install.packages("ggplot2", 
   repos = c("http://rstudio.org/_packages", 
             "http://cran.rstudio.com")
   )
## 
##   There is a binary version available but the source version is later:
##         binary source needs_compilation
## ggplot2  3.3.3  3.3.5             FALSE

Outline of Coverage

Chapter 1: The Economics of Money, Banking, and Financial Markets

Key Questions:

Why Study Financial Markets?

Textbook Pages: 2-4

Bond Rates from 1986 to 2021

Spreads, Levels, and Behavior of Interest Rates

library(plotly) #Note: Install plotly and quantmod locally#
library(rmarkdown)
library(shiny)

BondRates <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/BondRates_Chapter_1.csv')
BondRates_Plot <- plot_ly(BondRates, x = ~Month, y = ~Moodys_Baa, name = 'Baa-Rated Corporate Bonds', type = 'scatter', mode = 'line')
BondRates_Plot <- BondRates_Plot %>% add_trace(y = ~LT_Gov, name = 'US Long-Term Government Bonds')
BondRates_Plot <- BondRates_Plot %>% add_trace(y = ~T_Bill_3, name = '3-Month Treasury Bills')
BondRates_Plot <- BondRates_Plot %>% layout(yaxis = list(title = "Interest Rate (% annual rate)", xaxis = list(title = "Year")))

div(BondRates_Plot, align = "center")

Market Returns from 1928 to 2020

library(plotly)
Returns <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/Securities%20Market%20Returns.csv')

Returns_Plot <- plot_ly(Returns, x = ~Year, y = ~S_P_500, name = 'S&P 500 Index', type = 'scatter', mode = 'line')
Returns_Plot <- Returns_Plot %>% add_trace(y = ~Three_month_T_Bill, name = '3-Month US T-Bill')
Returns_Plot <- Returns_Plot %>% add_trace(y = ~US_T_Bond, name = 'US Treasury Bond (10-Year)')
Returns_Plot <- Returns_Plot %>% add_trace(y = ~Baa_Corporate_Bond, name = 'Baa Rated 10-Year Corporate Bond')
Returns_Plot <- Returns_Plot %>% layout(yaxis = list(title = "Percentage Returns"), xaxis = list(title = "Year"))
Returns_Plot

Why Study Financial Institutions & Banking?

Textbook Pages: 4-7

Financial Intermediaries: G-SIBs

library(plotly)
GSIBs <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/GSIBs.csv')

GSIBs_Plot <- plot_ly(GSIBs, x = ~Year, y = ~G_SIBs, name = 'Total SBA 7(A) Loans by G-SIBs', type = 'scatter', mode = 'none', stackgroup = 'one')
GSIBs_Plot <- GSIBs_Plot %>% add_trace(y = ~Other, name = 'Total Non-G-SIB 7(A) Loan Origination')
GSIBs_Plot <- GSIBs_Plot %>% layout(title = 'Comparison of 7(A) Loans Originated by G-SIB and Non-G-SIB Companies', yaxis = list(title = "Loan Amount"), xaxis = list(title = "Year"))
GSIBs_Plot

Financial Crises

library(plotly)
sandp <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/sandp_lr.csv')

sandp_Plot <- plot_ly(sandp, x = ~Date, y = ~Price, name = 'S&P 500 Index', type = 'scatter', mode = 'line')

sandp_Plot  <- sandp_Plot %>% layout(title = 'S&P 500 Index from 1871 to 2021', yaxis = list(title = "Index Level - Inflation Adjusted"), xaxis = list(title = "Date"))

sandp_Plot

Why Study Money and Monetary Policy?

Textbook Pages: 7-11

M1 and M2

library(plotly)
library(rmarkdown)
library(shiny)
M1_M2 <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/M1_M2.csv')

M1_M2_Plot1 <- plot_ly(M1_M2, x = ~Date, y = ~M1SL, name = 'M1', type = 'scatter', mode = 'line')
M1_M2_Plot1 <- M1_M2_Plot1 %>% add_trace(y = ~M2SL, name = 'M2')
M1_M2_Plot1 <- M1_M2_Plot1 %>% layout(title = 'M1 and M2 from January 1959 to June 2021', yaxis = list(title = "Money Stock (in Billions of Dollars)", xaxis = list(title = "Date")))
M1_M2_Plot1

