Methods and systems for assessing underwriting and distribution risks associated with subordinate debt

• US 9,892,461 B2
• Filed: 06/09/2008
• Issued: 02/13/2018
• Est. Priority Date: 06/09/2008
• Status: Active Grant

• Current Assignee: GE Corporate Financial Services, Inc. (General Electric Company)
• Original Assignee: GE Corporate Financial Services, Inc. (General Electric Company)
• Inventors: Keyes, Tim Kerry, Keenan, Sean Coleman, Allred, Charles
• Primary Examiner(s): Hamilton, Lalita M
• Application Number:
  US12/135,884
• Publication Number:
  US 20090307146A1
• Time in Patent Office: 3,536 Days
• Field of Search: 705 35- 39
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A method for assessing underwriting and distribution risks associated with a portfolio of subordinate debt is provided. The method is performed using a computer system coupled to a database. The method includes storing in the database historical bond issue data for a period of time preceding and proceeding at least one historical liquidity event and generating a plurality of simulated subordinate debt warehouses using the computer and the historical bond issue data stored in the database. The method also includes calculating a historical loss distribution based on the plurality of simulated subordinate debt warehouses generated. The method also includes determining a value at risk for a portfolio of subordinate debt resulting from a potential liquidity event by applying the historical loss distribution to the portfolio of subordinate debt.

Claims

1. A method for assessing underwriting and distribution risks associated with a portfolio of subordinate debt, said method performed using a server computer device coupled to a database, said method comprising:

identifying at least one historical bond liquidity event impacting bonds included within a bond market index, the bond liquidity event defined at least in part by a predefined decline in the bond market index;

storing in the database historical bond issue data for previously issued high yield bonds, including actual bond prices, for a predetermined period of time before and after the at least one historical bond liquidity event;

generating, by the server computer device, a plurality of simulated subordinate debt warehouses by randomly selecting a plurality of the previously issued high yield bonds stored within the database for each simulated subordinate debt warehouse, such that the high yield bonds in each of the simulated subordinate debt warehouses are different from each other and all of the high yield bonds in the simulated subordinate debt warehouses are from the same predetermined period of time before and after the historical bond liquidity event;

calculating, by the server computer device, a historical loss in value of each of the simulated subordinate debt warehouses based on a price history, over the predetermined period of time, of each of the plurality of the previously issued high yield bonds in the respective simulated subordinate debt warehouse, wherein the price history is automatically extracted by the server computer device from the database;

generating, by the server computer device, a probability curve representing a historical loss distribution of the plurality of simulated subordinate debt warehouses based on a percentage of the historical loss in value of each simulated subordinated debt warehouse relative to an initial value of the respective simulated subordinate debt warehouse;

receiving, at the server computer device from a user using a client computer device, a user input signal, the user input signal including candidate warehouse data and a user risk criteria, the candidate warehouse data including data representing a candidate warehouse having a plurality of candidate bonds for assessment, the user risk criteria including an acceptable value at risk resulting from a potential liquidity event and an acceptable percentage confidence level associated with the acceptable value at risk;

determining, by the server computer device, at least one of an actual value at risk and an actual percentage confidence level for the candidate warehouse resulting from the potential liquidity event by applying the historical loss distribution to the candidate warehouse along with the user risk criteria, wherein the actual value at risk represents a value that an erosion of the candidate warehouse will not exceed based on the historical loss distribution and the acceptable percentage confidence level, and wherein the actual percentage confidence level represents a probability based on the historical loss distribution that the erosion of the candidate warehouse will not exceed the acceptable value at risk; and

in response to determining the at least one of the actual value at risk and the actual percentage confidence level, reporting to the user, by the server computer device via a web-based interface, an indication of whether the user risk criteria has been satisfied, wherein the user risk criteria is determined by the server computer device to be satisfied in response to at least one of (i) the actual value at risk being no greater than the acceptable value at risk, and (ii) the actual percentage confidence level being no less than the acceptable percentage confidence level.

2. A method in accordance with claim 1, wherein applying the historical loss distribution to the portfolio of subordinate debt further comprises:

generating a recommended subordinate debt warehouse that satisfies the selected risk criteria, based on the historical loss distribution.

