Data 606 Final Proposal
Tyler Graham
2025-05-04
Libraries & Packages
#install.packages("tidyquant")
#install.packages("tidyverse")
#install.packages("recipes")
library(tidyquant)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## ── Attaching core tidyquant packages ─────────────────────── tidyquant 1.0.11 ──
## ✔ PerformanceAnalytics 2.0.8 ✔ TTR 0.24.4
## ✔ quantmod 0.4.27 ✔ xts 0.14.1
## ── Conflicts ────────────────────────────────────────── tidyquant_conflicts() ──
## ✖ zoo::as.Date() masks base::as.Date()
## ✖ zoo::as.Date.numeric() masks base::as.Date.numeric()
## ✖ PerformanceAnalytics::legend() masks graphics::legend()
## ✖ quantmod::summary() masks base::summary()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::first() masks xts::first()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::last() masks xts::last()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsData Preparation
# Download Yahoo Finance data: Bitcoin, Gold, VIX
symbols <- c("BTC-USD", "GC=F", "^VIX")
market_data <- tq_get(symbols,
from = "2020-01-01",
to = "2023-12-31") %>%
select(symbol, date, adjusted) %>%
pivot_wider(names_from = symbol, values_from = adjusted) %>%
rename(BTC = `BTC-USD`, Gold = `GC=F`, VIX = `^VIX`)
# Download DXY (U.S. Dollar Index) from FRED
getSymbols("DTWEXBGS", src = "FRED")## [1] "DTWEXBGS"dxy <- DTWEXBGS %>%
as_tibble(rownames = "date") %>%
rename(DXY = DTWEXBGS) %>%
mutate(date = as.Date(date))
# Ensure date formats match
market_data <- market_data %>%
mutate(date = as.Date(date))
# Merge DXY with Yahoo data
market_data <- market_data %>%
left_join(dxy, by = "date") %>%
drop_na(BTC, Gold, VIX, DXY)
# Save the full dataset for later reuse
saveRDS(market_data, file = "market_data.rds")
# Preview
head(market_data)## # A tibble: 6 × 5
## date BTC Gold VIX DXY
## <date> <dbl> <dbl> <dbl> <dbl>
## 1 2020-01-02 6985. 1524. 12.5 115.
## 2 2020-01-03 7345. 1549. 14.0 115.
## 3 2020-01-06 7769. 1566. 13.9 115.
## 4 2020-01-07 8164. 1572. 13.8 115.
## 5 2020-01-08 8080. 1557. 13.4 115.
## 6 2020-01-09 7879. 1552. 12.5 115.Research question
Does Bitcoin behave like a non-traditional store of value by demonstrating a relationship with traditional financial indicators such as gold prices, the U.S. Dollar Index (DXY), and the Volatility Index (VIX)? Specifically, is there evidence of negative correlation with the dollar index and positive correlation with gold, similar to other stores of value?
Cases
Each case is a daily observation from financial markets, covering approximately 1,000 days from January 2020 to December 2023. Each row represents a day and contains the adjusted closing price for each asset.
Data collection
The data is gathered from public financial APIs using the tidyquant R package, which interfaces with Yahoo Finance and FRED. The data includes historical prices for Bitcoin (BTC), gold, the VIX (volatility index), and the U.S. Dollar Index (DXY).
Type of study
This is an observational study using historical financial market data with no experimental intervention.
Data Source
The data is not self-collected. All financial series are sourced from: - Bitcoin: Yahoo Finance - BTC-USD - Gold: Yahoo Finance - GC=F - VIX: Yahoo Finance - ^VIX - U.S. Dollar Index (to be added separately from FRED: https://fred.stlouisfed.org/series/DTWEXBGS)
Describe your variables?
All variables are quantitative (numeric): -
BTC – Daily closing price of Bitcoin (dependent variable) -
Gold – Daily closing price of gold - VIX –
Daily value of the volatility index - date – Date (used for
merging, not for modeling)
The dependent variable is Bitcoin price, and the others are potential explanatory variables in the regression.
Relevant summary statistics
summary(market_data)## date BTC Gold VIX
## Min. :2020-01-02 Min. : 4971 Min. :1477 Min. :12.07
## 1st Qu.:2021-01-01 1st Qu.:17003 1st Qu.:1757 1st Qu.:17.23
## Median :2022-01-01 Median :27377 Median :1828 Median :21.39
## Mean :2021-12-30 Mean :28890 Mean :1828 Mean :22.89
## 3rd Qu.:2022-12-29 3rd Qu.:40025 3rd Qu.:1919 3rd Qu.:26.37
## Max. :2023-12-29 Max. :67567 Max. :2082 Max. :82.69
## DXY
## Min. :110.5
## 1st Qu.:114.5
## Median :118.2
## Mean :118.0
## 3rd Qu.:121.1
## Max. :128.5# Correlation matrix
cor(market_data %>% select(BTC, Gold, VIX, DXY), use = "complete.obs")## BTC Gold VIX DXY
## BTC 1.0000000 0.2171814 -0.4207926 -0.5094088
## Gold 0.2171814 1.0000000 -0.3934708 -0.1000124
## VIX -0.4207926 -0.3934708 1.0000000 0.2739848
## DXY -0.5094088 -0.1000124 0.2739848 1.0000000# Scatterplot matrix
pairs(~ BTC + Gold + VIX + DXY, data = market_data)
# Linear regression model
model <- lm(BTC ~ Gold + VIX + DXY, data = market_data)
summary(model)##
## Call:
## lm(formula = BTC ~ Gold + VIX + DXY, data = market_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29102.7 -6778.2 -611.7 9062.0 29559.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 204917.874 13117.716 15.621 <2e-16 ***
## Gold 8.585 3.686 2.329 0.02 *
## VIX -493.619 51.200 -9.641 <2e-16 ***
## DXY -1528.709 95.571 -15.995 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12210 on 990 degrees of freedom
## Multiple R-squared: 0.3486, Adjusted R-squared: 0.3466
## F-statistic: 176.6 on 3 and 990 DF, p-value: < 2.2e-16