2018/11/28 by Eric Benhamou, Benhamou, Eric · 1 citation
Decision Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.1811.11618
openalex publication_date 2018/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we revisit the Kalman filter theory. After giving the\nintuition on a simplified financial markets example, we revisit the maths\nunderlying it. We then show that Kalman filter can be presented in a very\ndifferent fashion using graphical models. This enables us to establish the\nconnection between Kalman filter and Hidden Markov Models. We then look at\ntheir application in financial markets and provide various intuitions in terms\nof their applicability for complex systems such as financial markets. Although\nthis paper has been written more like a self contained work connecting Kalman\nfilter to Hidden Markov Models and hence revisiting well known and establish\nresults, it contains new results and brings additional contributions to the\nfield. First, leveraging on the link between Kalman filter and HMM, it gives\nnew algorithms for inference for extended Kalman filters. Second, it presents\nan alternative to the traditional estimation of parameters using EM algorithm\nthanks to the usage of CMA-ES optimization. Third, it examines the application\nof Kalman filter and its Hidden Markov models version to financial markets,\nproviding various dynamics assumptions and tests. We conclude by connecting\nKalman filter approach to trend following technical analysis system and showing\ntheir superior performances for trend following detection.\n