I need help completing this!Project 2 Instructions: · Please read the project description before...

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I need help completing this!Project 2 Instructions:  · Please read the project description before your start. · To save a copy of your ipython notebook: click File -> Download .ipyhon · Write your code in the code cells below each Step description. You may add extra cells if needed. [1] # Run this cell as the first step to make sure we use the most updated version of statsmodels in python # If it fails to import the latest version, try click Runtime -> Restart runtime and start from here again !pip install statsmodels==0.12.0 import statsmodels Collecting statsmodels==0.12.0   Downloading https://files.pythonhosted.org/packages/ff/68/ca52fc6a114141f13dfaee340fc355e2825753f1cbe3702a13a5046e16de/statsmodels-0.12.0-cp37-cp37m-manylinux1_x86_64.whl (9.5MB)      |████████████████████████████████| 9.5MB 8.5MB/s  Requirement already satisfied: pandas>=0.21 in /usr/local/lib/python3.7/dist-packages (from statsmodels==0.12.0) (1.1.5) Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels==0.12.0) (0.5.1) Requirement already satisfied: scipy>=1.1 in /usr/local/lib/python3.7/dist-packages (from statsmodels==0.12.0) (1.4.1) Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from statsmodels==0.12.0) (1.19.5) Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->statsmodels==0.12.0) (2.8.1) Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.21->statsmodels==0.12.0) (2018.9) Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5->statsmodels==0.12.0) (1.15.0) Installing collected packages: statsmodels   Found existing installation: statsmodels 0.10.2     Uninstalling statsmodels-0.10.2:       Successfully uninstalled statsmodels-0.10.2 Successfully installed statsmodels-0.12.0 [2] import numpy as np import pandas as pd import datetime import matplotlib.pyplot as plt import statsmodels.api as sm from statsmodels.tsa.arima.model import ARIMA from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from scipy import stats   !pip install arch from arch import arch_model Collecting arch   Downloading https://files.pythonhosted.org/packages/5f/a8/a85dad77039d2884547d4fb83d54edfc13a12e31981934d6d0fb6303b791/arch-4.19-cp37-cp37m-manylinux1_x86_64.whl (807kB)      |████████████████████████████████| 808kB 10.4MB/s  Requirement already satisfied: statsmodels>=0.10 in /usr/local/lib/python3.7/dist-packages (from arch) (0.12.0) Requirement already satisfied: scipy>=1.2.3 in /usr/local/lib/python3.7/dist-packages (from arch) (1.4.1) Requirement already satisfied: cython>=0.29.14 in /usr/local/lib/python3.7/dist-packages (from arch) (0.29.22) Collecting property-cached>=1.6.4   Downloading https://files.pythonhosted.org/packages/5c/6c/94d8e520b20a2502e508e1c558f338061cf409cbee78fd6a3a5c6ae812bd/property_cached-1.6.4-py2.py3-none-any.whl Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from arch) (1.1.5) Requirement already satisfied: numpy>=1.14 in /usr/local/lib/python3.7/dist-packages (from arch) (1.19.5) Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels>=0.10->arch) (0.5.1) Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->arch) (2018.9) Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->arch) (2.8.1) Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5->statsmodels>=0.10->arch) (1.15.0) Installing collected packages: property-cached, arch Successfully installed arch-4.19 property-cached-1.6.4 Modeling volatility using US Dollar / Australian Dollar exchange rate data Step 1: import data from Github · Check to see that DataFrame usdaud a column USDAUD, which is the USD/AUD exchange rate · The sample is daily and covers the period from Jan 2, 2001 to Oct 14, 2004 Double-click (or enter) to edit [5] usdaud = pd.read_csv('https://raw.githubusercontent.com/shihanxie/Econ475/main/data/usdaud.csv') usdaud.index = pd.date_range(start='2001-01-02', periods= usdaud.shape[0], freq='D') Step 2: Compute and plot the first difference of the log(exchange rate), or Δlog(???????)×100. From now on, we will use ?? to refer Δlog(???????)×100, which is the daily percentage change in USD/AUD exchange rate. Hint: use np.log(var).diff() to compute the first difference of the log of var. [3]     Step 3: Plot the histogram and compute the descriptive statistics of ??. Conduct the proper test to see if it is normally distributed. [3]   Step 4: Compare the historgram of ?? to a normal distribution. [3]   Step 5: Compute the correlogram of squared ?? up to 12 lags. [3]   Step 6: Estimate an AR(1) model for squared ?? [3]   Step 7: Estimate an ARCH(1) model and a GARCH(1,1) model for ??=?+????∣Ω?−1∼?(0,?2?) [3]   Step 8: Estimate an AR(1)-ARCH(1) model and an AR(1)-GARCH(1,1) model for ??