Use python deep learning data complexion programming yo solve all…

Question Answered step-by-step Use python deep learning data complexion programming yo solve all… Use python deep learning data complexion programming yo solve all of theseTemperature control to meet design requirements be sure to answer all qns or i will report An electric furnace intended for the heat treatment of objects consists of a closed enclosure heated by an electrical resistance which supplied by a voltage v(t). Ten objects can be treated simultaneously in the furnace. The heat treatment consists of maintaining the objects for 1 hour at a temperature of 1,200 °C (regulated in an optimal way because the objects are destroyed if the temperature exceeds 1,400 °C). between each two treatments, a time of 24 minutes is necessary to cool the oven and handling of products. The electric furnace is governed by the differential equation de + 2000 = 0.020(e) Questions a – determine the following – Open loop transfer function G(s) of the electric furnace. – Static gain. – What would happen if the furnace was powered continuously and in an open loop? Assuming, despite everything, that we apply a 100 V input to the electric furnace in open loop. b-after what time would a temperature of 1200 °C be reached in the oven? We decide to control the temperature in the electric furnace by using a temperature sensor which delivers a voltage u(t). The sensor is governed by the differential equation u(t) + 2 = 510-30(t) – du dt We also interduce a gain K in the feedforward loop. c – Draw a block diagram of the control loop and determine the closed loop transfer function. Determine the stability conditions of this system. d – Imposing a phase margin Ag = 45°, determine the value of the rise time in the closed loop system. e – We obviously want to regulate the temperature of the electric furnace at 1,200 °C. Determine the value of the setpoint to be introduced into the system. With the system tuned to obtain a phase margin Ap = 45°, what is the maximum temperature reached in the electric furnace? Conclude. f- we would like to limit the overshoot at 10% , determine the raise time and how many object can be treated in 24 hours. d – We want to reach a processing rate of 100 objects per 24 hours. Determine the value of K that allow us to meet this objective. What is the phase margin then? What type of corrector should be added to the feedforward loop in order to limit the overshoot at 10% while maintain the desired processing rateTemperature control to meet design requirements An electric furnace intended for the heat treatment of objects consists of a closed enclosure heated by an electrical resistance which supplied by a voltage v(t). Ten objects can be treated simultaneously in the furnace. The heat treatment consists of maintaining the objects for 1 hour at a temperature of 1,200 °C (regulated in an optimal way because the objects are destroyed if the temperature exceeds 1,400 °C). between each two treatments, a time of 24 minutes is necessary to cool the oven and handling of products. The electric furnace is governed by the differential equation de + 2000 = 0.020(e) Questions a – determine the following – Open loop transfer function G(s) of the electric furnace. – Static gain. – What would happen if the furnace was powered continuously and in an open loop? Assuming, despite everything, that we apply a 100 V i1 Sample and description statistics (5 points) From Eikon download bond issues with the following characteristics: 1. Sector: Consumer Goods, Manufacturing, Telephone, Transportations 2. Domicile: United States 3. Amount Outstanding: > 100,000,000 4. Coupon: > 0% 2 5. Maturity: > 1-October-2024 6. S&P Long-term Issue Credit Rating: > D. 7. make sure that the following filters are visible on the left hand side: (a) Currency (b) Convertible (c) Callable (d) Putable (e) Seniority (f) Market of Issue You should end up with more than 2,000 bonds; thus, you will need to download the data to Excel in batches (e.g., first, download small bond issues, then medium, and lastly with large bond issues). Discuss briefly your sample, including the number of observations, outliers. Provide the descriptive statistics of the sample. How you choose to s entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods (this task should include at least one pie chart, one histogram, and one scatter plot) while also noting any peculiarities within the data set. You should put more emphasis on variables that are the dependent variables in the regressions estimated in other tasks. 