2018年10月31日，老文章

Exercise 1

Recall that n!n! is read as “nn factorial” and defined as n!=n×(n?1)×?×2×1n!=n×(n?1)×?×2×1

There are functions to compute this in various modules, but let’s write our own version as an exercise

In particular, write a function factorial such that factorial(n) returns n!n! for any positive integer n

# -*- coding: utf-8 -*-
"""
Created on Tue Oct 16 23:01:45 2018

@author: Wengsway

"""

def factorial(n):
      if n < 2:
            return 1
      else:
            return n*factorial(n-1)
n = int(input('please input any positive integer:'))
print(factorial(n))

Exercise 2

The binomial random variable Y～Bin(n,p)Y～Bin(n,p) represents the number of successes in nn binary trials, where each trial succeeds with probability pp

Without any import besides from numpy.random import uniform, write a function binomial_rv such that binomial_rv(n, p) generates one draw of YY

Hint: If UU is uniform on (0,1)(0,1) and p∈(0,1)p∈(0,1), then the expression U < p evaluates to True with probability p

# -*- coding: utf-8 -*-
"""
Created on Wed Oct 17 18:26:42 2018

@author: Wengsway

"""

import numpy as np

def factorial(n):
      if n < 2:
            return 1
      else:
            return n*factorial(n-1)
u = np.random.normal(0,1)
N = int(input("Please input the N value："))
def binomial_rv(n,p):
      y = p**n * (1-p)**(N-n) * factorial(N)/(factorial(n) * factorial(N-n))
      return y
n = int(input("Please input the n value:"))
p = float(input("Please input the p value:"))
print(binomial_rv(n,p))

Exercise 3

Compute an approximation to ππ using Monte Carlo. Use no imports besides

import numpy as np

Your hints are as follows:

• If is a bivariate uniform random variable on the unit square , then the probability that lies in a subset of is equal to the area of
• If U1,…,Un are iid copies of , then, as gets large, the fraction that fall in converges to the probability of landing in
• For a circle, area = pi * radius^2

# -*- coding: utf-8 -*-
"""
Created on Wed Oct 17 10:23:25 2018

@author: Wengsway

"""

import numpy as np

frequency = 0
numbers = int(input("Please input the number of times:"))
for i in range(1, numbers):
    x, y = np.random.uniform(0,1), np.random.uniform(0,1)
    area = np.sqrt(x**2 + y**2)
    if area <= 1.0:
        frequency = frequency + 1
pi = 4 * (frequency/numbers)
print(pi)

Exercise 4

Write a program that prints one realization of the following random device:

• Flip an unbiased coin 10 times
• If 3 consecutive heads occur one or more times within this sequence, pay one dollar
• If not, pay nothing

Use no import besides from numpy.random import uniform

# -*- coding: utf-8 -*-
"""
Created on Wed Oct 17 12:25:01 2018

@author: Wengsway

"""

from numpy.random import randint

x = randint(0,2,10).tolist()
j = 0
for i in range(len(x)-2):
      if x[i] == 1 and x[i+1] == 1 and x[i+2] == 1:
            j = j + 1
if j >= 1:
      print("You pay one dollar!")
else:
      print("You pay nothing!")

Exercise 5

Your next task is to simulate and plot the correlated time series

The sequence of shocks {} is assumed to be iid and standard normal

In your solution, restrict your import statements to

import numpy as np
import matplotlib.pyplot as plt

Set T=200 and α=0.9

# -*- coding: utf-8 -*-
"""
Created on Wed Oct 17 10:50:23 2018

@author: Wengsway

"""

import numpy as np
import matplotlib.pyplot as plt

def timeseries(α,T):
      x = [0]
      ε = np.random.randn(T)
      for t in range(T):
            x.append(α*x[t-1]+ε[t])
      return x
T = int(input('Please input the T:'))
α = float(input('Please input the α：'))
plt.figure(figsize=(10,5))
plt.plot(timeseries(α,T+1),color = 'Hotpink',label = 'x')
plt.legend()

Exercise 6

To do the next exercise, you will need to know how to produce a plot legend

The following example should be sufficient to convey the idea

import numpy as np
import matplotlib.pyplot as plt

x = [np.random.randn() for i in range(100)]
plt.plot(x, label="white noise")
plt.legend()
plt.show()

Now, starting with your solution to exercise 5, plot three simulated time series, one for each of the cases α=0, α=0.8 and α=0.98

In particular, you should produce (modulo randomness) a figure that looks as follows

not found

(The figure nicely illustrates how time series with the same one-step-ahead conditional volatilities, as these three processes have, can have very different unconditional volatilities.)

Use a for loop to step through the αα values

Important hints:

• If you call the plot() function multiple times before calling show(), all of the lines you produce will end up on the same figure
  • And if you omit the argument 'b-' to the plot function, Matplotlib will automatically select different colors for each line
• The expression 'foo' + str(42) evaluates to 'foo42'

# -*- coding: utf-8 -*-
"""
Created on Wed Oct 17 12:14:49 2018

@author: Wengsway

"""

import numpy as np
import matplotlib.pyplot as plt

def timeseries(α,T):
      x = [0]
      ε = np.random.randn(T)
      for t in range(T):
            x.append(α*x[t-1]+ε[t])
      return x
T = int(input('Please input the T:'))
for i in range(3):
      α = float(input('Please input the α：'))    
      plt.figure(figsize=(10,5))
      plt.plot(timeseries(α,T+1),color = 'r',label = 'x')
      plt.legend()
      plt.show()