VIX

The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the stock market’s expectation of volatility implied by S&P 500 index options. It is calculated and disseminated on a real-time basis by the Chicago Board Options Exchange (CBOE), and is commonly referred to as the fear index or the fear gauge. - https://en.wikipedia.org/wiki/VIX

# vix <- tq_get(c("^VIX"),
#                get  = "stock.prices",
#                from = "2009-01-01",
#                to   = Sys.Date()) %>% 
#   mutate(symbol = "VIX")
# 
# vix %>% saveRDS("data/vix.RDS")

vix <- readRDS("data/vix.RDS")

years <- tibble(year = seq(2010, 2019), 
                from = seq.Date(date("2010-01-01"), date("2019-01-01"), by = "years"),
                to   = seq.Date(date("2010-12-31"), date("2019-12-31"), by = "years"))

vix %>% 
  filter(year(date) >= 2010) %>%
  ggplot(aes(x = date, y = adjusted, color = symbol)) +
  geom_rect(data = years, inherit.aes = F, aes(xmin = from, xmax = to, ymin = -Inf, ymax = Inf, fill = factor(year)), alpha = 0.2) +
  geom_line() +
  geom_smooth(color = "black") +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y", expand = c(0, 0)) +
  scale_y_continuous(breaks = seq(0,100, by = 5)) +
  scale_fill_brewer(type = "qual", palette = "Set3", guide = F) +
  labs(title = "CBOE Volatility Index (VIX)", subtitle = "since Jan 2010",
       x = "", y = "value", caption = "Data: Yahoo Finance, graph: mgei.github.io") +
  theme_tq() +
  theme(legend.position = "none")

Yield curves

In finance, the yield curve is a curve showing several yields or interest rates across different contract lengths (2 month, 2 year, 20 year, etc. …) for a similar debt contract. - https://en.wikipedia.org/wiki/Yield_curve

# usd_yields_raw <- Quandl("USTREASURY/YIELD")
# 
# usd_yields_raw %>% saveRDS("data/usd_yields_raw.RDS")

usd_yields_raw <- readRDS("data/usd_yields_raw.RDS")

usd_yields <- usd_yields_raw %>% 
  as_tibble() %>%
  gather(term, Value, -Date) %>% 
  mutate(term = case_when(term == "1 MO" ~ 1/12,
                          term == "2 MO" ~ 1/6,
                          term == "3 MO" ~ 1/4,
                          term == "6 MO" ~ 1/2,
                          T ~ str_remove(term, " YR") %>% as.numeric()))

usd_yields %>% 
  filter(year(Date) %in% c(2019)) %>%
  group_by(month(Date)) %>% 
  filter(Date == max(Date)) %>% 
  ggplot(aes(x = term, y = Value/100, alpha = Date, group = Date, linetype = if_else(Date < max(Date), "previous", "current"))) +
  geom_rect(inherit.aes = F, aes(xmin = -Inf, xmax = Inf, ymin = 0.0225, ymax = 0.025),  fill = "lightblue") +
  geom_line(aes(x = if_else(term %in% c(1/12, 10), term, NA_real_), y = if_else(term  %in% c(1/12, 10), Value/100, NA_real_)), color = "red") +
  geom_line(color = "black") +
  labs(title = "Yield Curve USD", subtitle = "Year 2019, red connecting line indicating the slope (1mt-10yr), in lightblue the target range",
       x = "Term in years", y = "yield",
       caption = "Data: Quandl/USTREASURY, graph: mgei.github.io",
       linetype = "Observation") +
  scale_x_continuous(breaks = pull(unique(usd_yields["term"]))) +
  scale_y_continuous(breaks = seq(-0.01, 1, by = 0.001), labels = scales::percent) +
  theme_bw()

chf_yiels <- read_csv2("snb-data-rendoblim-en-selection-20190603_1430.csv", skip = 3) %>% 
  mutate(Date = ymd(paste0(Date, "-01")), 
         term = D0 %>% str_remove("J") %>% as.numeric(),
         Value = as.numeric(Value))

