Country Risk Premiums

Aswath Damodaran provides country equity risk premiums (ERPs) on his website. First I downloaded his Excel spreadsheets (i.e., with Country ERPs) for the last five years and consolidated them into simple sheet.

Claude 3.5 Sonnet wrote most of this first code chunk; this is not an in-depth analysis, so I’m sure I failed to fix some of the unmatched countries manually.

library(tidyverse)
library(readxl)
library(rnaturalearth)

# Read your Excel file
your_data <- read_excel("c-erps-2cols-v2.xlsx", sheet = "2024n")

# Get the country data from rnaturalearth
world_data <- ne_countries(scale = "medium", returnclass = "sf")

# Function to find the best match
find_best_match <- function(country, reference_list) {
  distances <- adist(country, reference_list)
  best_match <- reference_list[which.min(distances)]
  return(best_match)
}

# Apply the function to your data
your_data <- your_data %>%
  mutate(matched_country = map_chr(country, ~find_best_match(.x, world_data$name)))

# View the matches
view(your_data %>% select(country, matched_country))

# Create a manual correction list for the Jersey mismatch
manual_corrections <- tibble(
  original = c("Jersey (States of)", "Yemen, Republic"),
  corrected = c("Jersey", "Yemen")
)

# Apply manual corrections
your_data <- your_data %>%
  left_join(manual_corrections, by = c("country" = "original")) %>%
  mutate(final_country = coalesce(corrected, matched_country)) %>%
  select(-corrected, -matched_country)

# Join with rnaturalearth data
joined_data <- your_data %>%
  left_join(world_data, by = c("final_country" = "name"))

# Function to find unmatched countries
find_unmatched_countries <- function(matched_data, reference_data) {
  unmatched <- setdiff(reference_data$name, matched_data$final_country)
  return(unmatched)
}

# Get the list of unmatched countries
unmatched_countries <- find_unmatched_countries(your_data, world_data)

# Print the unmatched countries
cat("Unmatched countries:\n")

Unmatched countries:

print(unmatched_countries)

 [1] "Afghanistan"               "Anguilla"                 
 [3] "Aland"                     "American Samoa"           
 [5] "Antarctica"                "Ashmore and Cartier Is."  
 [7] "Fr. S. Antarctic Lands"    "Antigua and Barb."        
 [9] "Burundi"                   "St-Barthélemy"            
[11] "Bhutan"                    "Central African Rep."     
[13] "Congo"                     "Comoros"                  
[15] "N. Cyprus"                 "Djibouti"                 
[17] "Dominica"                  "Eritrea"                  
[19] "Falkland Is."              "Faeroe Is."               
[21] "Micronesia"                "Eq. Guinea"               
[23] "Grenada"                   "Greenland"                
[25] "Guam"                      "Heard I. and McDonald Is."
[27] "Indian Ocean Ter."         "Br. Indian Ocean Ter."    
[29] "Siachen Glacier"           "Kiribati"                 
[31] "St. Kitts and Nevis"       "Kosovo"                   
[33] "Lao PDR"                   "Saint Lucia"              
[35] "Lesotho"                   "St-Martin"                
[37] "Monaco"                    "Marshall Is."             
[39] "N. Mariana Is."            "Mauritania"               
[41] "New Caledonia"             "Norfolk Island"           
[43] "Niue"                      "Nepal"                    
[45] "Nauru"                     "Pitcairn Is."             
[47] "Palau"                     "Puerto Rico"              
[49] "Dem. Rep. Korea"           "Palestine"                
[51] "Fr. Polynesia"             "W. Sahara"                
[53] "S. Sudan"                  "S. Geo. and S. Sandw. Is."
[55] "Saint Helena"              "San Marino"               
[57] "Somaliland"                "St. Pierre and Miquelon"  
[59] "São Tomé and Principe"     "Seychelles"               
[61] "Chad"                      "Turkmenistan"             
[63] "Timor-Leste"               "Tonga"                    
[65] "Vatican"                   "British Virgin Is."       
[67] "U.S. Virgin Is."           "Vanuatu"                  
[69] "Wallis and Futuna Is."     "Samoa"                    

