Gold Rallies When Real Interest Rates Turn Negative

Periods of negative real interest rates consistently coincide with rising U.S. gold prices

A two-panel visualization exploring the inverse relationship between U.S. gold prices and real interest rates from 2003 to 2025. Shaded bands highlight periods when real rates turned negative, revealing a consistent pattern: gold rallies when holding cash loses its real value. Built with R, ggplot2, and patchwork using data from BullionVault and FRED.

Original

The original visualization comes from Gold Prices

Original visualization

Makeover

Figure 1: A two-panel line chart showing the relationship between gold prices and real interest rates from 2003 to 2025. The top panel displays gold prices indexed to 2003 equals 100, rising from 100 to approximately 850 over the period. The bottom panel shows the 10-year real interest rate fluctuating between negative 1 percent and positive 3 percent. Three periods of negative real rates are highlighted with shaded bands: 2008 during the financial crisis, 2011 to 2013 following the debt ceiling crisis, and 2020 to 2022 during the pandemic response. Each shaded period corresponds with significant gold price increases, illustrating that gold tends to rally when real interest rates fall below zero. Annotations mark the 2008 Financial Crisis, 2020 Pandemic Response, and 2024 Rally Resumes. The chart demonstrates the inverse relationship between real rates and gold prices.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
  tidyverse, ggtext, showtext, scales, glue,
  patchwork, fredr, janitor, lubridate
)
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 10,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|

gold_prices_raw <- read_csv(
  here::here("data/MakeoverMonday/2026/MM2026 WK5 Gold Price.csv")) |>
  clean_names()

### |- Real interest rates from FRED ----
# real_rates_raw <- fredr(
#   series_id = "DFII10",
#   observation_start = as.Date("2003-01-01"),
#   observation_end = Sys.Date(),
#   frequency = "m"
# ) |> 
#   select(date, value) |> 
#   rename(real_rate = value)

# Read saved FRED data (instead of fredr call)
real_rates_raw <- read_csv(
  here::here("data/MakeoverMonday/2026/MM2026_WK5_fredr_real_rates.csv")) |>
  clean_names() |>
  select(date, value) |>
  rename(real_rate = value)
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(gold_prices_raw)
glimpse(real_rates_raw)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

### |- Clean gold prices ----
gold_clean <- gold_prices_raw |>
  mutate(
    date = case_when(
      str_detect(date, " ") ~ as.Date(str_extract(date, "^[^ ]+")),
      TRUE ~ as.Date(date)
    ),
    price_kg = close_kg
  ) |>
  filter(date >= as.Date("2003-01-01")) |>
  arrange(date)

### |- Aggregate gold to monthly ----
gold_monthly <- gold_clean |>
  mutate(year_month = floor_date(date, "month")) |>
  group_by(year_month) |>
  summarise(
    price_kg = last(price_kg),
    .groups = "drop"
  ) |>
  rename(date = year_month)

### |- Merge datasets ----
combined_data <- gold_monthly |>
  left_join(real_rates_raw, by = "date") |>
  filter(!is.na(real_rate)) |>
  mutate(
    # Index gold to base 100 (first observation)
    gold_indexed = (price_kg / first(price_kg)) * 100,
    year = year(date),
    # Flag negative rate periods
    negative_rate = real_rate < 0
  )

### |- Identify negative rate periods for shading ----
# Find continuous spans where real_rate < 0
negative_periods <- combined_data |>
  mutate(
    # Create group ID for consecutive negative periods
    neg_group = cumsum(negative_rate != lag(negative_rate, default = FALSE))
  ) |>
  filter(negative_rate) |>
  group_by(neg_group) |>
  summarise(
    xmin = min(date),
    xmax = max(date),
    .groups = "drop"
  ) |>
  # Add small buffer for visual clarity
  mutate(
    xmax = xmax + days(15)
  )

### |- Key annotations ----
annotations <- tibble(
  date = as.Date(c("2008-10-01", "2020-04-01", "2024-06-01")),
  label = c(
    "2008\nFinancial\nCrisis",
    "2020\nPandemic\nResponse",
    "2024\nRally\nResumes"
  )
)
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    gold          = "#D4A03E",
    gold_dark     = "#8B6914",
    rate_line     = "#2E5A87",
    negative_zone = "#E07A5F",
    text_dark     = "#2D2D2D",
    text_mid      = "#5A5A5A",
    text_light    = "#8A8A8A",
    grid          = "#E8E8E8",
    background    = "#FAFAFA",
    zero_line     = "#4A4A4A"
  )
)

### |-  Main titles ----
title_text <- "Gold Rallies When Real Interest Rates Turn Negative"

subtitle_text <- str_glue(
  "Periods of negative real interest rates consistently coincide with rising U.S. gold prices<br>",
  "<span style='color:{colors$palette$negative_zone}'>■</span> ",
  "Shaded periods show when real interest rates fell below zero"
)

caption_text <- create_mm_caption(
  mm_year = 2026, mm_week = 05,
  source_text = str_glue(
    "BullionVault (gold prices) | FRED DFII10 (10-Year Real Interest Rate)"
  )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.3), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.8), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "right",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),

