Abstract
This chapter presents a powerful class of probabilistic models for financial data. Many of these models overcome some of the severe stationarity limitations of the frequentist models in the previous chapters. The fitting procedure demonstrated is also different—the use of Kalman filtering algorithms for state-space models rather than maximum likelihood estimation or Bayesian inference. Simple examples of hidden Markov models and particle filters in finance and various algorithms are presented.
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Notes
- 1.
Dynamic Bayesian networks models are a graphical model used to model dynamic processes through hidden state evolution.
- 2.
With the exception of heteroscedastic modeling in Chap. 6.
- 3.
An acronym for Bayesian inference Using Gibbs Sampling.
- 4.
Sometimes the Gibbs sampler is referred to as data augmentation following this paper.
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Appendix
Appendix
Python Notebooks
The notebooks provided in the accompanying source code repository are designed to gain familiarity with how to implement the Viterbi algorithm and particle filtering for stochastic volatility model calibration. Further details of the notebooks are included in the README.md file.
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Dixon, M.F., Halperin, I., Bilokon, P. (2020). Probabilistic Sequence Modeling. In: Machine Learning in Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-41068-1_7
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