60.1K

Verified Solution

Link Copied!

Based on this, and after I gathered all the information, please help me solve the fourth and fifth parts

4. Present the regression results, evaluate the model performance in terms of t-statistics, R-squares, Adjusted R-squared and Interpret the estimated coefficients. The model is as follow: SR=0 + 1 + 2 +

5. Conclusion.

.

Please write in bullet points, not paragraphs

Stocks

Oil stocks

Covid cases

all data has been collected along with scatter plot and regression

The country you chose is Australia

Please write in bullet points, not paragraphs

We are in the midst of one of the largest pandemics in history, COVID-19; originating in China it has migrated across the globe. According to the World Health Organization, there have been 231,703,120 confirmed cases of COVID-19, including 4,746,620 deaths, as of 27 September, 2021. In 1918-almost a century ago-the Spanish flu pandemic killed about 40 million people worldwide. As investigated by Barro et al. (2020), the number of deaths caused by the Spanish flu is expected to be higher than those caused by COVID-19, but the impact of the COVID-19 pandemic on the economy is expected to be far greater than that of the Spanish flu. As many countries adopted strict quarantine policies after the COVID-19 outbreak, economic activities have been greatly restricted. The long-term consequences of the COVID-19 pandemic may be high unemployment and business closures. Some industries, such as tourism and aviation, face various difficulties and challenges. While the COVID-19 pandemic has triggered a sharp rise in uncertainty, the shock to the stock market and oil market is unprecedented. Thus, investors and markets are facing a high degree of uncertainty regarding both physical and financial impacts of the virus. The recent Coronavirus (COVID-19) pandemic has resulted in unusual volatility in the financial markets. The aim of this presentation is to understand the impact of COVID-19 on the financial economy, which deals with the stock market, interest rates, exchange rate, oil price and real estate prices. - You have to prepare a presentation with the following guidelines: 1. Introduction: introduce the topic; discuss the background of the topic (you also need to provide some evidence with facts, graphs, tables and figures). 2. Theoretical background: explain what is the theoretical relationship between COVID-19 and the stock markets (based on literature reviews). 3. Describe the source(s) of the data you have collected and explain in small table how each variable is measured which means the indicators (proxies), the chosen period and individual country. 4. Present the regression results, evaluate the model performance in terms of t-statistics, R-squares, Adjusted R-squared and Interpret the estimated coefficients. The model is as follow: SR-B. + B.CC + B,0P, + Et 5. Conclusion. 6. The reference: you need to follow APA style for your references. Scatterplot of Adj Close versus Date -4000 3500 3000 2500 2000 y = 1.0567x 43524 0.3907 = R 1500 1000 500 0 26/09/2020 15/11/2020 04/01/2021 23/02/2021 14/04/2021 03/06/2021 23/07/2021 11/09/2021 31/10/2021 SUMMARY OUTPUT Regression Statistics Multiple R 0.625038089 R Square 0.390672613 Adjusted R Square 0.388000125 Standard Error 127.9991698 Observations 230 ANOVA df Significance F 2.49271E-26 Regression Residual Total 1 228 229 SS 2395032.56 3735503.542 6130536.102 MS F 2395032.56 146.1830828 16383.78746 Intercept Date Coefficients -43524,4424 1.056672866 Standard Error 3871.999439 0.087396089 t Stat P-value - 11.2408184 1.29274E-23 12.09061962 2.49271E-26 Lower 95% -51153.91985 0.884465586 Upper 95% -35894.9648 1.228880147 Lower 95.0% -51153.9199 0.884465586 Upper 95.0% -35894.9648 1.228880147 Scatterplot of Adj Close versus Date 60 50 40 30 y = 0.0246x - 1049.1 0.3078 = R? 20 10 0 26/09/2020 15/11/2020 04/01/2021 23/02/2021 14/04/2021 03/06/2021 23/07/2021 11/09/2021 31/10/2021 SUMMARY OUTPUT Regression Statistics Multiple R 0.554809154 R Square 0.307813197 Adjusted R Square 0.304816717 Standard Error 3.61443407 Observations 233 ANOVA df F 102.724941 Regression Residual Total SS 1342.012358 3017.814873 4359.827232 MS 1342.012358 13.06413365 Significance F. 3.29016E-20 1 231 232 Intercept Date Coefficients -1049.07619 0.024642281 Standard Error t Stat P-value 107.7204448 -9.7388773 5.33982E-19 0.002431325 10.13533132 3.29016E-20 Lower 95% Upper 95% -1261.316349 -836.836039 0.019851875 0.029432688 Lower 95.0% -1261.31635 0.019851875 Upper 95.0% -836.836039 0.029432688 Scatterplot of No. of Cases versus Date 120,000 100,000 4E06 - 82 100 = 80,000 03859 = R2 60,000 40,000 20,000 26 -2 15 -20 -1 3 -21 14--21 3[]--1 23 -1 11 - 21 31--21 SUMMARY OUTPUT Regression Statistics Multiple R 0.621240796 R Square 0.385940127 Adjusted R Square 0.384096104 Standard Error 12341.10946 Observations 335 ANOVA df Significance F 3.82101E-37 Regression Residual Total 1 333 334 SS MS F 31875856241 31875856241 209.2923965 50716893237 152302982.7 82592749478 Intercept Date