2 Which bonds are more likely to include “a call” feature? (7 points) A bond issuer can repurchase its bonds before their maturity if they include a call feature. Firstly, identify statistically significant characteristics of callable bonds. You may consider, issue size, maturity, industry, credit rating, and other variables available in Eikon. Secondly, compute the average of the individual marginal effects of the amount outstanding on the probability that a bond includes a call feature. Thirdly, estimate the probability that a bond with the following characteristics includes a call feature: 3 • maturity: 10 years • coupon: 2.5% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • market of issue: Global • puttable: no. Do the results suggest that this bond is callable or not? In this task, you are expected to use a logit regression analysis. To ensure that the results are robust, estimate at least two regression models (e.g., in the first regression model, one includes amount in $ and in the second model, one uses the natural logarithm of amount in $). 3 Which bonds are more likely to be issued in the domestic and foreign markets? (6 points) “Market of Issue” can take the following values: • Domestic: a bond is issued in the US • Global: a bond is issued in the US and foreign markets • Eurobonds: a bond denominated in USD is issued in the foreign market. You need to estimate the probabilities that a bond with the following characteristics is issued in each market: 4 • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • callable: yes • puttable: no. According to the analysis, what is the most likely market of issue of this bond? In the analysis, estimate multinomial logit regression model and briefly discuss the determinants of “Market of Issue.” 4 Estimating yield for a hypothetical bond (7 points) Lastly, you need to estimate the yield for a bond with the following characteristics: • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics 5 • domicile: USA • convertible: no • callable: yes • puttable: no • market of issue: Global. To ensure that the results are robust, estimate at least 3 regression models (e.g., in the first regression model, one includes amount in $, in the second model, one uses the natural logarithm of amount in $, and the third model features something else). Briefly discuss the determinants of yield. Using one of the regression models, compute two additional yields: 1. the amount is $1,000,000,000, other bond characteristics the same as above 2. S&P credit rating is AA, other bond characteristics the same as above (i.e., amount: $750,000,000 etc.). Are the results the same as the main estimate? Why?nput to the electric furnace in open loop. b-after what time would a temperature of 1200 °C be reached in the oven? We decide to control the temperature in the electric furnace by using a temperature sensor which delivers a voltage u(t). The sensor is governed by the differential equation u(t) + 2 = 510-30(t) – du dt We also interduce a gain K in the feedforward loop. c – Draw a block diagram of the control loop and determine the closed loop transfer function. Determine the stability conditions of this system. d – Imposing a phase margin Ag = 45°, determine the value of the rise time in the closed loop system. e – We obviously want to regulate the temperature of the electric furnace at 1,200 °C. Determine the value of the setpoint to be introduced into the system. With the system tuned to obtain a phase margin Ap = 45°, what is the maximum temperature reached in the electric furnace? Conclude. f- we would like to limit the overshoot at 10% , determine the raise time and how many object can be treated in 24 hours. d – We want to reach a processing rate of 100 objects per 24 hours. Determine the value of K that allow us to meet this objective. What is the phase margin then? What type of corrector should be added to the feedforward loop in order to limit the overshoot at 10% while maintain the desired processing rateTemperature control to meet design requirements An electric furnace intended for the heat treatment of objects consists of a closed enclosure heated by an electrical resistance which supplied by a voltage v(t). Ten objects can be treated simultaneously in the furnace. The heat treatment consists of maintaining the objects for 1 hour at a temperature of 1,200 °C (regulated in an optimal way because the objects are destroyed if the temperature exceeds 1,400 °C). between each two treatments, a time of 24 minutes is necessary to cool the oven and handling of products. The electric furnace is governed by the differential equation de + 2000 = 0.020(e) Questions a – determine the following – Open loop transfer function G(s) of the electric furnace. – Static gain. – What would happen if the furnace was powered continuously and in an open loop? Assuming, despite everything, that we apply a 100 V input to the electric furnace in open loop. b-after what time would a temperature of 1200 °C be reached in the oven? We decide to control the temperature in the electric furnace by using a temperature sensor which delivers a voltage u(t). The sensor is governed by the differential