3. A method in accordance with claim 1, wherein storing in the database historical bond issue data comprises storing a historical bond price from six months preceding the at least one historical bond liquidity event and a historical bond price from six months proceeding the at least one historical bond liquidity event, wherein the at least one historical bond liquidity event comprises a period of time wherein a decline in a bond market index was greater than any other time period being considered.

4. A method in accordance with claim 1, wherein generating the plurality of simulated subordinate debt warehouses and generating the historical loss distribution further comprises performing Monte Carlo trials on the historical bond issue data.

5. A method in accordance with claim 1, wherein generating the historical loss distribution further comprises generating a baseline warehouse loss distribution and generating an adjusted warehouse loss distribution by adjusting the baseline warehouse loss distribution to account for at least one of assumed underwriting fees and assumed demand coupon values.

6. A method in accordance with claim 5, wherein determining the actual value at risk for a portfolio of subordinate debt comprises applying the adjusted warehouse loss distribution to the candidate warehouse.

7. A method in accordance with claim 6, wherein determining the actual value at risk for the candidate warehouse further comprises:

calculating an effective deal size of each potential bond deal included in the candidate warehouse;

calculating a base value at risk of each potential bond deal; and

adjusting the base value at risk of each potential bond deal to determine a final value at risk of each of the potential bond deals.

8. A method in accordance with claim 7, wherein calculating the effective deal size further comprises increasing a deal commitment value to account for differences between at least one of actual underwriting fees and the assumed underwriting fees and actual demand coupon values and the assumed demand coupon values.

9. A method in accordance with claim 1, further comprising:

determining a scaling value by comparing an assumed value of underwriting fees to an actual value of the underwriting fees; and

determining an effective deal size by increasing a deal commitment associated with each of a plurality of bond deals included in the candidate warehouse by the scaling value.

10. A server computer device for assessing underwriting and distribution risks associated with a portfolio of subordinate debt, said server computer device comprising a processor and a database, said server computer device configured to:

identify at least one historical bond liquidity event impacting bonds included within a bond market index, the bond liquidity event defined at least in part by a predefined decline in the bond market index;

store in said database historical bond issue data for previously issued high yield bonds, including actual bond prices, for a predetermined period of time before and after the at least one historical bond liquidity event;

generate a plurality of simulated subordinate debt warehouses by randomly selecting a plurality of the previously issued high yield bonds stored within the database for each simulated subordinate debt warehouse, such that the high yield bonds in each of the simulated subordinate debt warehouses are different from each other and all of the high yield bonds in the simulated subordinate debt warehouses are from the same predetermined period of time before and after the historical bond liquidity event;

calculate, using said processor, a historical loss in value of each of the simulated subordinate debt warehouses based on a price history, over the predetermined period of time, of each of the plurality of the previously issued high yield bonds in the respective simulated subordinate debt warehouse, wherein the price history is automatically extracted by the server computer device from the database;

generate, using said processor, a probability curve representing a historical loss distribution of the plurality of simulated subordinate debt warehouses based on a percentage of the historical loss in value of each simulated subordinated debt warehouse relative to an initial value of the respective simulated subordinate debt warehouse;

receive from a user using a client computer device, a user input signal, the user input signal including candidate warehouse data and a user risk criteria, the candidate warehouse data including data representing a candidate warehouse having a plurality of candidate bonds for assessment, the user risk criteria including an acceptable value at risk resulting from a potential liquidity event and an acceptable percentage confidence level associated with the acceptable value at risk;

determine, using said processor, at least one of an actual value at risk and an actual percentage confidence level for the candidate warehouse resulting from the potential liquidity event by applying the historical loss distribution to the candidate warehouse along with the user risk criteria, wherein the actual value at risk represents a value that an erosion of the candidate warehouse will not exceed based on the historical loss distribution and the acceptable percentage confidence level, and wherein the actual percentage confidence level represents a probability based on the historical loss distribution that the erosion of the candidate warehouse will not exceed the acceptable value at risk; and

in response to determining the at least one of the actual value at risk and the actual percentage confidence level, report to the user, by the server computer device via a web-based interface, an indication of whether the user risk criteria has been satisfied, wherein the user risk criteria is determined by the server computer device to be satisfied in response to at least one of (i) the actual value at risk being no greater than the acceptable value at risk, and (ii) the actual percentage confidence level being no less than the acceptable percentage confidence level.

