R/data_and_models.R
In loelschlaeger/fHMM: Fitting Hidden Markov Models to Financial Data
#' Standard & Poor’s 500 (S&P 500) index data
#'
#' @description
#' Standard & Poor’s 500 (S&P 500) index data from 1928 to 2022 from Yahoo
#' Finance.
#'
#' @details
#' The data was obtained via:
#' \preformatted{
#' spx <- download_data(
#' symbol = "^GSPC", # S&P 500 identifier on Yahoo Finance
#' from = "1928-01-01", # first observation
#' to = "2022-12-31" # last observation
#' )
#' }
#'
#' @format A \code{data.frame} with 23864 rows and the following 7 columns:
#' \itemize{
#' \item \code{Date}: The date.
#' \item \code{Open}: Opening price.
#' \item \code{High}: Highest price.
#' \item \code{Low}: Lowest price.
#' \item \code{Close}: Close price adjusted for splits.
#' \item \code{Adj.Close}: Close price adjusted for dividends and splits.
#' \item \code{Volume}: Trade volume.
#' }
#'
#' @keywords data
"spx"
#' Deutscher Aktienindex (DAX) index data
#'
#' @description
#' Deutscher Aktienindex (DAX) index data from 1988 to 2022 from Yahoo Finance.
#'
#' @details
#' The data was obtained via:
#' \preformatted{
#' dax <- download_data(
#' symbol = "^GDAXI", # DAX identifier on Yahoo Finance
#' from = "1988-01-01", # first observation
#' to = "2022-12-31" # last observation
#' )
#' }
#'
#' @format A \code{data.frame} with 9012 rows and the following 7 columns:
#' \itemize{
#' \item \code{Date}: The date.
#' \item \code{Open}: Opening price.
#' \item \code{High}: Highest price.
#' \item \code{Low}: Lowest price.
#' \item \code{Close}: Close price adjusted for splits.
#' \item \code{Adj.Close}: Close price adjusted for dividends and splits.
#' \item \code{Volume}: Trade volume.
#' }
#'
#' @keywords data
"dax"
#' Volkswagen AG (VW) stock data
#'
#' @description
#' Volkswagen AG (VW) stock data from 1998 to 2022 from Yahoo Finance.
#'
#' @details
#' The data was obtained via:
#' \preformatted{
#' vw <- download_data(
#' symbol = "VOW3.DE", # Volkswagen AG identifier on Yahoo Finance
#' from = "1988-07-22", # first observation
#' to = "2022-12-31" # last observation
#' )
#' }
#'
#' @format A \code{data.frame} with 6260 rows and the following 7 columns:
#' \itemize{
#' \item \code{Date}: The date.
#' \item \code{Open}: Opening price.
#' \item \code{High}: Highest price.
#' \item \code{Low}: Lowest price.
#' \item \code{Close}: Close price adjusted for splits.
#' \item \code{Adj.Close}: Close price adjusted for dividends and splits.
#' \item \code{Volume}: Trade volume.
#' }
#'
#' @keywords data
"vw"
#' Unemployment rate data USA
#'
#' @description
#' The monthly unemployment rate in the USA from 1955 to 2022 on a daily
#' observation basis.
#'
#' @format
#' A \code{data.frame} with 24806 rows and the following 3 columns:
#' \itemize{
#' \item \code{date}: The date.
#' \item \code{rate}: The unemployment rate.
#' \item \code{rate_diff}: The difference rate to previous month.
#' }
#'
#' @source
#' OECD (2023), Unemployment rate (indicator).
#' doi: 10.1787/52570002-en (Accessed on 18 January 2023)
#' <https://www.oecd.org/en/data/indicators/unemployment-rate.html>
#'
#' @keywords data
"unemp"
#' DAX 2-state HMM with normal distributions
#'
#' @description
#' A pre-computed HMM on closing prices of the DAX from 2000 to 2022
#' with two hidden states and normal state-dependent distributions for
#' demonstration purpose.
#'
#' @usage data("dax_model_2n")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- set_controls(
#' states = 2,
#' sdds = "normal",
#' data = list(
#' file = dax,
#' date_column = "Date",
#' data_column = "Close",
#' logreturns = TRUE,
#' from = "2000-01-03",
#' to = "2022-12-31"
#' ),
#' fit = list("runs" = 10, "gradtol" = 1e-6, "steptol" = 1e-6)
#' )
#' dax_data <- prepare_data(controls)
#' dax_model_2n <- fit_model(dax_data, seed = 1)
#' dax_model_2n <- decode_states(dax_model_2n)
#' dax_model_2n <- compute_residuals(dax_model_2n)
#' summary(dax_model_2n)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"dax_model_2n"
#' DAX 3-state HMM with t-distributions
#'
#' @description
#' A pre-computed HMM on closing prices of the DAX from 2000 to 2022
#' with three hidden states and state-dependent t-distributions for
#' demonstration purpose.
