Python代写|BENG0091 Coursework 1


Trading in the stock market is literally risky business. Given that risk is unavoidable, the key question
that arises is how a risk-averse investor can construct a portfolio that yields the desired returns at
the lowest possible risk. Efficient frontier theory, pioneered by Nobel Laureate Harry Markowitz,
provides an answer to this question, and allows us to identify investment portfolios that strike the
best balance between risk and return. While this theory makes certain assumptions that are
probably oversimplifications (notably, assuming that past performance is an indicator of future
trends and that asset returns are normally distributed), the theory is extremely important in
understanding the effect of diversification in investing, and has been extended in attempts to better
capture the real market behaviour (e.g. post-modern portfolio theory, Black–Litterman model, etc.).

In this coursework, we will explore the main concepts of modern portfolio theory and we will try to
come up with efficient portfolios of stocks of three fictitious companies:

Nano-Automata Corps (abbreviated as NAC)
Quantum Cryptographers Inc. (abbreviated as QCI)
Inter-Galactic Telecommunications (abbreviated as IGT)

Data analysis of the stock prices of these companies in the past 10 years has shown that the daily
closing prices of NAC adhere to the following stochastic process:

where X n , n =1,2,… are i.i.d. that follow the Laplace distribution, X n ∼Laplace (μ,b) , n denotes the
day, and nmax the maximum number of days we want to predict the stock price for. The Laplace
distribution is continuous with two parameters, b and µ, and has the following probability density:

From the data analysis we know that X n ∼Laplace (0.00051 0.0162 , )

The prices of the stocks of QCI and IGT can be expressed as follows:

where Yn ∼Laplace (0. ,0. 00033 0088) i.i.d. and Zn ∼Laplace (0.00072,0.0181) i.i.d. for n = 1,2,3,…

Things to do

1. We will first do some preliminary work that will enable us to simulate stochastic realisations of
the prices of the stocks of interest.

(a) Derive an explicit mathematical formula for the cumulative distribution function of the
Laplace distribution. [2]

(b) Describe a simple approach for the generation of random deviates from the Laplace
distribution. [4]

(c) Write a program that generates 100000 samples from the Laplace distribution with µ = 0.5
and b = 0.3 (use the approach you developed in the previous question; do not use built-in
functions for this). To verify that your method works correctly, show a histogram of these
samples and a line-graph of the probability density function (both in the same plot). [6]

2. We are now able to perform the stochastic simulations of the prices of the three stocks of
interest. We will assume that each year contains 260 days of trading (roughly 52 weeks of 5
working days each), and we will simulate stock prices for a 5-year window, i.e. nmax =1300 days.

(a) Write a program that simulates the stock price sequencies S S S n n n NAC QCI GT , , , 0,1,2,…, I n = nmax .

To do this, first generate three random streams, one for X n , n n =1,2,…, max , one for Yn and
similarly, one for Zn , using the method you developed in Question 1. Thus, you are allowed
to use only a uniform random number generator available for the programming language of
your choice (MATLAB, Python etc.), initialised with a seed value of your choice.

Subsequently, use these sequences in the application of the equations that give the stock
price per day, e.g. for QCI, you will evaluate the expression S X Y S nQCI = + + ⋅ 1 0.5 1.7 ( n n n )⋅ QCI −1
recursively. Present graphs of the stock prices per day in the same plot. [12]

(b) Repeat the procedures of Question 2(a) with two more different random seeds and
produce plots of the stock prices ped day. You will present two separate plots, one for each
random seed. Each of these plots will contain three line-graphs, one for each stock (NAC,
QCI, and IGT). Comment on the observed behaviour in the three “scenarios” that you
simulated and plotted in Questions 2(a) and 2(b). For instance, how does the magnitude of
the fluctuations (volatility) compare with the overall “average” behaviour of a stock (drift)
in the three different scenarios? Which stock(s) would you pick for your portfolio if you
were an investor? [10]