fHMM: Hidden Markov Models for Financial Time Series in R
Hidden Markov models constitute a versatile class of statistical models for time series that are driven by hidden states. In financial applications, the hidden states can often be linked to market regimes such as bearish and bullish markets or recessions and periods of economics growth. To give an...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2024-06-01
|
| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4736 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850035351644536832 |
|---|---|
| author | Lennart Oelschläger Timo Adam Rouven Michels |
| author_facet | Lennart Oelschläger Timo Adam Rouven Michels |
| author_sort | Lennart Oelschläger |
| collection | DOAJ |
| description |
Hidden Markov models constitute a versatile class of statistical models for time series that are driven by hidden states. In financial applications, the hidden states can often be linked to market regimes such as bearish and bullish markets or recessions and periods of economics growth. To give an example, when the market is in a nervous state, corresponding stock returns often follow some distribution with relatively high variance, whereas calm periods are often characterized by a different distribution with relatively smaller variance. Hidden Markov models can be used to explicitly model the distribution of the observations conditional on the hidden states and the transitions between states, and thus help us to draw a comprehensive picture of market behavior. While various implementations of hidden Markov models are available, a comprehensive R package that is tailored to financial applications is still lacking. In this paper, we introduce the R package fHMM, which provides various tools for applying hidden Markov models to financial time series. It contains functions for fitting hidden Markov models to data, conducting simulation experiments, and decoding the hidden state sequence. Furthermore, functions for model checking, model selection, and state prediction are provided. In addition to basic hidden Markov models, hierarchical hidden Markov models are implemented, which can be used to jointly model multiple data streams that were observed at different temporal resolutions. The aim of the fHMM package is to give R users with an interest in financial applications access to hidden Markov models and their extensions.
|
| format | Article |
| id | doaj-art-e476da767a0548269f39c3f9906926c9 |
| institution | DOAJ |
| issn | 1548-7660 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-e476da767a0548269f39c3f9906926c92025-08-20T02:57:30ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602024-06-01109110.18637/jss.v109.i09fHMM: Hidden Markov Models for Financial Time Series in RLennart Oelschläger0Timo Adam1Rouven Michels2Bielefeld UniversityUniversity of St AndrewsBielefeld University Hidden Markov models constitute a versatile class of statistical models for time series that are driven by hidden states. In financial applications, the hidden states can often be linked to market regimes such as bearish and bullish markets or recessions and periods of economics growth. To give an example, when the market is in a nervous state, corresponding stock returns often follow some distribution with relatively high variance, whereas calm periods are often characterized by a different distribution with relatively smaller variance. Hidden Markov models can be used to explicitly model the distribution of the observations conditional on the hidden states and the transitions between states, and thus help us to draw a comprehensive picture of market behavior. While various implementations of hidden Markov models are available, a comprehensive R package that is tailored to financial applications is still lacking. In this paper, we introduce the R package fHMM, which provides various tools for applying hidden Markov models to financial time series. It contains functions for fitting hidden Markov models to data, conducting simulation experiments, and decoding the hidden state sequence. Furthermore, functions for model checking, model selection, and state prediction are provided. In addition to basic hidden Markov models, hierarchical hidden Markov models are implemented, which can be used to jointly model multiple data streams that were observed at different temporal resolutions. The aim of the fHMM package is to give R users with an interest in financial applications access to hidden Markov models and their extensions. https://www.jstatsoft.org/index.php/jss/article/view/4736 |
| spellingShingle | Lennart Oelschläger Timo Adam Rouven Michels fHMM: Hidden Markov Models for Financial Time Series in R Journal of Statistical Software |
| title | fHMM: Hidden Markov Models for Financial Time Series in R |
| title_full | fHMM: Hidden Markov Models for Financial Time Series in R |
| title_fullStr | fHMM: Hidden Markov Models for Financial Time Series in R |
| title_full_unstemmed | fHMM: Hidden Markov Models for Financial Time Series in R |
| title_short | fHMM: Hidden Markov Models for Financial Time Series in R |
| title_sort | fhmm hidden markov models for financial time series in r |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4736 |
| work_keys_str_mv | AT lennartoelschlager fhmmhiddenmarkovmodelsforfinancialtimeseriesinr AT timoadam fhmmhiddenmarkovmodelsforfinancialtimeseriesinr AT rouvenmichels fhmmhiddenmarkovmodelsforfinancialtimeseriesinr |