scholarly journals Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters

2016 ◽  
Vol 36 (8) ◽  
pp. 989-1002 ◽  
Author(s):  
Peter Nystrup ◽  
Henrik Madsen ◽  
Erik Lindström
Keyword(s):  
Time Series ◽  
Hidden Markov Models ◽  
Long Memory ◽  
Markov Models ◽  
Financial Time Series ◽  
Hidden Markov ◽  
Time Varying ◽  
Varying Parameters ◽  
Financial Time ◽  
Time Varying Parameters

scholarly journals Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models

2021 ◽  
pp. 1471082X2110340
Author(s):  
Lennart Oelschläger ◽  
Timo Adam
Keyword(s):  
Time Series ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Financial Time Series ◽  
Hidden Markov ◽  
Financial Time ◽  
State Process ◽  
Bank Stock ◽  
Hierarchical Hidden Markov Models

Financial markets exhibit alternating periods of rising and falling prices. Stock traders seeking to make profitable investment decisions have to account for those trends, where the goal is to accurately predict switches from bullish to bearish markets and vice versa. Popular tools for modelling financial time series are hidden Markov models, where a latent state process is used to explicitly model switches among different market regimes. In their basic form, however, hidden Markov models are not capable of capturing both short- and long-term trends, which can lead to a misinterpretation of short-term price fluctuations as changes in the long-term trend. In this article, we demonstrate how hierarchical hidden Markov models can be used to draw a comprehensive picture of market behaviour, which can contribute to the development of more sophisticated trading strategies. The feasibility of the suggested approach is illustrated in two real-data applications, where we model data from the Deutscher Aktienindex and the Deutsche Bank stock. The proposed methodology is implemented in the R package fHMM, which is available on CRAN.

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

2015 ◽  
Vol 15 (9) ◽  
pp. 1531-1541 ◽  
Author(s):  
Peter Nystrup ◽  
Henrik Madsen ◽  
Erik Lindström
Keyword(s):  
Time Series ◽  
Hidden Markov Models ◽  
Continuous Time ◽  
Markov Models ◽  
Financial Time Series ◽  
Hidden Markov ◽  
Financial Time

Robust Diagnosis of Operating Mode Based on Time-Varying Hidden Markov Models

2016 ◽  
Vol 63 (2) ◽  
pp. 1142-1152 ◽  
Author(s):  
Nima Sammaknejad ◽  
Biao Huang ◽  
Yaojie Lu
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Operating Mode ◽  
Time Varying

scholarly journals An Extended Time Series Algorithm for Modal Identification from Nonstationary Ambient Response Data Only

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Chang-Sheng Lin ◽  
Dar-Yun Chiang ◽  
Tse-Chuan Tseng
Keyword(s):  
Time Series ◽  
Arma Model ◽  
Identification Problem ◽  
Nonstationary Time Series ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Time Varying ◽  
Varying Parameters ◽  
Response Data ◽  
Time Varying Parameters

Modal Identification is considered from response data of structural systems under nonstationary ambient vibration. The conventional autoregressive moving average (ARMA) algorithm is applicable to perform modal identification, however, only for stationary-process vibration. The ergodicity postulate which has been conventionally employed for stationary processes is no longer valid in the case of nonstationary analysis. The objective of this paper is therefore to develop modal-identification techniques based on the nonstationary time series for linear systems subjected to nonstationary ambient excitation. Nonstationary ARMA model with time-varying parameters is considered because of its capability of resolving general nonstationary problems. The parameters of moving averaging (MA) model in the nonstationary time-series algorithm are treated as functions of time and may be represented by a linear combination of base functions and therefore can be used to solve the identification problem of time-varying parameters. Numerical simulations confirm the validity of the proposed modal-identification method from nonstationary ambient response data.

scholarly journals Hidden Markov Models for Time Series: An Introduction Using R (2nd Edition)

2017 ◽  
Vol 80 (Book Review 1) ◽  
Author(s):  
Abdolvahab Khademi
Keyword(s):  
Time Series ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Influence of some ARFIMA model parameters on the accuracy of financial time series forecasting

2021 ◽  
Vol 62 ◽  
pp. 85-100
Author(s):  
Robert Garafutdinov ◽  
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Model Parameters ◽  
Memory Length ◽  
Financial Time ◽  
True Value ◽  
Arfima Model ◽  
Practical Recommendations

The influence of ARFIMA model parameters on the accuracy of financial time series forecasting on the example of artificially generated long memory series and daily log returns of RTS index is investigated. The investigated parameters are deviation of the integration order value from its «true» value, as well as the memory «length» considered by the model. Based on the research results, some practical recommendations for modeling using ARFIMA have been formulated.

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