Controls
In fHMM: Fitting Hidden Markov Models to Financial Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "fHMM-"
)

The {fHMM} package allows for multiple hidden Markov model specifications, including different data transformations, state-dependent distributions, and a hierarchical model structure. This vignette^[This vignette was build using R r paste(R.Version()[c("major", "minor")], collapse = ".") with the {fHMM} r utils::packageVersion("fHMM") package.] outlines what and how specifications are possible.

We first load the package via the familiar library() call:

library("fHMM")

The `set_controls` function

The {fHMM} philosophy is to start the modeling process by setting all data, model, and estimation specifications. This is done by defining a named list of controls and passing it to the set_controls() function. The function checks the specifications and returns an fHMM_controls object which stores all specifications and thereby provides required information for other {fHMM} functionalities.

Example specifications

For demonstration, we list example specifications using data from the Deutscher Aktienindex DAX^[The download_data() function is explained in the vignette on data management.] [@jan92]:

dax <- download_data(symbol = "^GDAXI")
head(dax)

HMMs for empirical data

The following lines of code specify a 3-state HMM with state-dependent t-distributions on the data in the file dax.csv. The dates are provided in the column called Date and the data in the column called Close. The logreturns = TRUE line transforms the index data to log-returns. The runs = 50 line sets the number of numerical optimization runs to 50.

controls <- list(
  states = 3,
  sdds   = "t",
  data   = list(file        = dax,
                date_column = "Date",
                data_column = "Close",
                logreturns  = TRUE),
  fit    = list(runs        = 50)
)
set_controls(controls)

Simulated HMM data

The following specifies a 2-state HMM with state-dependent Gamma distributions, where the expectation values for state 1 and 2 are fixed to 0.5 and 2, respectively. The model will be fitted to 500 data points (horizon = 500), that are going to be simulated from this model specification.

controls <- list(
  states  = 2,
  sdds    = "gamma(mu = 0.5|2)",
  horizon = 500
)
set_controls(controls)

Hierarchical HMMs

Specifying hierarchical HMMs is analogously, except that new parameters can be specified (for example period, see below) and some parameters now can be specified for both hierarchies.

controls <- list(
  hierarchy = TRUE,
  horizon   = c(100, 10),
  sdds      = c("t(df = 1)", "t(df = Inf)"),
  period    = "m"
)
set_controls(controls)

The help page of the set_controls() function provides an overview of all possible specifications, it can be accessed via help("set_controls", package = "fHMM").