Hidden Markov models for multi-scale time series : an application to stock market data

Over the last decades, hidden Markov models have emerged as a versatile class of statistical models for time series where the observed variables are driven by latent states. While conventional hidden Markov models are restricted to modeling single-scale data, economic variables are often observed at different temporal resolutions: an economy’s gross domestic product, for instance, is typically observed on a yearly, quarterly, or monthly basis, whereas stock prices are available daily or at even finer temporal resolutions. In this paper, we propose hierarchical hidden Markov models to incorporate such multi-scale data into a joint model, where we illustrate the suggested approach using 16 years of monthly trade volumes and daily log-returns of the Goldman Sachs stock.Publisher PD

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

Oelschläger L, Adam T. Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models. Statistical Modelling. 2023;23(2):107-126.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

Detecting bearish and bullish markets in financial time series using 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

Bayes Estimation of Latent Class Mixed Multinomial Probit Models

Oelschläger L, Bauer D. Bayes Estimation of Latent Class Mixed Multinomial Probit Models. Presented at the TRB Annual Meeting 2021.Discrete choice models lie at the heart of many transportation models. This holds true for mode choice models as well as for models of vehicle purchase decisions, to name just a few applications. Individual heterogeneity in preferences for different decision makers has been modelled by imposing mixing distributions on the coefficients. However, the literature does not provide much guidance so far for the specification of the mixing distributions apart from trial and error procedures using several standard parametric distributions. We propose an approach that is capable of approximating any underlying mixing distribution. The approach uses a Bayesian framework for estimating a latent class mixed multinomial probit model where the number of latent classes is updated within the algorithm on a weight-based strategy. Presenting simulation results, we demonstrate that the approach is suitable for guiding the specification of mixing distributions in empirical applications

Hidden Markov models for multi-scale time series:an application to stock market data

Over the last decades, hidden Markov models have emerged as a versatile class of statistical models for time series where the observed variables aredriven by latent states. While conventional hidden Markov models are restrictedto modeling single-scale data, economic variables are often observed at differenttemporal resolutions: an economy’s gross domestic product, for instance, is typically observed on a yearly, quarterly, or monthly basis, whereas stock prices areavailable daily or at even finer temporal resolutions. In this paper, we proposehierarchical hidden Markov models to incorporate such multi-scale data into ajoint model, where we illustrate the suggested approach using 16 years of monthlytrade volumes and daily log-returns of the Goldman Sachs stock

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

Oelschläger L, Adam T. Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models. Statistical Modelling. 2021: 1471082X211034048.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