Controls

The {fHMM} package allows for multiple hidden Markov model specifications, including different data transformations, state-dependent distributions, and a hierarchical model structure. This vignette¹ outlines what and how specifications are possible.

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 DAX² (Janßen and Rudolph 1992):

download_data(symbol = "^GDAXI", file = "dax.csv")
#> Download successful.
#> * symbol: ^GDAXI
#> * from: 1987-12-30
#> * to: 2023-10-12
#> * path: C:\Users\loelschlaeger\AppData\Local\Temp\Rtmp0UK1WS\Rbuild370061d934e\fHMM\vignettes\dax.csv

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.csv",
                date_column = "Date",
                data_column = "Close",
                logreturns  = TRUE),
  fit    = list(runs        = 50)
)
set_controls(controls)
#> fHMM controls:
#> * hierarchy: FALSE 
#> * data type: empirical 
#> * number of states: 3 
#> * sdds: t() 
#> * number of runs: 50

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)
#> fHMM controls:
#> * hierarchy: FALSE 
#> * data type: simulated 
#> * number of states: 2 
#> * sdds: gamma(mu = 0.5|2) 
#> * number of runs: 100

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)
#> fHMM controls:
#> * hierarchy: TRUE 
#> * data type: simulated 
#> * number of states: 2 2 
#> * sdds: t(df = 1) t(df = Inf) 
#> * number of runs: 100

The help page of the set_controls() function provides an overview of all possible specifications.

This vignette was build using R 4.2.2 with the {fHMM} 1.1.1 package.↩︎
The download_data() function is explained in the vignette on data management.↩︎