11. A server computer device in accordance with claim 10, further configured to store at least one of a historical bond price from six months preceding the at least one historical bond liquidity event, a historical bond price on a first day of the event, a historical bond price on a last day of the event, and a historical bond price on from six months proceeding the at least one historical bond liquidity event, wherein the at least one historical bond liquidity event comprises a period of time wherein a decline in a bond market index was greater than any other time period being considered.

12. A server computer device in accordance with claim 10, further configured to perform Monte Carlo trials on the historical bond issue data to generate the plurality of simulated subordinate debt warehouses and the historical loss distribution.

13. A server computer device in accordance with claim 10, further configured to generate a baseline warehouse loss distribution and an adjusted warehouse loss distribution, said server computer device configured to generate the adjusted warehouse loss distribution by adjusting the baseline warehouse loss distribution to account for at least one of assumed underwriting fees and assumed demand coupon values.

14. A server computer device in accordance with claim 10, further configured to:

calculate an effective deal size of each potential bond deal included in the candidate warehouse;

calculate a base value at risk of each potential bond deal; and

adjust the base value at risk of each potential bond deal to determine a final value at risk of each of the potential bond deals.

15. A server computer device in accordance with claim 14, further configured to calculate the effective deal size by increasing a deal commitment value to account for differences between at least one of actual underwriting fees and the assumed underwriting fees and actual demand coupon values and the assumed demand coupon values.

16. A server computer device in accordance with claim 14, further configured to adjust the base value at risk by calculating underwriting fees that exceed the assumed value and reducing the base value at risk to account for the excess underwriting fees.

17. A system for assessing underwriting and distribution risks associated with a portfolio of subordinate debt, said system comprising:

a database for storing information; and

a server system configured to be coupled to said database, said server further configured to;

identify at least one historical bond liquidity event impacting bonds included within a bond market index, the bond liquidity event defined at least in part by a predefined decline in the bond market index;

store in said database historical bond issue data for previously issued high yield bonds, including actual bond prices, for a predetermined period of time before and after the at least one historical bond liquidity event;

generate a plurality of simulated subordinate debt warehouses by randomly selecting a plurality of the previously issued high yield bonds stored within the database for each simulated subordinate debt warehouse, such that the high yield bonds in each of the simulated subordinate debt warehouses are different from each other and all of the high yield bonds in the simulated subordinate debt warehouses are from the same predetermined period of time before and after the historical bond liquidity event;

calculate a historical loss in value of each of the simulated subordinate debt warehouses based on a price history, over the predetermined period of time, of each of the plurality of the previously issued high yield bonds in the respective simulated subordinate debt warehouse, wherein the price history is automatically extracted by the server computer device from the database;

generate a probability curve representing a historical loss distribution of the plurality of simulated subordinate debt warehouses based on a percentage of the historical loss in value of each simulated subordinated debt warehouse relative to an initial value of the respective simulated subordinate debt warehouse;

receive from a user using a client computer device, a user input signal, the user input signal including candidate warehouse data and a user risk criteria, the candidate warehouse data including data representing a candidate warehouse having a plurality of candidate bonds for assessment, the user risk criteria including an acceptable value at risk resulting from a potential liquidity event and an acceptable percentage confidence level associated with the acceptable value at risk;

determine at least one of an actual value at risk and an actual percentage confidence level for the candidate warehouse resulting from the potential liquidity event by applying the historical loss distribution to the candidate warehouse along with the user risk criteria, wherein the actual value at risk represents a value that an erosion of the candidate warehouse will not exceed based on the historical loss distribution and the acceptable percentage confidence level, and wherein the actual percentage confidence level represents a probability based on the historical loss distribution that the erosion of the candidate warehouse will not exceed the acceptable value at risk; and

in response to determining the at least one of the actual value at risk and the actual percentage confidence level, report to the user, by the server computer device via a web-based interface, an indication of whether the user risk criteria has been satisfied, wherein the user risk criteria is determined by the server computer device to be satisfied in response to at least one of (i) the actual value at risk being no greater than the acceptable value at risk, and (ii) the actual percentage confidence level being no less than the acceptable percentage confidence level.

