# 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.

    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 DAX2 (Janßen and Rudolph 1992):

    download_data(symbol = "^GDAXI", file = "dax.csv")
    #> Download successful.
    #> * symbol: ^GDAXI
    #> * from: 1987-12-30
    #> * to: 2023-02-13
    #> * path: C:\Users\Lennart\AppData\Local\Temp\Rtmp42gnFG\Rbuild186c5f42744c\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.

    ?set_controls

set_controls | R Documentation

## Set and validate controls

### Arguments

`controls` | A `list` of controls, see below. Either none, all, or selected parameters can be specified. Unspecified parameters are set to default values (see the values in brackets below). If `hierarchy = TRUE`, parameters marked with a `(*)` must be a `vector` of length 2, where the first entry corresponds to the coarse-scale and the second entry to the fine-scale layer. `hierarchy` (`FALSE`): A `logical`, set to `TRUE` for an hierarchical HMM. `states` `(*)` (`2`): An `integer`, the number of states of the underlying Markov chain. `sdds` `(*)` (`“t(df = Inf)”`): A `character`, specifying the state-dependent distribution. One of `“t”` (the t-distribution), or `“gamma”` (the gamma distribution), or `“lnorm”` (the log-normal distribution). You can fix the parameters (mean `mu`, standard deviation `sigma`, degrees of freedom `df`) of these distributions via, e.g., `“t(df = Inf)”` or `“gamma(mu = 0, sigma = 1)”`. To fix different values of a parameter for different states, separate by "|", e.g. `“t(mu = -1|1)”`. `horizon` `(*)` (`100`): A `numeric`, specifying the length of the time horizon. The first entry of `horizon` is ignored if `data` is specified. `period` (`“m”`): Only relevant if `hierarchy = TRUE` and `horizon[2] = NA`. In this case, a `character` which specifies a flexible, periodic fine-scale time horizon and can be one of `“w”` for a week, `“m”` for a month, `“q”` for a quarter, `“y”` for a year. `data` (`NA`): A `list` of controls specifying the data. If `data = NA`, data gets simulated (default). Otherwise: `file` `(*)`: Either: A `data.frame`, which must have a column named `date_column` (with dates) and `data_column` (with financial data). If `hierarchy = TRUE`, this `data.frame` is used for both the coarse- and the fine-scale layer. To have different data sets for theses layers, `file` can be a `list` of two `data.frame`. A `character`, the path to a .csv-file with financial data, which must have a column named `date_column` (with dates) and `data_column` (with financial data). `date_column` `(*)` (`“Date”`): A `character`, the name of the column in `file` with dates. Can be `NA` in which case consecutive integers are used as time points. `data_column` `(*)` (`“Close”`): A `character`, the name of the column in `file` with financial data. `from` (`NA`): A `character` of the format `“YYYY-MM-DD”`, setting a lower data limit. No lower limit if `from = NA`. Ignored if `controls\(data\)date_column` is `NA`. `to` (`NA`): A `character` of the format `“YYYY-MM-DD”`, setting an upper data limit. No upper limit if `from = NA`. Ignored if `controls\(data\)date_column` is `NA`. `logreturns` `(*)` (`FALSE`): A `logical`, if `TRUE` the data is transformed to log-returns. `merge` (`function(x) mean(x)`): Only relevant if `hierarchy = TRUE`. In this case, a `function` with one argument `x`, which merges a numeric vector of fine-scale data `x` into one coarse-scale observation. For example, `merge = function(x) mean(x)` defines the mean of the fine-scale data as the coarse-scale observation, `merge = function(x) mean(abs(x))` for the mean of the absolute values, `merge = function(x) sum(abs(x))` for the sum of the absolute values, `merge = function(x) (tail(x,1)-head(x,1))/head(x,1)` for the relative change of the first to the last fine-scale observation. `fit`: A `list` of controls specifying the model fitting: `runs` (`100`): An `integer`, setting the number of randomly initialized optimization runs from which the best one is selected as the final model. `origin` (`FALSE`): A `logical`, if `TRUE` the optimization is initialized at the true parameter values. Only for simulated data. If `origin = TRUE`, this sets `run = 1` and `accept = 1:5`. `accept` (`1:3`): An `integer` (vector), specifying which optimization runs are accepted based on the output code of `nlm`. `gradtol` (`1e-6`): A positive `numeric` value, passed on to `nlm`. `iterlim` (`200`): A positive `integer`, passed on to `nlm`. `print.level` (`0`): One of `0`, `1`, and `2` to control the verbosity of the optimization, passed on to `nlm`. `steptol` (`1e-6`): A positive `numeric` value, passed on to `nlm`.
`x` | An object of class `fHMM_controls`.
`…` | Currently not used.

## References

Janßen, B., and B. Rudolph. 1992. “Der Deutsche Aktienindex DAX.” Knapp Verlag.

1. This vignette was build using R .4 with the `{fHMM}` 1.1.0 package.↩︎

2. The `download_data()` function is explained in the vignette on data management.↩︎