#'
#' @usage data("dax_model_3t")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- set_controls(
#' states = 3,
#' sdds = "t",
#' data = list(
#' file = dax,
#' date_column = "Date",
#' data_column = "Close",
#' logreturns = TRUE,
#' from = "2000-01-03",
#' to = "2022-12-31"
#' ),
#' fit = list(
#' runs = 100,
#' iterlim = 300,
#' gradtol = 1e-6,
#' steptol = 1e-6
#' )
#' )
#' dax_data <- prepare_data(controls)
#' dax_model_3t <- fit_model(dax_data, seed = 1, ncluster = 10)
#' dax_model_3t <- decode_states(dax_model_3t)
#' dax_model_3t <- compute_residuals(dax_model_3t)
#' summary(dax_model_3t)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"dax_model_3t"
#' DAX/VW hierarchical HMM with t-distributions
#'
#' @description
#' A pre-computed HHMM with monthly averaged closing prices of the
#' DAX from 2010 to 2022 on the coarse scale, Volkswagen AG stock data on the
#' fine scale, two hidden fine-scale and coarse-scale states, respectively, and
#' state-dependent t-distributions for demonstration purpose.
#'
#' @usage data("dax_vw_model")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- set_controls(
#' hierarchy = TRUE,
#' states = c(2, 2),
#' sdds = c("t", "t"),
#' period = "m",
#' data = list(
#' file = list(dax, vw),
#' from = "2010-01-01",
#' to = "2022-12-31",
#' logreturns = c(TRUE, TRUE)
#' ),
#' fit = list(
#' runs = 200,
#' iterlim = 300,
#' gradtol = 1e-6,
#' steptol = 1e-6
#' )
#' )
#' dax_vw_data <- prepare_data(controls)
#' dax_vw_model <- fit_model(dax_vw_data, seed = 1, ncluster = 10)
#' dax_vw_model <- decode_states(dax_vw_model)
#' dax_vw_model <- compute_residuals(dax_vw_model)
#' summary(dax_vw_model)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"dax_vw_model"
#' Unemployment rate and S&P 500 hierarchical HMM
#'
#' @description
#' A pre-computed HHMM with monthly unemployment rate in the US on the coarse
#' scale using 3 states and S&P 500 index data on the fine scale using 2 states
#' from 1970 to 2020 for demonstration purpose.
#'
#' @usage data("unemp_spx_model_3_2")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- list(
#' hierarchy = TRUE, states = c(3, 2),
#' sdds = c("t", "t"), period = "m",
#' data = list(
#' file = list(unemp, spx),
#' data_column = c("rate_diff", "Close"),
#' date_column = c("date", "Date"),
#' from = "1970-01-01", to = "2020-01-01",
#' logreturns = c(FALSE, TRUE)
#' ),
#' fit = list(runs = 50, iterlim = 1000, gradtol = 1e-6, steptol = 1e-6)
#' )
#' controls <- set_controls(controls)
#' unemp_spx_data <- prepare_data(controls)
#' unemp_spx_model_3_2 <- fit_model(unemp_spx_data, seed = 1, ncluster = 10)
#' unemp_spx_model_3_2 <- decode_states(unemp_spx_model_3_2)
#' unemp_spx_model_3_2 <- compute_residuals(unemp_spx_model_3_2)
#' summary(unemp_spx_model_3_2)
#' state_order <- matrix(c(3, 2, 1, 2, 2, 2, 1, 1, 1), 3, 3)
#' unemp_spx_model_3_2 <- reorder_states(unemp_spx_model_3_2, state_order)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"unemp_spx_model_3_2"
#' Simulated 2-state HMM with gamma distributions
#'
#' @description
#' A pre-computed 2-state HMM with state-dependent gamma distributions with
#' means fixed to \code{0.5} and \code{2} on \code{500} simulated observations.
#'
#' @usage data("sim_model_2gamma")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- set_controls(
#' states = 2,
#' sdds = "gamma(mu = 1|2)",
#' horizon = 200,
#' runs = 10
#' )
#' pars <- fHMM_parameters(
#' controls = controls,
#' Gamma = matrix(c(0.9, 0.2, 0.1, 0.8), nrow = 2),
#' sigma = c(0.5, 1),
#' seed = 1
#' )
#' data_sim <- prepare_data(controls, true_parameters = pars, seed = 1)
#' sim_model_2gamma <- fit_model(data_sim, seed = 1)
#' sim_model_2gamma <- decode_states(sim_model_2gamma)
#' sim_model_2gamma <- compute_residuals(sim_model_2gamma)
#' summary(sim_model_2gamma)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"sim_model_2gamma"
loelschlaeger/fHMM documentation built on June 15, 2025, 9:30 a.m.