chf_yiels %>% 
  filter(year(Date) %in% c(2019)) %>% 
  ggplot(aes(x = term, y = Value/100, alpha = Date, group = Date, linetype = if_else(Date < max(Date), "previous", "current"))) +
  geom_hline(yintercept = 0, color = "red", alpha = 0.5) +
  geom_hline(yintercept = -0.0075, color = "blue", alpha = 0.5) +
  geom_rect(inherit.aes = F, aes(xmin = -Inf, xmax = Inf, ymin = -0.0125, ymax = -0.0025),  fill = "lightblue") +
  geom_line(aes(x = if_else(term %in% c(1, 10), term, NA_real_), y = if_else(term  %in% c(1, 10), Value/100, NA_real_)), color = "red") +
  geom_line() +
  labs(title = "Yield Curve CHF", subtitle = "Year 2019",
       x = "Term in years", y = "yield",
       caption = "Data: SNB, graph: mgei.github.io",
       linetype = "Observation") +
  scale_x_continuous(breaks = pull(unique(chf_yiels["term"]))) +
  scale_y_continuous(breaks = seq(-0.015, 1, by = 0.001), labels = scales::percent) +
  theme_bw()

Why care about the yield curve?

The slope of the yield curve is one of the most powerful predictors of future economic growth, inflation, and recessions. - https://en.wikipedia.org/wiki/Yield_curve#Relationship_to_the_business_cycle

# SPX <- tq_get("^GSPC", from = "1990-01-01")
# 
# SPX %>% saveRDS("data/SPX.RDS")

SPX <- readRDS("data/SPX.RDS")

yield_curve <- usd_yields_raw %>% 
  as_tibble() %>% 
  mutate(adjusted = `10 YR` - `3 MO`, 
         symbol = "yield 10yr-3mt") %>% 
  select(date = Date, adjusted, symbol)

dropindicators <- yield_curve %>% 
  filter(adjusted < 0) %>% 
  group_by(year(date)) %>% 
  filter(date == min(date)) %>% 
  ungroup() %>% 
  filter(week(date) > 1)
  
bind_rows(SPX %>% mutate(symbol = "SPX"),
          yield_curve) %>% 
  ggplot(aes(x = date, y = adjusted, color = symbol)) +
  geom_hline(yintercept = 0, color = "red") +
  geom_line() +
  geom_vline(data = bind_rows(dropindicators, dropindicators %>% mutate(symbol = "SPX")), aes(xintercept = date), alpha = 0.6) +
  facet_wrap(~symbol, ncol = 1, scales = "free_y") +
  labs(title = "S&P500 vs. the Yield Curve", subtitle = "Since 1990",
       x = "", y = "value",
       caption = "Data: Yahoo Finance, Quandl/USTREASURY, graph: mgei.github.io",
       linetype = "Observation") +
  scale_x_date(date_breaks = "1 year", date_labels = "%y", expand = c(0, 100)) +
  scale_y_continuous() +
  # scale_size_continuous(breaks = c(0.25, 0.5)) +
  theme_tq() +
  theme(legend.position = "none")

SPX %>% 
  ggplot(aes(x = date, y = adjusted)) +
  geom_line(color = "red") +
  geom_vline(data = bind_rows(dropindicators, dropindicators %>% mutate(symbol = "SPX")), aes(xintercept = date), alpha = 0.6) +
  labs(title = "S&P500", subtitle = "Log-scale, since 1990",
       x = "", y = "log value",
       caption = "Data: Yahoo Finance, Quandl/USTREASURY, graph: mgei.github.io",
       linetype = "Observation") +
  scale_x_date(date_breaks = "1 year", date_labels = "%y", expand = c(0, 100)) +
  scale_y_log10(breaks = round((1:100)^3, -1)) +
  # scale_size_continuous(breaks = c(0.25, 0.5)) +
  theme_tq() +
  theme(legend.position = "none")

To keep in mind is that interest rates were higher before the financial crisis.

usd_yields %>% 
  filter(term %in% c(3/12, 10)) %>% 
  ggplot(aes(x = Date, y = Value, color = factor(term), group = factor(term))) +
  geom_line() +
  geom_vline(data = bind_rows(dropindicators, dropindicators %>% mutate(symbol = "SPX")), aes(xintercept = date), alpha = 0.6) +
  labs(title = "US treasury yields", subtitle = "Since 1990",
       x = "", y = "value",
       color = "term",
       caption = "Data: Yahoo Finance, Quandl/USTREASURY, graph: mgei.github.io",
       linetype = "Observation") +
  scale_x_date(date_breaks = "1 year", date_labels = "%y", expand = c(0, 100)) +
  scale_color_discrete(labels = c("3mt", "10yr")) +
  # scale_y_log10() +
  # scale_size_continuous(breaks = c(0.25, 0.5)) +
  theme_tq()