Coefficients 4433590.986 100.868666 Standard Error t Stat P-value 308897.0225 -14.35297418 1.05574E-36 6.972355971 14.4669415 3.82101E-37 Lower 95% Upper 95% Lower 95.0% Upper 95.0% -5041226.472 -3825955.5 -5041226.472 -3825955.5 87.1532508 114.5840811 87.1532508 114.5840811 We are in the midst of one of the largest pandemics in history, COVID-19; originating in China it has migrated across the globe. According to the World Health Organization, there have been 231,703,120 confirmed cases of COVID-19, including 4,746,620 deaths, as of 27 September, 2021. In 1918-almost a century ago-the Spanish flu pandemic killed about 40 million people worldwide. As investigated by Barro et al. (2020), the number of deaths caused by the Spanish flu is expected to be higher than those caused by COVID-19, but the impact of the COVID-19 pandemic on the economy is expected to be far greater than that of the Spanish flu. As many countries adopted strict quarantine policies after the COVID-19 outbreak, economic activities have been greatly restricted. The long-term consequences of the COVID-19 pandemic may be high unemployment and business closures. Some industries, such as tourism and aviation, face various difficulties and challenges. While the COVID-19 pandemic has triggered a sharp rise in uncertainty, the shock to the stock market and oil market is unprecedented. Thus, investors and markets are facing a high degree of uncertainty regarding both physical and financial impacts of the virus. The recent Coronavirus (COVID-19) pandemic has resulted in unusual volatility in the financial markets. The aim of this presentation is to understand the impact of COVID-19 on the financial economy, which deals with the stock market, interest rates, exchange rate, oil price and real estate prices. - You have to prepare a presentation with the following guidelines: 1. Introduction: introduce the topic; discuss the background of the topic (you also need to provide some evidence with facts, graphs, tables and figures). 2. Theoretical background: explain what is the theoretical relationship between COVID-19 and the stock markets (based on literature reviews). 3. Describe the source(s) of the data you have collected and explain in small table how each variable is measured which means the indicators (proxies), the chosen period and individual country. 4. Present the regression results, evaluate the model performance in terms of t-statistics, R-squares, Adjusted R-squared and Interpret the estimated coefficients. The model is as follow: SR-B. + B.CC + B,0P, + Et 5. Conclusion. 6. The reference: you need to follow APA style for your references. Scatterplot of Adj Close versus Date -4000 3500 3000 2500 2000 y = 1.0567x 43524 0.3907 = R 1500 1000 500 0 26/09/2020 15/11/2020 04/01/2021 23/02/2021 14/04/2021 03/06/2021 23/07/2021 11/09/2021 31/10/2021 SUMMARY OUTPUT Regression Statistics Multiple R 0.625038089 R Square 0.390672613 Adjusted R Square 0.388000125 Standard Error 127.9991698 Observations 230 ANOVA df Significance F 2.49271E-26 Regression Residual Total 1 228 229 SS 2395032.56 3735503.542 6130536.102 MS F 2395032.56 146.1830828 16383.78746 Intercept Date Coefficients -43524,4424 1.056672866 Standard Error 3871.999439 0.087396089 t Stat P-value - 11.2408184 1.29274E-23 12.09061962 2.49271E-26 Lower 95% -51153.91985 0.884465586 Upper 95% -35894.9648 1.228880147 Lower 95.0% -51153.9199 0.884465586 Upper 95.0% -35894.9648 1.228880147 Scatterplot of Adj Close versus Date 60 50 40 30 y = 0.0246x - 1049.1 0.3078 = R? 20 10 0 26/09/2020 15/11/2020 04/01/2021 23/02/2021 14/04/2021 03/06/2021 23/07/2021 11/09/2021 31/10/2021 SUMMARY OUTPUT Regression Statistics Multiple R 0.554809154 R Square 0.307813197 Adjusted R Square 0.304816717 Standard Error 3.61443407 Observations 233 ANOVA df F 102.724941 Regression Residual Total SS 1342.012358 3017.814873 4359.827232 MS 1342.012358 13.06413365 Significance F. 3.29016E-20 1 231 232 Intercept Date Coefficients -1049.07619 0.024642281 Standard Error t Stat P-value 107.7204448 -9.7388773 5.33982E-19 0.002431325 10.13533132 3.29016E-20 Lower 95% Upper 95% -1261.316349 -836.836039 0.019851875 0.029432688 Lower 95.0% -1261.31635 0.019851875 Upper 95.0% -836.836039 0.029432688 Scatterplot of No. of Cases versus Date 120,000 100,000 4E06 - 82 100 = 80,000 03859 = R2 60,000 40,000 20,000 26 -2 15 -20 -1 3 -21 14--21 3[]--1 23 -1 11 - 21 31--21 SUMMARY OUTPUT Regression Statistics Multiple R 0.621240796 R Square 0.385940127 Adjusted R Square 0.384096104 Standard Error 12341.10946 Observations 335 ANOVA df Significance F 3.82101E-37 Regression Residual Total 1 333 334 SS MS F 31875856241 31875856241 209.2923965 50716893237 152302982.7 82592749478 Intercept Date Coefficients 4433590.986 100.868666 Standard Error t Stat P-value 308897.0225 -14.35297418 1.05574E-36 6.972355971 14.4669415 3.82101E-37 Lower 95% Upper 95% Lower 95.0% Upper 95.0% -5041226.472 -3825955.5 -5041226.472 -3825955.5 87.1532508 114.5840811 87.1532508 114.5840811

Answer & Explanation Solved by verified expert

See Answer