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#' Standard & Poor’s 500 (S&P 500) index data
#'
#' @description
#' Standard & Poor’s 500 (S&P 500) index data from 1928 to 2022 from Yahoo
#' Finance.
#'
#' @details
#' The data was obtained via:
#' \preformatted{
#' spx <- download_data(
#' symbol = "^GSPC", # S&P 500 identifier on Yahoo Finance
#' from = "1928-01-01", # first observation
#' to = "2022-12-31" # last observation
#' )
#' }
#'
#' @format A \code{data.frame} with 23864 rows and the following 7 columns:
#' \itemize{
#' \item \code{Date}: The date.
#' \item \code{Open}: Opening price.
#' \item \code{High}: Highest price.
#' \item \code{Low}: Lowest price.
#' \item \code{Close}: Close price adjusted for splits.
#' \item \code{Adj.Close}: Close price adjusted for dividends and splits.
#' \item \code{Volume}: Trade volume.
#' }
#'
#' @keywords data
"spx"
#' Deutscher Aktienindex (DAX) index data
#'
#' @description
#' Deutscher Aktienindex (DAX) index data from 1988 to 2022 from Yahoo Finance.
#'
#' @details
#' The data was obtained via:
#' \preformatted{
#' dax <- download_data(
#' symbol = "^GDAXI", # DAX identifier on Yahoo Finance
#' from = "1988-01-01", # first observation
#' to = "2022-12-31" # last observation
#' )
#' }
#'
#' @format A \code{data.frame} with 9012 rows and the following 7 columns:
#' \itemize{
#' \item \code{Date}: The date.
#' \item \code{Open}: Opening price.
#' \item \code{High}: Highest price.
#' \item \code{Low}: Lowest price.
#' \item \code{Close}: Close price adjusted for splits.
#' \item \code{Adj.Close}: Close price adjusted for dividends and splits.
#' \item \code{Volume}: Trade volume.
#' }
#'
#' @keywords data
"dax"
#' Volkswagen AG (VW) stock data
#'
#' @description
#' Volkswagen AG (VW) stock data from 1998 to 2022 from Yahoo Finance.
#'
#' @details
#' The data was obtained via:
#' \preformatted{
#' vw <- download_data(
#' symbol = "VOW3.DE", # Volkswagen AG identifier on Yahoo Finance
#' from = "1988-07-22", # first observation
#' to = "2022-12-31" # last observation
#' )
#' }
#'
#' @format A \code{data.frame} with 6260 rows and the following 7 columns:
#' \itemize{
#' \item \code{Date}: The date.
#' \item \code{Open}: Opening price.
#' \item \code{High}: Highest price.
#' \item \code{Low}: Lowest price.
#' \item \code{Close}: Close price adjusted for splits.
#' \item \code{Adj.Close}: Close price adjusted for dividends and splits.
#' \item \code{Volume}: Trade volume.
#' }
#'
#' @keywords data
"vw"
#' Unemployment rate data USA
#'
#' @description
#' The monthly unemployment rate in the USA from 1955 to 2022 on a daily
#' observation basis.
#'
#' @format
#' A \code{data.frame} with 24806 rows and the following 3 columns:
#' \itemize{
#' \item \code{date}: The date.
#' \item \code{rate}: The unemployment rate.
#' \item \code{rate_diff}: The difference rate to previous month.
#' }
#'
#' @source
#' OECD (2023), Unemployment rate (indicator).
#' doi: 10.1787/52570002-en (Accessed on 18 January 2023)
#' <https://www.oecd.org/en/data/indicators/unemployment-rate.html>
#'
#' @keywords data
"unemp"
#' DAX 2-state HMM with normal distributions
#'
#' @description
#' A pre-computed HMM on closing prices of the DAX from 2000 to 2022
#' with two hidden states and normal state-dependent distributions for
#' demonstration purpose.
#'
#' @usage data("dax_model_2n")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- set_controls(
#' states = 2,
#' sdds = "normal",
#' data = list(
#' file = dax,
#' date_column = "Date",
#' data_column = "Close",
#' logreturns = TRUE,
#' from = "2000-01-03",
#' to = "2022-12-31"
#' ),
#' fit = list("runs" = 10, "gradtol" = 1e-6, "steptol" = 1e-6)
#' )
#' dax_data <- prepare_data(controls)
#' dax_model_2n <- fit_model(dax_data, seed = 1)
#' dax_model_2n <- decode_states(dax_model_2n)
#' dax_model_2n <- compute_residuals(dax_model_2n)
#' summary(dax_model_2n)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"dax_model_2n"
#' DAX 3-state HMM with t-distributions
#'
#' @description
#' A pre-computed HMM on closing prices of the DAX from 2000 to 2022
#' with three hidden states and state-dependent t-distributions for
#' demonstration purpose.
#'
#' @usage data("dax_model_3t")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- set_controls(
#' states = 3,
#' sdds = "t",
#' data = list(
#' file = dax,
#' date_column = "Date",
#' data_column = "Close",
#' logreturns = TRUE,
#' from = "2000-01-03",
#' to = "2022-12-31"
#' ),
#' fit = list(
#' runs = 100,
#' iterlim = 300,
#' gradtol = 1e-6,
#' steptol = 1e-6
#' )
#' )
#' dax_data <- prepare_data(controls)
#' dax_model_3t <- fit_model(dax_data, seed = 1, ncluster = 10)
#' dax_model_3t <- decode_states(dax_model_3t)
#' dax_model_3t <- compute_residuals(dax_model_3t)
#' summary(dax_model_3t)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"dax_model_3t"
#' DAX/VW hierarchical HMM with t-distributions
#'
#' @description
#' A pre-computed HHMM with monthly averaged closing prices of the
#' DAX from 2010 to 2022 on the coarse scale, Volkswagen AG stock data on the
#' fine scale, two hidden fine-scale and coarse-scale states, respectively, and
#' state-dependent t-distributions for demonstration purpose.
#'
#' @usage data("dax_vw_model")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- set_controls(
#' hierarchy = TRUE,
#' states = c(2, 2),
#' sdds = c("t", "t"),
#' period = "m",
#' data = list(
#' file = list(dax, vw),
#' from = "2010-01-01",
#' to = "2022-12-31",
#' logreturns = c(TRUE, TRUE)
#' ),
#' fit = list(
#' runs = 200,
#' iterlim = 300,
#' gradtol = 1e-6,
#' steptol = 1e-6
#' )
#' )
#' dax_vw_data <- prepare_data(controls)
#' dax_vw_model <- fit_model(dax_vw_data, seed = 1, ncluster = 10)
#' dax_vw_model <- decode_states(dax_vw_model)
#' dax_vw_model <- compute_residuals(dax_vw_model)
#' summary(dax_vw_model)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"dax_vw_model"
#' Unemployment rate and S&P 500 hierarchical HMM
#'
#' @description
#' A pre-computed HHMM with monthly unemployment rate in the US on the coarse
#' scale using 3 states and S&P 500 index data on the fine scale using 2 states
#' from 1970 to 2020 for demonstration purpose.
#'
#' @usage data("unemp_spx_model_3_2")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- list(
#' hierarchy = TRUE, states = c(3, 2),
#' sdds = c("t", "t"), period = "m",
#' data = list(
#' file = list(unemp, spx),
#' data_column = c("rate_diff", "Close"),
#' date_column = c("date", "Date"),
#' from = "1970-01-01", to = "2020-01-01",
#' logreturns = c(FALSE, TRUE)
#' ),
#' fit = list(runs = 50, iterlim = 1000, gradtol = 1e-6, steptol = 1e-6)
#' )
#' controls <- set_controls(controls)
#' unemp_spx_data <- prepare_data(controls)
#' unemp_spx_model_3_2 <- fit_model(unemp_spx_data, seed = 1, ncluster = 10)
#' unemp_spx_model_3_2 <- decode_states(unemp_spx_model_3_2)
#' unemp_spx_model_3_2 <- compute_residuals(unemp_spx_model_3_2)
#' summary(unemp_spx_model_3_2)
#' state_order <- matrix(c(3, 2, 1, 2, 2, 2, 1, 1, 1), 3, 3)
#' unemp_spx_model_3_2 <- reorder_states(unemp_spx_model_3_2, state_order)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"unemp_spx_model_3_2"
#' Simulated 2-state HMM with gamma distributions
#'
#' @description
#' A pre-computed 2-state HMM with state-dependent gamma distributions with
#' means fixed to \code{0.5} and \code{2} on \code{500} simulated observations.
#'
#' @usage data("sim_model_2gamma")
#'
#' @details
#' The model was estimated via:
#' \preformatted{
#' controls <- set_controls(
#' states = 2,
#' sdds = "gamma(mu = 1|2)",
#' horizon = 200,
#' runs = 10
#' )
#' pars <- fHMM_parameters(
#' controls = controls,
#' Gamma = matrix(c(0.9, 0.2, 0.1, 0.8), nrow = 2),
#' sigma = c(0.5, 1),
#' seed = 1
#' )
#' data_sim <- prepare_data(controls, true_parameters = pars, seed = 1)
#' sim_model_2gamma <- fit_model(data_sim, seed = 1)
#' sim_model_2gamma <- decode_states(sim_model_2gamma)
#' sim_model_2gamma <- compute_residuals(sim_model_2gamma)
#' summary(sim_model_2gamma)
#' }
#'
#' @format An object of class \code{\link{fHMM_model}}.
#'
#' @keywords model
"sim_model_2gamma"
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