In msperlin/afedR: Data and Functions for Book "Analyzing Financial and Economical Data with R"

library(BatchGetSymbols)
library(tidyverse)

tickers <- GetSP500Stocks()$Tickers
first_date <- Sys.Date() - 3*365
last_date  <- Sys.Date()

df_stocks <- BatchGetSymbols(tickers = tickers, 
                             first.date = first_date, 
                             last.date = last_date)[[2]]

library(tidyverse)
library(fMarkovSwitching)

set.seed(10)

random_stocks <- sample(unique(df_stocks$ticker), 4)

temp_df <- df_stocks %>%
  dplyr::filter(ticker %in% random_stocks)

  tab_switching_model <- temp_df %>%
  na.omit() %>%
  group_by(ticker) %>%
  do(ms_model = MS_Regress_Fit(dep = .$ret.adjusted.prices,
                               indep = rep(1, nrow(.)),
                               S = c(1), 
                               k = 2))  # fitting the model)


# using temp folder for solution

plot_and_save <- function(model_in, ticker_in) {

  if (!dir.exists('figs')) dir.create('figs')

  out_file <- file.path('figs', paste0('plot_', ticker_in, '.jpg'))

  jpeg(out_file)
  plot(model_in)
  dev.off()

  return(TRUE)


}

library(purrr)

l_out <- pmap(.l = list(model_in = tab_switching_model$ms_model,
                        ticker_in = tab_switching_model$ticker), 
              .f =  plot_and_save)

# none
#my_answers <- make_random_answers(my_sol)
my_answers <- rep(NA, 5)

Question

For the same SP500 database, select 4 stocks at random and estimate a Markov regime switching model with a configuration equivalent to that presented in section \@ref(estimating-markov). Use the plot function to display the graph of the smoothed probabilities and save each figure in a folder called 'fig'.

Solution

Meta-information

extype: string exsolution: r mchoice2string(c(TRUE, FALSE, FALSE, FALSE, FALSE), single = TRUE) exname: "function 01" exshuffle: TRUE

msperlin/afedR documentation built on Sept. 11, 2022, 9:49 a.m.