Can Markov switching model generate long memory?

Long memory with Markov-Switching GARCH

Exact Calculation of Optimal Filters in Hidden Markov Switching Long-Memory Chain

This paper considers the problem of continuous state estimation in the presence of random switches. There are three random chains ) ..., , ( 1 1 N N X X X = , ) ..., , ( 1 1 N N R R R = , and ) ..., , ( 1 1 N N Y Y Y = . The random variables i X , i Y , and i R take their values from q R , m R , and { } s S ..., , 1 = , respectively. N Y1 is observed, N X1 and N R1 are hidden, and the problem i...

Long memory and nonlinearities in realized volatility: A Markov switching approach

Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. We consider an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate parameters, latent process and pre...

Lecture on the Markov Switching Model

Simultaneous Equations in the Markov-Switching Model

In this paper, Hamilton’s (1989) Markov-switching model is extended to the simultaneous equations model. Using a framework for an instrumental variable interpretation of full information maximum likelihood (FIML) by Hausman (1975), we can deal with the problem of simultaneous equations based on the Hamilton filter. When we compared the proposed FIML Markov-switching model to LIML Markovswitchin...