Application of Hidden Markov Model in Financial Time Series Data

Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series

Volatility: a hidden Markov process in financial time series.

Volatility characterizes the amplitude of price return fluctuations. It is a central magnitude in finance closely related to the risk of holding a certain asset. Despite its popularity on trading floors, volatility is unobservable and only the price is known. Diffusion theory has many common points with the research on volatility, the key of the analogy being that volatility is a time-dependent...

Clustering financial time series: New insights from an extended hidden Markov model

In recent years, large amounts of financial data have become available for analysis. We propose exploring returns from 21 European stock markets by model-based clustering of regime switching models. These econometric models identify clusters of time series with similar dynamic patterns and moreover allow relaxing assumptions of existing approaches, such as the assumption of conditional Gaussian...

Hidden Markov model segmentation of hydrological and enviromental time series

Motivated by Hubert's segmentation procedure [16, 17], we discuss the application of hidden Markov models (HMM) to the segmentation of hydrological and enviromental time series. We use a HMM algorithm which segments time series of several hundred terms in a few seconds and is compu-tationally feasible for even longer time series. The segmentation algorithm computes the Maximum Likelihood segmen...

Bayesian Hidden Markov Models for Financial Data

Hidden Markov Models, also known as Markov Switching Models, can be considered an extension of mixture models, allowing for dependent observations. The main problem associated with Hidden Markov Models is represented by the choice of the number of regimes, i.e. the number of the generating data processes, which differ one from another just for the value of the parameters. Applying a hierarchica...