=?+???−1+????∣Ω?−1∼?(0,?2?) [3]   Step 9: Plot the estimated conditional variance of the best-fitting model among the ones considered [3]   USDAUD 0.5592 0.5635 0.5655 0.5712 0.566 0.5628 0.5572 0.5595 0.5557 0.5549 0.5514 0.5562 0.557 0.5544 0.5528 0.5449 0.5451 0.5432 0.5432 0.5474 0.548 0.554 0.5521 0.5508 0.549 0.5462 0.5361 0.5367 0.5381 0.5337 0.5282 0.524 0.5305 0.5224 0.5234 0.5246 0.5218 0.5213 0.5236 0.5248 0.5262 0.5292 0.5218 0.517 0.509 0.5089 0.5085 0.5096 0.5031 0.4981 0.4944 0.494 0.4984 0.4997 0.4936 0.4913 0.4963 0.499 0.4963 0.4936 0.4921 0.4881 0.4828 0.486 0.4893 0.4898 0.4958 0.4948 0.4945 0.5025 0.5082 0.5081 0.5105 0.5055 0.499 0.5085 0.5178 0.5061 0.5035 0.5045 0.5065 0.5102 0.5096 0.5177 0.5213 0.5187 0.5189 0.5186 0.5182 0.5222 0.5241 0.5207 0.518 0.5193 0.5233 0.5273 0.5251 0.529 0.5231 0.5188 0.5175 0.5193 0.5166 0.513 0.5068 0.5084 0.5062 0.5093 0.5141 0.5191 0.5243 0.5223 0.5237 0.5276 0.5261 0.5257 0.5238 0.521 0.5182 0.5178 0.5168 0.5157 0.5198 0.5178 0.511 0.51 0.5106 0.5162 0.5147 0.5083 0.5096 0.5106 0.507 0.5048 0.5074 0.508 0.5065 0.5142 0.5126 0.5055 0.5079 0.508 0.5073 0.5072 0.5064 0.5057 0.508 0.5167 0.519 0.5176 0.5166 0.5155 0.5185 0.5103 0.513 0.5174 0.5196 0.5268 0.5274 0.5354 0.5323 0.5297 0.5362 0.5338 0.5311 0.5285 0.5292 0.53 0.533 0.5277 0.5212 0.5198 0.523 0.5199 0.5135 0.5161 0.5158 0.5156 0.5021 0.4954 0.4929 0.492 0.4841 0.49 0.4939 0.4891 0.4866 0.4946 0.4923 0.4953 0.4971 0.4975 0.506 0.5009 0.5016 0.4985 0.5015 0.5135 0.5136 0.5139 0.5087 0.508 0.5073 0.5087 0.5076 0.5047 0.5017 0.5056 0.5058 0.503 0.5086 0.5077 0.5076 0.5131 0.5152 0.5147 0.5145 0.5199 0.5186 0.518 0.5203 0.5205 0.5188 0.5162 0.517 0.519 0.5217 0.5213 0.5176 0.5203 0.5183 0.5155 0.517 0.5182 0.5156 0.515 0.5148 0.5201 0.5179 0.5186 0.5176 0.5157 0.5132 0.5047 0.5075 0.5086 0.508 0.5075 0.5112 0.5117 0.5145 0.5146 0.5194 0.5183 0.5238 0.5237 0.5229 0.521 0.518 0.5189 0.5155 0.5135 0.515 0.5186 0.5198 0.5195 0.5167 0.5166 0.514 0.506 0.5072 0.508 0.5105 0.5087 0.5094 0.507 0.5104 0.5125 0.5087 0.5087 0.5167 0.5167 0.5177 0.5171 0.5169 0.5133 0.5132 0.5146 0.5158 0.5167 0.5177 0.5202 0.5207 0.5216 0.5253 0.5235 0.5212 0.5206 0.52 0.5229 0.5257 0.5245 0.5235 0.5308 0.5322 0.5326 0.5305 0.5287 0.5275 0.5336 0.5333 0.5347 0.5347 0.5314 0.5305 0.5306 0.527 0.5274 0.5303 0.5342 0.5337 0.5313 0.532 0.537 0.5387 0.539 0.539 0.5392 0.5424 0.5442 0.5427 0.538 0.5372 0.5387 0.5365 0.5381 0.5393 0.5411 0.539 0.5424 0.5445 0.546 0.5462 0.548 0.5492 0.5522 0.5524 0.5553 0.5584 0.5585 0.5558 0.5615 0.5625 0.5645 0.566 0.5714 0.5744 0.5722 0.5748 0.5711 0.5693 0.5683 0.5715 0.5688 0.5603 0.5583 0.5618 0.5655 0.5709 0.5743 0.5737 0.5683 0.5614 0.564 0.5628 0.5615 0.5623 0.5603 0.5568 0.5644 0.5688 0.5672 0.5593 0.5595 0.5599 0.5583 0.5516 0.552 0.5555 0.5517 0.5454 0.5405 0.5416 0.537 0.541 0.5445 0.5445 0.5387 0.5396 0.5292 0.528 0.5363 0.5331 0.5354 0.5392 0.5381 0.5375 0.5443 0.544 0.5422 0.5464 0.5444 0.5398 0.5433 0.5442 0.5534 0.551 0.5517 0.5495 0.5465 0.5443 0.5419 0.5467 0.5467 0.5468 0.5476 0.5518 0.5504 0.5487 0.5474 0.5485 0.551 0.5464 0.5447 0.5445 0.5445 0.5443 0.544 0.5429 0.5422 0.5442 0.5465 0.5461 0.5497 0.547 0.5485 0.5473 0.5485 0.5467 0.5487 0.55 0.5498 0.553 0.5527 0.5526 0.555 0.5545 0.5585 0.5561 0.5528 0.5548 0.5595 0.561 0.5615 0.5615 0.566 0.5658 0.561 0.5598 0.5626 0.5628 0.5635 0.5583 0.5595 0.5622 0.5638 0.559 0.5597 0.5563 0.5601 0.5591 0.5589 0.5604 0.5614 0.5603 0.563 0.5595 0.5607 0.5651 0.5646 0.566 0.566 0.565 0.565 0.5618 0.5613 0.563 0.5627 0.5608 0.5636 0.5625 0.5629 0.5655 0.5762 0.5753 0.5749 0.5765 0.582 0.5833 0.5832 0.5848 0.587 0.5909 0.5867 0.5862 0.5908 0.5922 0.5896 0.5887 0.5908 0.5872 0.586 0.5843 0.5906 0.5902 0.591 0.5903 0.5894 0.5893 0.59 0.596 0.5921 0.5912 0.5943 0.5985 0.5972 0.6057 0.6053 0.6075 0.6054 0.6075 0.6136 0.6137 0.6161 0.6142 0.6137 0.6157 0.604 0.5959 0.5941 0.5966 0.5922 0.5927 0.5913 0.5935 0.5905 0.5947 0.5968 0.5972 0.6 0.6003 0.6045 0.6045 0.5997 0.5996 0.601 0.597 0.6004 0.6032 0.6061 0.6047 0.6031 0.6056 0.6115 0.6147 0.6144 0.614 0.6212 0.6173 0.6205 0.6152 0.6192 0.62 0.6262 0.6318 0.6298 0.6343 0.6383 0.6362 0.6424 0.6448 0.6482 0.6458 0.6474 0.6425 0.6502 0.656 0.6572 0.6563 0.6585 0.6575 0.6585 0.6479 0.6477 0.6513 0.6564 0.6599 0.6657 0.6674 0.6591 0.6583 0.6565 0.6642 0.6652 0.6671 0.6676 0.6717 0.6729 0.6696 0.6675 0.6643 0.6622 0.6723 0.6651 0.6655 0.6713 0.676 0.6809 0.6823 0.6806 0.657 0.6545 0.6587 0.6607 0.66 0.6518 0.6565 0.6517 0.6454 0.6518 0.6505 0.6597 0.6648 0.6642 0.6639 0.6653 0.6525 0.6474 0.6538 0.6472 0.6466 0.6465 0.651 0.6535 0.6586 0.6568 0.6572 0.6562 0.6583 0.6562 0.6563 0.6593 0.6535 0.6506 0.6513 0.6455 0.6406 0.639 0.649 0.6402 0.6405 0.6395 0.6468 0.6502 0.658 0