equation u(t) + 2 = 510-30(t) – du dt We also interduce a gain K in the feedforward loop. c – Draw a block diagram of the control loop and determine the closed loop transfer function. Determine the stability conditions of this system. d – Imposing a phase margin Ag = 45°, determine the value of the rise time in the closed loop system. e – We obviously want to regulate the temperature of the electric furnace at 1,200 °C. Determine the value of the setpoint to be introduced into the system. With the system tuned to obtain a phase margin Ap = 45°, what is the maximum temperature reached in the electric furnace? Conclude. f- we would like to limit the overshoot at 10% , determine the raise time and how many object can be treated in 24 hours. d – We want to reach a processing rate of 100 objects per 24 hours. Determine the value of K that allow us to meet this objective. What is the phase margin then? What type of corrector should be added to the feedforward loop in order to limit the overshoot at 10% while maintain the desired processing rate1 Sample and description statistics (5 points) From Eikon download bond issues with the following characteristics: 1. Sector: Consumer Goods, Manufacturing, Telephone, Transportations 2. Domicile: United States 3. Amount Outstanding: > 100,000,000 4. Coupon: > 0% 2 5. Maturity: > 1-October-2024 6. S&P Long-term Issue Credit Rating: > D. 7. make sure that the following filters are visible on the left hand side: (a) Currency (b) Convertible (c) Callable (d) Putable (e) Seniority (f) Market of Issue You should end up with more than 2,000 bonds; thus, you will need to download the data to Excel in batches (e.g., first, download small bond issues, then medium, and lastly with large bond issues). Discuss briefly your sample, including the number of observations, outliers. Provide the descriptive statistics of the sample. How you choose to d is entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods (this task should include at least one pie chart, one histogram, and one scatter plot) while also noting any peculiarities within the data set. You should put more emphasis on variables that are the dependent variables in the regressions estimated in other tasks. 2 Which bonds are more likely to include “a call” feature? (7 points) A bond issuer can repurchase its bonds before their maturity if they include a call feature. Firstly, identify statistically significant characteristics of callable bonds. You may consider, issue size, maturity, industry, credit rating, and other variables available in Eikon. Secondly, compute the average of the individual marginal effects of the amount outstanding on the probability that a bond includes a call feature. Thirdly, estimate the probability that a bond with the following characteristics includes a call feature: 3 • maturity: 10 years • coupon: 2.5% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • market of issue: Global • puttable: no. Do the results suggest that this bond is callable or not? In this task, you are expected to use a logit regression analysis. To ensure that the results are robust, estimate at least two regression models (e.g., in the first regression model, one includes amount in $ and in the second model, one uses the natural logarithm of amount in $). 3 Which bonds are more likely to be issued in the domestic and foreign markets? (6 points) “Market of Issue” can take the following values: • Domestic: a bond is issued in the US • Global: a bond is issued in the US and foreign markets • Eurobonds: a bond denominated in USD is issued in the foreign market. You need to estimate the probabilities that a bond with the following characteristics is issued in each market: 4 • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • callable: yes • puttable: no. According to the analysis, what is the most likely market of issue of this bond? In the analysis, estimate multinomial logit regression model and briefly discuss the determinants of “Market of Issue.” 4 Estimating yield for a hypothetical bond (7 points) Lastly, you need to estimate the yield for a bond with the following characteristics: • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics 5 • domicile: USA • convertible: no • callable: yes • puttable: no • market of issue: Global. To ensure that the results are robust, estimate at least 3 regression models (e.g., in the first regression model, one includes amount in $, in the second model, one uses the natural logarithm of amount in $, and the third model features something else). Briefly discuss the determinants of yield. Using one of the regression models, compute two additional yields: 1. the amount is $1,000,000,000, other bond characteristics the same as above 2. S&P credit rating is AA, other bond characteristics the same as above (i.e., amount: $750,000,000 etc.). Are the results the same as the main