Infinite-State Markov-Switching for Dynamic Volatility

Dufays, Arnaud

doi:10.1093/jjfinec/nbv017

Cited by 25 publications

(14 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this survey we presented the infinite hidden Markov model [19,20], a relatively recent development in Statistics which has already found diverse applicability, among other fields, in Genomics [40], Genetics [41], Finance [42,43], Tracking [44,45], Machine learning [46,47], and also Biophysics [22,23,24].…”

Section: Perspectivesmentioning

confidence: 99%

An Introduction to Infinite HMMs for Single-Molecule Data Analysis

Sgouralis

Pressé

2017

Biophysical Journal

175

View full text Add to dashboard Cite

The hidden Markov model (HMM) has been a workhorse of single molecule data analysis and is now commonly used as a standalone tool in time series analysis or in conjunction with other analyses methods such as tracking. Here we provide a conceptual introduction to an important generalization of the HMM which is poised to have a deep impact across Biophysics: the infinite hidden Markov model (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM. While the current literature on the iHMM is primarily intended for audiences in Statistics, the idea is powerful and the iHMM's breadth in applicability outside Machine Learning and Data Science warrants a careful exposition. Here we explain the key ideas underlying the iHMM with a special emphasis on implementation and provide a description of a code we are making freely available. In a companion article, we provide an important extension of the iHMM to accommodate complications such as drift.

show abstract

Section: Perspectivesmentioning

confidence: 99%

An Introduction to Infinite HMMs for Single-Molecule Data Analysis

Sgouralis

Pressé

2017

Biophysical Journal

175

View full text Add to dashboard Cite

show abstract

“…The infinite hidden Markov model (IHMM), which is a Bayesian nonparametric model, allows for a very flexible conditional distribution that can change over time. Applications of IHMM include Jochmann (), Dufays (), Song (), Carpantier and Dufays () and Maheu and Yang ().…”

Section: Introductionmentioning

confidence: 99%

Improving Markov switching models using realized variance

Liu

Maheu

2017

J of Applied Econometrics

View full text Add to dashboard Cite

This paper proposes a class of models that jointly model returns and ex-post variance measures under a Markov switching framework. Both univariate and multivariate return versions of the model are introduced. Bayesian estimation can be conducted under a fixed dimension state space or an infinite one. The proposed models can be seen as nonlinear common factor models subject to Markov switching and are able to exploit the information content in both returns and ex-post volatility measures. Applications to U.S. equity returns and foreign exchange rates compare the proposed models to existing alternatives. The empirical results show that the joint models improve density forecasts for returns and point predictions of return variance. The joint Markov switching models can increase the precision of parameter estimates and sharpen the inference of the latent state variable.Keywords: infinite hidden Markov model, realized covariance, density forecast, MCMC * We thank Qiao Yang for comments. Maheu is grateful to the SSHRC for financial support.

show abstract

“…DPs have also been used extensively in economics and finance to model unknown distributions (see Chib & Tiwari (1988), Chib & Hamilton (2002), Hirano (2002), Jensen & Maheu (2010), and Bassetti et al (2014)). In addition, Song (2014), Dufays (2015), Jochmann (2015) and Jin & Maheu (2016) apply the DP to structural break models to allow for an infinite number of possible regimes.…”

Section: Dirichlet Process Priormentioning

confidence: 99%

Untitled

Fisher

Jensen

2018

View full text Add to dashboard Cite

Change point models using hierarchical priors share in the information of each regime when estimating the parameter values of a regime. Because of this sharing, hierarchical priors have been very successful when estimating the parameter values of short-lived regimes and predicting the out-of-sample behavior of the regime parameters. However, the hierarchical priors have been parametric. Their parametric nature leads to global shrinkage that biases the estimates of the parameter coefficient of extraordinary regimes toward the value of the average regime. To overcome this shrinkage, we model the hierarchical prior nonparametrically by letting the hyperparameter's prior-in other words, the hyperprior-be unknown and modeling it with a Dirichlet processes prior. To apply a nonparametric hierarchical prior to the probability of a break occurring, we extend the change point model to a multiple-change-point panel model. The hierarchical prior then shares in the cross-sectional information of the break processes to estimate the transition probabilities. We apply our multiple-change-point panel model to a longitudinal data set of actively managed, U.S. equity, mutual fund returns to measure fund performance and investigate the chances of a skilled fund being skilled in the future. JEL classification: C11, C14, C41, G11, G17

show abstract

Infinite-State Markov-Switching for Dynamic Volatility

Cited by 25 publications

References 47 publications

An Introduction to Infinite HMMs for Single-Molecule Data Analysis

An Introduction to Infinite HMMs for Single-Molecule Data Analysis

Improving Markov switching models using realized variance

Untitled

Contact Info

Product

Resources

About