Suraj · Accepted Answer

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
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   "source": [
    "import numpy as np
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    "import pandas as pd
",
    "import datetime
",
    "import matplotlib.pyplot as plt
",
    "import statsmodels.api as sm
",
    "from statsmodels.tsa.arima.model import ARIMA
",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
",
    "from scipy import stats
",
    "from arch import arch_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
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     "execution_count": 96,
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   "source": [
    "df=pd.read_csv("C:/Users/Hp/Desktop/data.csv")
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    "df.head()"
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   "execution_count": 97,
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       "            USDAUD
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       "2001-01-04  0.5655
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       "2001-01-05  0.5712
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       "2001-01-06  0.5660"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = pd.date_range(start='2001-01-02', periods= df.shape[0], freq='D')
",
    "df.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "yt=np.log(df["USDAUD"]).diff() "
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   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\Anaconda\lib\site-packages\numpy\lib\histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal
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      "  keep = (tmp_a >= first_edge)
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      "G:\Anaconda\lib\site-packages\numpy\lib\histograms.py:840: RuntimeWarning: invalid value encountered in less_equal
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      "  keep &= (tmp_a "
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    "plt.title("HIstogram of yt")
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   "execution_count": 100,
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       "count    1381.000000
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       "mean        0.000205
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       "min        -0.035291
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       "25%        -0.003773
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       "50%         0.000366
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       "75%         0.004633
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       "max         0.023646
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       "Name: USDAUD, dtype: float64"
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     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Descriptive statistics
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    "yt.describe()"
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  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png":

Project 2 Instructions: · Please read the project description before your start. · To save a copy of your ipython notebook: click File -> Download .ipyhon · Write your code in the code cells below...

Answer To: Project 2 Instructions: · Please read the project description before your start. · To save a copy of...

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	USDAUD
2001-01-02	0.5592
2001-01-03	0.5635
2001-01-04	0.5655
2001-01-05	0.5712
2001-01-06	0.5660