estimate? Why?1 Sample and description statistics (5 points) From Eikon download bond issues with the following characteristics: 1. Sector: Consumer Goods, Manufacturing, Telephone, Transportations 2. Domicile: United States 3. Amount Outstanding: > 100,000,000 4. Coupon: > 0% 2 5. Maturity: > 1-October-2024 6. S&P Long-term Issue Credit Rating: > D. 7. make sure that the following filters are visible on the left hand side: (a) Currency (b) Convertible (c) Callable (d) Putable (e) Seniority (f) Market of Issue You should end up with more than 2,000 bonds; thus, you will need to download the data to Excel in batches (e.g., first, download small bond issues, then medium, and lastly with large bond issues). Discuss briefly your sample, including the number of observations, outliers. Provide the descriptive statistics of the sample. How you choose to ds is entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods (this task should include at least one pie chart, one histogram, and one scatter plot) while also noting any peculiarities within the data set. You should put more emphasis on variables that are the dependent variables in the regressions estimated in other tasks. 2 Which bonds are more likely to include “a call” feature? (7 points) A bond issuer can repurchase its bonds before their maturity if they include a call feature. Firstly, identify statistically significant characteristics of callable bonds. You may consider, issue size, maturity, industry, credit rating, and other variables available in Eikon. Secondly, compute the average of the individual marginal effects of the amount outstanding on the probability that a bond includes a call feature. Thirdly, estimate the probability that a bond with the following characteristics includes a call feature: 3 • maturity: 10 years • coupon: 2.5% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • market of issue: Global • puttable: no. Do the results suggest that this bond is callable or not? In this task, you are expected to use a logit regression analysis. To ensure that the results are robust, estimate at least two regression models (e.g., in the first regression model, one includes amount in $ and in the second model, one uses the natural logarithm of amount in $). 3 Which bonds are more likely to be issued in the domestic and foreign markets? (6 points) “Market of Issue” can take the following values: • Domestic: a bond is issued in the US • Global: a bond is issued in the US and foreign markets • Eurobonds: a bond denominated in USD is issued in the foreign market. You need to estimate the probabilities that a bond with the following characteristics is issued in each market: 4 • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • callable: yes • puttable: no. According to the analysis, what is the most likely market of issue of this bond? In the analysis, estimate multinomial logit regression model and briefly discuss the determinants of “Market of Issue.” 4 Estimating yield for a hypothetical bond (7 points) Lastly, you need to estimate the yield for a bond with the following characteristics: • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics 5 • domicile: USA • convertible: no • callable: yes • puttable: no • market of issue: Global. To ensure that the results are robust, estimate at least 3 regression models (e.g., in the first regression model, one includes amount in $, in the second model, one uses the natural logarithm of amount in $, and the third model features something else). Briefly discuss the determinants of yield. Using one of the regression models, compute two additional yields: 1. the amount is $1,000,000,000, other bond characteristics the same as above 2. S&P credit rating is AA, other bond characteristics the same as above (i.e., amount: $750,000,000 etc.). Are the results the same as the main estimate? Why?1 Sample and description statistics (5 points) From Eikon download bond issues with the following characteristics: 1. Sector: Consumer Goods, Manufacturing, Telephone, Transportations 2. Domicile: United States 3. Amount Outstanding: > 100,000,000 4. Coupon: > 0% 2 5. Maturity: > 1-October-2024 6. S&P Long-term Issue Credit Rating: > D. 7. make sure that the following filters are visible on the left hand side: (a) Currency (b) Convertible (c) Callable (d) Putable (e) Seniority (f) Market of Issue You should end up with more than 2,000 bonds; thus, you will need to download the data to Excel in batches (e.g., first, download small bond issues, then medium, and lastly with large bond issues). Discuss briefly your sample, including the number of observations, outliers. Provide the descriptive statistics of the sample. How you choose to  is entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods (this task should include at least one pie chart, one histogram, and one scatter plot) while also noting any peculiarities within the data set. You should put more emphasis on variables that are the dependent variables in the regressions estimated in other tasks. 