018Multi-period portfolio selection with drawdown control 2021Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models

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Year2014

Typearticle

Published2014-12-30

VenueEuropean Journal of Operational Research

ISSN-L0377-2217

Languageen

Citations103

Files0 public / 0

ShelfFinancial Risk and Volatility Modeling

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doi10.1016/j.ejor.2014.12.041

openalex_workW1978637569

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2019

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