University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Homework 1
Exercise 1
2
Use the data presented in class (returns of IBM and TEXACO, R1 = 0.010, 1 = 0.0061, R2 =
2 = 0.0046): Find the smalles
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Homework 6
Exercise 1:
Suppose a stock has annual expected return and standard deviation = 0.20 and = 0.25. The current
price of the stock is
#
#=> ( Multi group model ) <=#
#
#=> obtain a lot of tickers via included data set <=#
library(stockPortfolio)
#Stock and index tickers:
ticker <- c("C", "KEY", "WFC", "JPM", "SO", "DUK", "D", "HE", "EIX", "LUV", "AMR", "AMGN", "GILD", "CELG", "GENZ", "B
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Multigroup model
Short sales allowed with riskless lending and borrowing- Theory
From: Simple Rules for Optimal Portfolio Selection: The Multi
#CONSTANT CORRELATION MODEL - EXAMPLE:
#Read the data:
data1 <- read.table("http:/www.stat.ucla.edu/~nchristo/statistics_c183_c283/stocks_10.txt",
header=TRUE)
#Compute the average correlation:
rho <- (sum(cor(data1[1:10])-10)/90
#Initialize the vectors
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Constructing the optimal portfolios - Constant correlation model
Short sales not allowed
The calculation of optimal portfolios is simplied by
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Constructing the optimal portfolios - Constant correlation model
Short sales allowed
The calculation of optimal portfolios is simplied by usin
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Constructing the optimal portfolios - Constant correlation model
Calculation steps
a. Step 1: Compute the historical mean return, standard dev
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Single index model - Short sales not allowed
Risk free asset exists
Kuhn-Tucker conditions
If we assume short sales then we can simply maximiz
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Constructing the optimal portfolios - Single index model
R commands
In this example we use data from 5 stocks plus the S &P 500 index.
Read th
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Homework 1
Exercise 1
2
Use the data presented in class (returns of IBM and TEXACO, R1 = 0.010, 1 = 0.0061, R2 =
2 = 0.0046): Find the smalles
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Homework 3
Exercise 1:
Access the data in R as follows:
> a <- read.table("http:/www.stat.ucla.edu/~nchristo/datac183c283/
statc183c283_10stoc
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Homework 2
Access the following data:
http:/www.stat.ucla.edu/~nchristo/statistics_c183_c283/statc183c283_5stocks.txt.
In R you can access the
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Homework 5
Exercise 1:
You want to nd the value of a European call option for the following data: S0 = $50, E =
1
$60, u = 1.2, d = u , r = 0.
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Homework 4
Exercise 1:
An investor sells a European call on a share for $4. The stock price is $47 and the exercise price is
$50. When does th
University of California, Los Angeles
Department of Statistics
Statistics C183/C283
Instructor: Nicolas Christou
Dynamic Delta hedging
(From Options Futures and Other Derivatives by John Hull, Prentice Hall 6th Edition, 2006)
A nancial institution has sol
#Part 1.
problems
Use
below
R
to
solve
the
distribution
#Question1.1) 0.2007
# 2) 0.1673
#Question2.1) 0.68,
# 2) 168 bigger than 166.4, she is top 10% of all female
students.
# 3) 170.3 cm
#Part 2.
Use
R
regression
problems
#Obama_vs_mccain
data
to
below
Understanding the
Factors
Creating a Movie
Data Structure
Javier Magallanes
JungEun Lim
Zhaofeng Liang
S
Data Exploration
Data Source
Original Data:
Additional Data:
Budget
Movie title
Genre
Release date
Rating
Opening weekend gross
Total gross
Derived Da
Part 1
1.
1) 0.20
2) 0.167
2.
1) 0.68
2) Since 168 > 166.4, so she belongs to the top 10% of all female students.
3) 170.2687 cm
Part 2
1. The percentage of people who vote for Obama is the response variable; unemployment rate,
average income and populati