2 Which bonds are more likely to include “a call” feature? (7 points) A bond issuer can repurchase its bonds before their maturity if they include a call feature. Firstly, identify statistically significant characteristics of callable bonds. You may consider, issue size, maturity, industry, credit rating, and other variables available in Eikon. Secondly, compute the average of the individual marginal effects of the amount outstanding on the probability that a bond includes a call feature. Thirdly, estimate the probability that a bond with the following characteristics includes a call feature: 3 • maturity: 10 years • coupon: 2.5% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • market of issue: Global • puttable: no. Do the results suggest that this bond is callable or not? In this task, you are expected to use a logit regression analysis. To ensure that the results are robust, estimate at least two regression models (e.g., in the first regression model, one includes amount in $ and in the second model, one uses the natural logarithm of amount in $). 3 Which bonds are more likely to be issued in the domestic and foreign markets? (6 points) “Market of Issue” can take the following values: • Domestic: a bond is issued in the US • Global: a bond is issued in the US and foreign markets • Eurobonds: a bond denominated in USD is issued in the foreign market. You need to estimate the probabilities that a bond with the following characteristics is issued in each market: 4 • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • callable: yes • puttable: no. According to the analysis, what is the most likely market of issue of this bond? In the analysis, estimate multinomial logit regression model and briefly discuss the determinants of “Market of Issue.” 4 Estimating yield for a hypothetical bond (7 points) Lastly, you need to estimate the yield for a bond with the following characteristics: • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics 5 • domicile: USA • convertible: no • callable: yes • puttable: no • market of issue: Global. To ensure that the results are robust, estimate at least 3 regression models (e.g., in the first regression model, one includes amount in $, in the second model, one uses the natural logarithm of amount in $, and the third model features something else). Briefly discuss the determinants of yield. Using one of the regression models, compute two additional yields: 1. the amount is $1,000,000,000, other bond characteristics the same as above 2. S&P credit rating is AA, other bond characteristics the same as above (i.e., amount: $750,000,000 etc.). Are the results the same as the main estimate? Why?1 Sample and description statistics (5 points) From Eikon download bond issues with the following characteristics: 1. Sector: Consumer Goods, Manufacturing, Telephone, Transportations 2. Domicile: United States 3. Amount Outstanding: > 100,000,000 4. Coupon: > 0% 2 5. Maturity: > 1-October-2024 6. S&P Long-term Issue Credit Rating: > D. 7. make sure that the following filters are visible on the left hand side: (a) Currency (b) Convertible (c) Callable (d) Putable (e) Seniority (f) Market of Issue You should end up with more than 2,000 bonds; thus, you will need to download the data to Excel in batches (e.g., first, download small bond issues, then medium, and lastly with large bond issues). Discuss briefly your sample, including the number of observations, outliers. Provide the descriptive statistics of the sample. How you choose to  is entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods (this task should include at least one pie chart, one histogram, and one scatter plot) while also noting any peculiarities within the data set. You should put more emphasis on variables that are the dependent variables in the regressions estimated in other tasks. 2 Which bonds are more likely to include “a call” feature? (7 points) A bond issuer can repurchase its bonds before their maturity if they include a call feature. Firstly, identify statistically significant characteristics of callable bonds. You may consider, issue size, maturity, industry, credit rating, and other variables available in Eikon. Secondly, compute the average of the individual marginal effects of the amount outstanding on the probability that a bond includes a call feature. Thirdly, estimate the probability that a bond with the following characteristics includes a call feature: 3 • maturity: 10 years • coupon: 2.5% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • market of issue: Global • puttable: no. Do the results suggest that this bond is callable or not? In this task, you are expected to use a logit regression analysis. To ensure that the results are robust, estimate at least two regression models (e.g., in the first regression model, one includes amount in $ and in the second model, one uses the natural logarithm of amount in $). 