# You can also save this to a file if needed
# write_lines(unmatched_countries, "unmatched_countries.txt")

The next two code chunks are a collaboration between Claude 3.5 Sonnet and me (David Harper, CFA, FRM).

library(viridis)
library(ggplot2)
library(sf)

joined_data <- world_data %>%
    left_join(your_data, by = c("name" = "final_country"))

# Create the choropleth map with improved legend
ggplot(data = joined_data) +
    geom_sf(aes(fill = premium)) +
    scale_fill_gradientn(
        colors = c("darkgreen", "yellowgreen", "yellow", "orange", "red"),
        values = scales::rescale(c(0, 0.01, 0.02, 0.05, max(joined_data$premium, na.rm = TRUE))),
        # breaks = c(0, 0.01, 0.02, 0.05, round(max(joined_data$premium, na.rm = TRUE), 2)),
        # labels = scales::percent_format(accuracy = 0.1),
        breaks = c(0, 0.05, 0.10, 0.15, 0.20, round(max(joined_data$premium, na.rm = TRUE), 2)),
        labels = scales::percent_format(accuracy = 1)(c(0, 0.05, 0.10, 0.15, 0.20,
                                                        round(max(joined_data$premium, na.rm = TRUE), 2))),
        name = "Country ERP",
        na.value = "lightgrey",
        guide = guide_colorbar(
            direction = "horizontal",
            barwidth = 20,
            barheight = 1,
            title.position = "top",
            title.hjust = 0.5,
            label.hjust = 1,
            ticks = TRUE,
            frame.colour = "black",
            frame.linewidth = 0.5
        )
    ) +
    theme_minimal() +
    labs(title = "World Map of Premiums",
         subtitle = "(Dark) Green is low Country ERP (~ 0) while Red is high") +
    theme(
        legend.position = "bottom",
        legend.box.margin = margin(t = 0, b = 4),
        legend.title = element_text(size = 10, face = "bold"),
        legend.text = element_text(size = 8),
        plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
        plot.subtitle = element_text(hjust = 0.5, size = 12)
    )

# Save the plot with higher resolution
ggsave("premium_map_improved_legend.png", width = 15, height = 10, units = "in", dpi = 300)

library(sf)

# Ensure joined_data is still an sf object
joined_data <- st_as_sf(joined_data)

# Create the choropleth map for ERP change
ggplot() +
    geom_sf(data = joined_data, aes(fill = change, geometry = geometry)) +
    scale_fill_gradientn(
        colors = c("darkgreen", "yellowgreen", "white", "orange", "red"),
        values = scales::rescale(c(min(joined_data$change, na.rm = TRUE), 
                                   -0.05, -0.025, 0, 0.025, 0.05, 
                                   max(joined_data$change, na.rm = TRUE))),
        breaks = c(min(joined_data$change, na.rm = TRUE), 
                   -0.05, 0, 0.05, 
                   max(joined_data$change, na.rm = TRUE)),
        labels = scales::percent_format(accuracy = 1),
        name = "ERP Change",
        na.value = "lightgrey",
        guide = guide_colorbar(
            direction = "horizontal",
            barwidth = 20,
            barheight = 1,
            title.position = "top",
            title.hjust = 0.5,
            label.hjust = 0.5,
            ticks = TRUE,
            frame.colour = "black",
            frame.linewidth = 0.5
        )
    ) +
    theme_minimal() +
    labs(title = "Change in Equity Risk Premium (ERP) Over Past 5 Years",
         subtitle = "Green: Decreased ERP (Positive), Red: Increased ERP (Negative)") +
    theme(
        legend.position = "bottom",
        legend.box.margin = margin(t = 0, b = 4),
        legend.title = element_text(size = 10, face = "bold"),
        legend.text = element_text(size = 8),
        plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
        plot.subtitle = element_text(hjust = 0.5, size = 12)
    )