# ASSIGNMENT 8: EWMA Model (__ POINTS) # Please provide below your name, institutional email, and student number. # My name is: # My email is: # M...

# ASSIGNMENT 8: EWMA Model (__ POINTS)
# Please provide below your name, institutional email, and student number.
# My name is:
# My email is:
# My student number is:

#####1. Install the Packages if necessary####
if(!require("tidyverse")) install.packages("tidyverse")
if(!require("tidyquant")) install.packages("tidyquant")
if(!require("lubridate")) install.packages("lubridate")
if(!require("PerformanceAnalytics")) install.packages("PerformanceAnalytics")
if(!require("treasuryTR")) install.packages("treasuryTR")
if(!require("openxlsx")) install.packages("openxlsx")
if(!require("xts")) install.packages("xts")
if(!require("rugarch")) install.packages("rugarch")
#####2. Load the Packages####
library(tidyverse)
library(tidyquant)
library(lubridate)
library(PerformanceAnalytics)
library(treasuryTR)
library(openxlsx)
library(xts)
library(rugarch)
#####3. Data Wrangling####
######3.1.Asset Prices (Stocks) ########
Pa_daily <- c("NFLX","AMZN","AAPL") %>%
tq_get(get = "stock.prices",
from = "2017-01-01",
to = "2022-06-30")
Pa_daily_wide <- Pa_daily %>% dplyr::select(symbol,date,adjusted) %>%
pivot_wider(.,names_from = symbol, values_from = adjusted)
Pa_daily_wide_ts <- read.zoo(Pa_daily_wide, format = "%Y-%m-%d")
######3.2.Asset Returns (Stocks) ########
Ra_daily <- c("NFLX","AMZN","AAPL") %>%
tq_get(get = "stock.prices",
from = "2017-01-01",
to = "2022-06-30") %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "daily",
col_rename = "Ra")
Ra_daily_wide <- pivot_wider(Ra_daily,names_from = symbol, values_from = Ra)
Ra_daily_wide_ts <- read.zoo(Ra_daily_wide, format = "%Y-%m-%d")
######3.3.Asset Returns (Portfolio of Stocks) ########
Ra_daily_weights <- c(NFLX=1/3,AMZN=1/3,AAPL=1/3)
Ra_daily_port_ts <- Return.portfolio(Ra_daily_wide_ts, geometric = FALSE,
weights = Ra_daily_weights)
Ra_daily_port <- xts_to_tibble(Ra_daily_port_ts) %>%
rename(Portfolio=portfolio.returns)
######3.4.Baseline Returns (Portfolio) ########
Rb_daily <- "^GSPC" %>%
tq_get(get = "stock.prices",
from = "2017-01-01",
to = "2022-06-30") %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "daily",
col_rename = "SP500")
Rb_daily_ts <- read.zoo(Rb_daily, format = "%Y-%m-%d")
######3.5.Combine datasets ########
Daily_Ret <- full_join(Ra_daily_wide,Ra_daily_port,by="date") %>%
full_join(.,Rb_daily,by="date")
Daily_Ret_ts <- read.zoo(Daily_Ret, format = "%Y-%m-%d")
#####4. Analysis####
######4.1. Descriptive Statistics ####
Table_summary_prc <- table.Stats(Pa_daily_wide_ts) %>%
tibble::rownames_to_column(.,"Statistic")
Table_summary_ret <- table.Stats(Daily_Ret_ts) %>%
tibble::rownames_to_column(.,"Statistic")
######4.2. EWMA Model Specification ####
ewma_spec <- ugarchspec(mean.model = list(armaOrder = c(0,0),
include.mean=FALSE),
variance.model = list(model = "sGARCH"),
fixed.pars = list(omega=0))
######4.3. EWMA Model Fitting ####
ewma_fit_NFLX <- ugarchfit(data = Daily_Ret_ts[,1], spec = ewma_spec)
show(ewma_fit_NFLX)
ewma_fit_AMZN <- ugarchfit(data = Daily_Ret_ts[,2], spec = ewma_spec)
show(ewma_fit_AMZN)
ewma_fit_AAPL <- ugarchfit(data = Daily_Ret_ts[,3], spec = ewma_spec)
show(ewma_fit_AAPL)
ewma_fit_PORT <- ugarchfit(data = Daily_Ret_ts[,4], spec = ewma_spec)
show(ewma_fit_PORT)
ewma_fit_SP500 <- ugarchfit(data = Daily_Re