3 Which bonds are more likely to be issued in the domestic and foreign markets? (6 points) “Market of Issue” can take the following values: • Domestic: a bond is issued in the US • Global: a bond is issued in the US and foreign markets • Eurobonds: a bond denominated in USD is issued in the foreign market. You need to estimate the probabilities that a bond with the following characteristics is issued in each market: 4 • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • callable: yes • puttable: no. According to the analysis, what is the most likely market of issue of this bond? In the analysis, estimate multinomial logit regression model and briefly discuss the determinants of “Market of Issue.” 4 Estimating yield for a hypothetical bond (7 points) Lastly, you need to estimate the yield for a bond with the following characteristics: • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics 5 • domicile: USA • convertible: no • callable: yes • puttable: no • market of issue: Global. To ensure that the results are robust, estimate at least 3 regression models (e.g., in the first regression model, one includes amount in $, in the second model, one uses the natural logarithm of amount in $, and the third model features something else). Briefly discuss the determinants of yield. Using one of the regression models, compute two additional yields: 1. the amount is $1,000,000,000, other bond characteristics the same as above 2. S&P credit rating is AA, other bond characteristics the same as above (i.e., amount: $750,000,000 etc.). Are the results the same as the main estimate? Why?1 Sample and description statistics (5 points) From Eikon download bond issues with the following characteristics: 1. Sector: Consumer Goods, Manufacturing, Telephone, Transportations 2. Domicile: United States 3. Amount Outstanding: > 100,000,000 4. Coupon: > 0% 2 5. Maturity: > 1-October-2024 6. S&P Long-term Issue Credit Rating: > D. 7. make sure that the following filters are visible on the left hand side: (a) Currency (b) Convertible (c) Callable (d) Putable (e) Seniority (f) Market of Issue You should end up with more than 2,000 bonds; thus, you will need to download the data to Excel in batches (e.g., first, download small bond issues, then medium, and lastly with large bond issues). Discuss briefly your sample, including the number of observations, outliers. Provide the descriptive statistics of the sample. How you choose to is entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods (this task should include at least one pie chart, one histogram, and one scatter plot) while also noting any peculiarities within the data set. You should put more emphasis on variables that are the dependent variables in the regressions estimated in other tasks. 2 Which bonds are more likely to include “a call” feature? (7 points) A bond issuer can repurchase its bonds before their maturity if they include a call feature. Firstly, identify statistically significant characteristics of callable bonds. You may consider, issue size, maturity, industry, credit rating, and other variables available in Eikon. Secondly, compute the average of the individual marginal effects of the amount outstanding on the probability that a bond includes a call feature. Thirdly, estimate the probability that a bond with the following characteristics includes a call feature: 3 • maturity: 10 years • coupon: 2.5% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • market of issue: Global • puttable: no. Do the results suggest that this bond is callable or not? In this task, you are expected to use a logit regression analysis. To ensure that the results are robust, estimate at least two regression models (e.g., in the first regression model, one includes amount in $ and in the second model, one uses the natural logarithm of amount in $). 3 Which bonds are more likely to be issued in the domestic and foreign markets? (6 points) “Market of Issue” can take the following values: • Domestic: a bond is issued in the US • Global: a bond is issued in the US and foreign markets • Eurobonds: a bond denominated in USD is issued in the foreign market. You need to estimate the probabilities that a bond with the following characteristics is issued in each market: 4 • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • callable: yes • puttable: no. According to the analysis, what is the most likely market of issue of this bond? In the analysis, estimate multinomial logit regression model and briefly discuss the determinants of “Market of Issue.” 