fHMM_sdds: Define state-dependent distributions

In fHMM: Fitting Hidden Markov Models to Financial Data

Define state-dependent distributions

Description

This helper function defines state-dependent distributions.

Usage

fHMM_sdds(sdds, states)

## S3 method for class 'fHMM_sdds'
print(x, ...)

Arguments

sdds	A character, specifying the state-dependent distribution. One of "normal" (the normal distribution), "lognormal" (the log-normal distribution), "t" (the t-distribution), "gamma" (the gamma distribution), "poisson" (the Poisson distribution). The distribution parameters, i.e. the mean mu, standard deviation sigma (not for the Poisson distribution), degrees of freedom df (only for the t-distribution), can be fixed via, e.g., "t(df = 1)" or "gamma(mu = 0, sigma = 1)". To fix different values of a parameter for different states, separate by "|", e.g. "poisson(mu = 1|2|3)". If hierarchy = TRUE, sdds must be a vector of length 2. The first entry corresponds to the coarse-scale layer, while the second entry corresponds to the fine-scale layer. By default, sdds = "normal" if hierarchy = FALSE and sdds = c("normal", "normal") if hierarchy = TRUE.
states	An integer, the number of states of the underlying Markov chain. If hierarchy = TRUE, states must be a vector of length 2. The first entry corresponds to the coarse-scale layer, while the second entry corresponds to the fine-scale layer. By default, states = 2 if hierarchy = FALSE and states = c(2, 2) if hierarchy = TRUE.
...	Currently not used.

Value

A list of length 1 (or 2 in the hierarchical case). Each element again is a list, containing

• the "name" of the distribution

• and a list "pars" of its parameters, where unknown parameters are set to NULL.

fHMM documentation built on April 3, 2025, 5:49 p.m.