    # Axis formatting
    # axis.line.x = element_line(color = "#252525", linewidth = .1),
    # axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.y = element_markdown(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |- Top panel: Gold price (indexed) ----
p_top <- ggplot(combined_data, aes(x = date, y = gold_indexed)) +
  # Geoms
  geom_rect(
    data = negative_periods,
    aes(xmin = xmin, xmax = xmax, ymin = -Inf, ymax = Inf),
    inherit.aes = FALSE,
    fill = colors$palette$negative_zone,
    alpha = 0.2
  ) +
  geom_line(color = colors$palette$gold, linewidth = 1) +
  geom_vline(
    data = annotations,
    aes(xintercept = date),
    linetype = "dotted",
    color = colors$palette$text_light,
    linewidth = 0.5
  ) +
  geom_text(
    data = annotations,
    aes(x = date, y = max(combined_data$gold_indexed) * 0.95, label = label),
    size = 2.8,
    color = colors$palette$text_mid,
    lineheight = 0.85,
    hjust = 0.5,
    vjust = 1,
    family = "source"
  ) +
  # Scales
  scale_y_continuous(
    labels = comma_format(),
    expand = expansion(mult = c(0.02, 0.1)),
    breaks = seq(0, 800, 200)
  ) +
  scale_x_date(
    date_breaks = "5 years",
    date_labels = "%Y",
    expand = expansion(mult = c(0.02, 0.04))
  ) +
  # Labels
  labs(
    y = "Gold Price\n(indexed, 2003 = 100)"
  ) +
  # Theme
  theme(
    axis.title.x = element_blank(),
    axis.text.x = element_blank(),
    axis.title.y = element_text(
      size = rel(0.6),
      angle = 0,           # Makes it horizontal
      vjust = 1.01,        # Pushes it to the top of the axis
      hjust = 0.5,
      margin = margin(r = -50) # Adjusts distance from the axis
    ),
  )

### |- Bottom panel: Real interest rate ----
p_bottom <- ggplot(combined_data, aes(x = date, y = real_rate)) +
  # Geoms
  geom_rect(
    data = negative_periods,
    aes(xmin = xmin, xmax = xmax, ymin = -Inf, ymax = Inf),
    inherit.aes = FALSE,
    fill = colors$palette$negative_zone,
    alpha = 0.2
  ) +
  geom_hline(
    yintercept = 0, 
    color = colors$palette$zero_line, 
    linewidth = 0.6
  ) +
  geom_line(color = colors$palette$rate_line, linewidth = 0.8) +
  geom_vline(
    data = annotations,
    aes(xintercept = date),
    linetype = "dotted",
    color = colors$palette$text_light,
    linewidth = 0.5
  ) +
  # Scales
  scale_y_continuous(
    labels = label_percent(scale = 1, accuracy = 1),
    breaks = seq(-2, 3, 1),
    expand = expansion(mult = c(0.05, 0.15))
  ) +
  scale_x_date(
    date_breaks = "5 years",
    date_labels = "%Y",
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  # Labels
  labs(
    x = NULL,
    y = "Real Interest\nRate (%)"
  ) +
  # Theme
  theme(
    axis.title.y = element_text(
      size = rel(0.6),
      angle = 0,          
      vjust = 1.12,        
      hjust = 0.5,
      margin = margin(r = -50)
    ),
  )

### |- Combine panels ----
combined_plot <- p_top / p_bottom +
  plot_layout(heights = c(2, 1)) +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text, 
    theme = theme(
      plot.title = element_text(
        size = rel(1.45
                   ),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.15,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.8),
        family = 'sans',
        color = alpha(colors$subtitle, 0.88),
        lineheight = 1.5,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size = rel(0.55),
        family = fonts$subtitle,
        color = colors$caption,
        hjust = 0,
        lineheight = 1.4,
      margin = margin(t = 20, b = 5)
      )
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 18, 
  height = 8
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      janitor_2.2.0   fredr_2.1.0     patchwork_1.3.0
 [5] glue_1.8.0      scales_1.3.0    showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    lifecycle_1.0.4    compiler_4.4.0    
[17] farver_2.1.2       munsell_0.5.1      codetools_0.2-20   snakecase_0.11.1  
[21] htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3       pillar_1.9.0      
[25] camcorder_0.1.0    magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1  
[29] digest_0.6.37      stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4   
[33] fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1   cli_3.6.4         
[37] magrittr_2.0.3     utf8_1.2.4         withr_3.0.2        bit64_4.5.2       
[41] timechange_0.3.0   rmarkdown_2.29     bit_4.5.0          hms_1.1.3         
[45] evaluate_1.0.1     knitr_1.49         markdown_1.13      gridGraphics_0.5-1
[49] rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[53] renv_1.0.3         vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1 
[57] jsonlite_1.8.9     R6_2.5.1           fs_1.6.5           systemfonts_1.1.0 

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2026_05.qmd.

For the full repository, click here.

10. References

Expand for References

Primary Data (Makeover Monday):

1. Makeover Monday 2026 Week 5: BullionVault’s Gold Price Chart

2. Original Article: BullionVault’s Gold Price Chart

   • Source: BullionVault
   • Coverage: Monthly gold prices (USD per kg), 2003–2026

Enhancement Data:

3. Real Interest Rates: FRED DFII10 - 10-Year Treasury Inflation-Indexed Security
   • Source: Board of Governors of the Federal Reserve System
   • Frequency: Monthly
   • Coverage: January 2003 – Present
   • Citation: Board of Governors of the Federal Reserve System (US). (2026). Market Yield on U.S. Treasury Securities at 10-Year Constant Maturity, Quoted on an Investment Basis, Inflation-Indexed [DFII10]. Retrieved from FRED, Federal Reserve Bank of St. Louis.

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils