unemp_spx_model_3_2: Unemployment rate and S&P 500 hierarchical HMM

In fHMM: Fitting Hidden Markov Models to Financial Data

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")

Format

An object of class fHMM_model.

Details

The model was estimated via:

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)

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