M1 and M2

library(plotly)
library(rmarkdown)
library(shiny)
M1_M2 <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/M1_M2.csv')
M1_M2_Plot2 <- plot_ly(M1_M2, x = ~Date, y = ~M1SL_Percent, name = 'YOY Percent Change in M1', type = 'scatter', mode = 'line')
M1_M2_Plot2 <- M1_M2_Plot2 %>% add_trace(y = ~M2SL_Percent, name = 'YOY Percent Change in M2')
M1_M2_Plot2 <- M1_M2_Plot2 %>% layout(title = 'YOY (Year-Over-Year) % Change in M1 and M2 from January 1959 to June 2021', yaxis = list(title = "YOY Percent Change", xaxis = list(title = "Date")))
M1_M2_Plot2

Fed Funds Rate and Growth

library(plotly)
library(rmarkdown)
library(shiny)
Growth_FedFunds <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/Growth%20and%20Fed%20Funds%202.csv')
Growth_FedFunds_Plot <- plot_ly(Growth_FedFunds, x = ~Year, y = ~Fed_Funds, name = 'Fed Funds Rate', type = 'scatter', mode = 'line')

Growth_FedFunds_Plot <- Growth_FedFunds_Plot %>% add_trace(y = ~Growth_RGDP, name = 'Growth in Real GDP')
Growth_FedFunds_Plot <- Growth_FedFunds_Plot %>% add_trace(y = ~L_Growth_RGDP, name = 'Lagged Growth in Real GDP')
Growth_FedFunds_Plot <- Growth_FedFunds_Plot %>% layout(yaxis = list(title = "Percentage"), xaxis = list(title = "Year"))

Growth_FedFunds_Plot

GDP Deflator

library(plotly)
library(rmarkdown)
library(shiny)
GDPDEF <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/GDPDEF.csv')
GDPDEF_Plot <- plot_ly(GDPDEF, x = ~Date, y = ~GDPDEF, name = 'GDP Price Deflator', type = 'scatter', mode = 'line')
GDPDEF_Plot <- GDPDEF_Plot %>% layout(title = 'GDP Price Deflator from January 1959 to April 2021', yaxis = list(title = "GDP Price Deflator"), xaxis = list(title = "Date"))
GDPDEF_Plot

Budget Surplus or Deficit

library(plotly)
library(rmarkdown)
library(shiny)
Budget <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/Budget%20Deficits.csv')
Budget_Plot <- plot_ly(Budget, x = ~DATE, y = ~Percent_GDP, name = 'Budget Surplus or Deficit', type = 'scatter', mode = 'line')
Budget_Plot <- Budget_Plot %>% layout(title = 'US Budget Surplus or Deficit from 1929 to 2020', yaxis = list(title = "Percent of GDP"), xaxis = list(title = "Year"))
Budget_Plot

Why Study International Finance

Textbook Pages: 12-14

US Dollar and Exchange Rates

library(plotly)
library(rmarkdown)
library(shiny)
Exchange <- read.csv('https://raw.githubusercontent.com/Prof-Smith/Money-and-Banking/main/Trade%20Weighted%20US%20Dollar.csv')
Exchange_Plot <- plot_ly(Exchange, x = ~DATE, y = ~Dollar, name = 'Trade Weighted U.S. Dollar Index', type = 'scatter', mode = 'line')
Exchange_Plot <- Exchange_Plot %>% layout(title = 'Trade Weighted U.S. Dollar Index from 2016 to 2021', yaxis = list(title = "Value of Index"), xaxis = list(title = "Date"))
Exchange_Plot

Outline of Cases for the Course

Case 1: Financial Intermediaries

The interactions between banks and financial intermediaries, the central bank, and consumers in terms of the creation of money/credit are fascinating. For this module’s reflection paper, consider whose role it is to ‘create’ money/credit.

Werner (2016) indicates that over the last century, there have been three dominant theories about banking’s role in the economy. The first is that they act as intermediaries and collect deposits, then lend them out to consumers. The second is that banks on their own are not able to create money, but do so collectively through the fractional reserve system and the multiplier effect. The third suggests that banks do not act collectively, but each individual bank creates credit and money when they make loans. Based on an empirical test and case studies, Werner (2016) indicates that the first two theories are rejected and found empirical evidence supporting the third (similar to Werner 2014). Mumtaz et al., 2020, p. 331