Midterm Project for Statistical Methods in Finance

Preliminaries

Load the required packages.

#here put an r code which loads the requested packages
library("xts") #for time series

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library("nloptr") #for optimization
library(zoo)
library("dygraphs") #for plots
library(plotly) # for 3D plots

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library("magrittr") # pipes
library("webshot")
library(fBasics)

## Loading required package: timeDate

## Loading required package: timeSeries

## 
## Attaching package: 'timeSeries'

## The following object is masked from 'package:zoo':
## 
##     time<-

## 

## Rmetrics Package fBasics

## Analysing Markets and calculating Basic Statistics

## Copyright (C) 2005-2014 Rmetrics Association Zurich

## Educational Software for Financial Engineering and Computational Science

## Rmetrics is free software and comes with ABSOLUTELY NO WARRANTY.

## https://www.rmetrics.org --- Mail to: info@rmetrics.org

library('PerformanceAnalytics')

## 
## Attaching package: 'PerformanceAnalytics'

## The following objects are masked from 'package:timeDate':
## 
##     kurtosis, skewness

## The following object is masked from 'package:graphics':
## 
##     legend

Set working directory (this you have to change to your working directory)

setwd('/Users/liulingdu/Downloads')
getwd() 

## [1] "/Users/liulingdu/Downloads"

Load the APPLE data

AAPL <- read.csv(paste(getwd(),"/data/AAPL.txt",sep=''))

Convert the column with dates into Date format

D <- AAPL[,1]
D <- as.Date(toString(AAPL[1,1]),'%Y%m%d')
for (t in 2:dim(AAPL)[1])
    D <- c(D,as.Date(toString(AAPL[t,1]),'%Y%m%d'))

Convert the data frame into xts object ordered by date in vector D

AAPL_xts <- xts(AAPL[,1], order.by = D)

Print first few lines of the data

head(AAPL_xts)

##                [,1]
## 2001-01-02 20010102
## 2001-01-03 20010103
## 2001-01-04 20010104
## 2001-01-05 20010105
## 2001-01-08 20010108
## 2001-01-09 20010109

Print a summary of the data

summary(AAPL_xts)

##      Index               AAPL_xts       
##  Min.   :2001-01-02   Min.   :20010102  
##  1st Qu.:2004-11-15   1st Qu.:20041116  
##  Median :2008-10-20   Median :20081020  
##  Mean   :2008-10-21   Mean   :20083787  
##  3rd Qu.:2012-09-20   3rd Qu.:20120920  
##  Max.   :2016-08-26   Max.   :20160826

Compute percentage log-returns from close prices of APPLE stock

r_AAPL <- 100*diff(log(AAPL_xts[,1]))

Print a basic statistics for percentage log returns

basicStats(r_AAPL)

##                  r_AAPL
## nobs        3955.000000
## NAs            1.000000
## Minimum        0.000005
## Maximum        0.044320
## 1. Quartile    0.000005
## 3. Quartile    0.000005
## Mean           0.000190
## Median         0.000005
## Sum            0.750417
## SE Mean        0.000043
## LCL Mean       0.000105
## UCL Mean       0.000274
## Variance       0.000007
## Stdev          0.002715
## Skewness      16.120442
## Kurtosis     258.115017

Example of a plot using dygraph and xts object. It plots the close price

#do not use dygraph() if you knit to pdf
#uncomment if you knit to html 
#dygraph(AAPL_xts[,4]) %>%
#dyOptions(axisLineWidth = 1.5, fillGraph = FALSE, drawGrid = T, rightGap=50) %>%
#dyRangeSelector()
# for the homework and the project, if you use dygraph(), then you can knit to html, print the html file and submit the print out of the html file

Read the tickers for the data

ticker <- read.table(paste(getwd(),"/data/Tickers.txt",sep=''), header=FALSE)

Read the rest of the data and merge into one xts object

data_xts<-AAPL_xts
rets_xts<-r_AAPL

for (k in 3:dim(ticker)[1])
{
  prices <- read.csv(paste(getwd(),"/data/", ticker$V1[k],".txt",sep =''))
  dates <- NULL;
  dates <- as.Date(toString(prices[1,1]),'%Y%m%d')
  for (t in 2:dim(prices)[1])
      dates <- c(dates,as.Date(toString(prices[t,1]),'%Y%m%d'))
  prices_xts <- xts(prices[,4],order.by = dates)
  logret_xts <- 100*diff(log(prices_xts[,1]))
  rets_xts<-merge(rets_xts,logret_xts)
  data_xts<-merge(data_xts,prices_xts)
}

---

1. YOUR EMAIL↩