4 Estimating yield for a hypothetical bond (7 points) Lastly, you need to estimate the yield for a bond with the following characteristics: • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics 5 • domicile: USA • convertible: no • callable: yes • puttable: no • market of issue: Global. To ensure that the results are robust, estimate at least 3 regression models (e.g., in the first regression model, one includes amount in $, in the second model, one uses the natural logarithm of amount in $, and the third model features something else). Briefly discuss the determinants of yield. Using one of the regression models, compute two additional yields: 1. the amount is $1,000,000,000, other bond characteristics the same as above 2. S&P credit rating is AA, other bond characteristics the same as above (i.e., amount: $750,000,000 etc.). Are the results the same as the main estimate? Why?1 Sample and description statistics (5 points) From Eikon download bond issues with the following characteristics: 1. Sector: Consumer Goods, Manufacturing, Telephone, Transportations 2. Domicile: United States 3. Amount Outstanding: > 100,000,000 4. Coupon: > 0% 2 5. Maturity: > 1-October-2024 6. S&P Long-term Issue Credit Rating: > D. 7. make sure that the following filters are visible on the left hand side: (a) Currency (b) Convertible (c) Callable (d) Putable (e) Seniority (f) Market of Issue You should end up with more than 2,000 bonds; thus, you will need to download the data to Excel in batches (e.g., first, download small bond issues, then medium, and lastly with large bond issues). Discuss briefly your sample, including the number of observations, outliers. Provide the descriptive statistics of the sample. How you choose to  is entirely at your discretion. However, it is recommended that you consider using both summary statistic and graphical methods (this task should include at least one pie chart, one histogram, and one scatter plot) while also noting any peculiarities within the data set. You should put more emphasis on variables that are the dependent variables in the regressions estimated in other tasks. 2 Which bonds are more likely to include “a call” feature? (7 points) A bond issuer can repurchase its bonds before their maturity if they include a call feature. Firstly, identify statistically significant characteristics of callable bonds. You may consider, issue size, maturity, industry, credit rating, and other variables available in Eikon. Secondly, compute the average of the individual marginal effects of the amount outstanding on the probability that a bond includes a call feature. Thirdly, estimate the probability that a bond with the following characteristics includes a call feature: 3 • maturity: 10 years • coupon: 2.5% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • market of issue: Global • puttable: no. Do the results suggest that this bond is callable or not? In this task, you are expected to use a logit regression analysis. To ensure that the results are robust, estimate at least two regression models (e.g., in the first regression model, one includes amount in $ and in the second model, one uses the natural logarithm of amount in $). 3 Which bonds are more likely to be issued in the domestic and foreign markets? (6 points) “Market of Issue” can take the following values: • Domestic: a bond is issued in the US • Global: a bond is issued in the US and foreign markets • Eurobonds: a bond denominated in USD is issued in the foreign market. You need to estimate the probabilities that a bond with the following characteristics is issued in each market: 4 • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics • domicile: USA • convertible: no • callable: yes • puttable: no. According to the analysis, what is the most likely market of issue of this bond? In the analysis, estimate multinomial logit regression model and briefly discuss the determinants of “Market of Issue.” 4 Estimating yield for a hypothetical bond (7 points) Lastly, you need to estimate the yield for a bond with the following characteristics: • maturity: 10 years • coupon: 3% • amount: $750,000,000 • currency: US dollars • seniority: senior unsecured • S&P credit rating: A • sector: Electronics 5 • domicile: USA • convertible: no • callable: yes • puttable: no • market of issue: Global. To ensure that the results are robust, estimate at least 3 regression models (e.g., in the first regression model, one includes amount in $, in the second model, one uses the natural logarithm of amount in $, and the third model features something else). Briefly discuss the determinants of yield. Using one of the regression models, compute two additional yields: 1. the amount is $1,000,000,000, other bond characteristics the same as above 2. S&P credit rating is AA, other bond characteristics the same as above (i.e., amount: $750,000,000 etc.). Are the results the same as the main estimate? Why? Computer Science Engineering & Technology Software engineering Share QuestionEmailCopy link Comments (0)