18. A system in accordance with claim 17, wherein said server is further configured to store at least one of a historical bond price from six months preceding the at least one historical bond liquidity event, a historical bond price on a first day of the event, a historical bond price on a last day of the event, and a historical bond price from six months proceeding the at least one historical bond liquidity event, wherein the at least one historical bond liquidity event comprises a period of time wherein a decline in a bond market index was greater than any other time period being considered.

19. A system in accordance with claim 17, wherein said server is further configured to perform Monte Carlo trials on the historical bond issue data to generate the plurality of simulated subordinate debt warehouses and the historical loss distribution.

20. A system in accordance with claim 17, wherein said server is further configured to generate a baseline warehouse loss distribution and an adjusted warehouse loss distribution, said server configured to generate the adjusted warehouse loss distribution by adjusting the baseline warehouse loss distribution to account for at least one of assumed underwriting fees and assumed demand coupon values.

21. A system in accordance with claim 20, wherein said server is further configured to:

calculate an effective deal size of each potential bond deal included in the candidate warehouse;

calculate a base value at risk of each potential bond deal; and

adjust the base value at risk of each potential bond deal to determine a final value at risk of each of the potential bond deals.

22. A system in accordance with claim 21, wherein said server is further configured to calculate the effective deal size by increasing a deal commitment value to account for differences between at least one of actual underwriting fees and the assumed underwriting fees and actual demand coupon values and the assumed demand coupon values.

23. A system in accordance with claim 21, wherein said server is further configured to adjust the base value at risk by reducing the base value at risk to account for underwriting fees that exceed the assumed underwriting fees.

24. A computer program embodied on a non-transitory computer readable medium for assessing underwriting and distribution risks associated with a portfolio of subordinate debt, said program comprising at least one code segment that, when executed by a server computer device, causes the server computer device to:

identify at least one historical bond liquidity event impacting bonds included within a bond market index, the bond liquidity event defined at least in part by a predefined decline in the bond market index;

store in a database historical bond issue data for previously issued high yield bonds, including actual bond prices, for a predetermined period of time before and after the at least one historical bond liquidity event;

generate a plurality of simulated subordinate debt warehouses by randomly selecting a plurality of the previously issued high yield bonds stored within the database for each simulated subordinate debt warehouse, such that the high yield bonds in each of the simulated subordinate debt warehouses are different from each other and all of the high yield bonds in the simulated subordinate debt warehouses are from the same predetermined period of time before and after the historical bond liquidity event;

calculate a historical loss in value of each of the simulated subordinate debt warehouses based on a price history, over the predetermined period of time, of each of the plurality of the previously issued high yield bonds in the respective simulated subordinate debt warehouse, wherein the price history is automatically extracted by the server computer device from the database;

generate a probability curve representing a historical loss distribution of the plurality of simulated subordinate debt warehouses based on a percentage of the historical loss in value of each simulated subordinated debt warehouse relative to an initial value of the respective simulated subordinate debt warehouse;

receive from a user using a client computer device, a user input signal, the user input signal including candidate warehouse data and a user risk criteria, the candidate warehouse data including data representing a candidate warehouse having a plurality of candidate bonds for assessment, the user risk criteria including an acceptable value at risk resulting from a potential liquidity event and an acceptable percentage confidence level associated with the acceptable value at risk;

determine at least one of an actual value at risk and an actual percentage confidence level for the candidate warehouse resulting from the potential liquidity event by applying the historical loss distribution to the candidate warehouse along with the user risk criteria, wherein the actual value at risk represents a value that an erosion of the candidate warehouse will not exceed based on the historical loss distribution and the acceptable percentage confidence level, and wherein the actual percentage confidence level represents a probability based on the historical loss distribution that the erosion of the candidate warehouse will not exceed the acceptable value at risk; and

in response to determining the at least one of the actual value at risk and the actual percentage confidence level, report to the user, by the server computer device via a web-based interface, an indication of whether the user risk criteria has been satisfied, wherein the user risk criteria is determined by the server computer device to be satisfied in response to at least one of (i) the actual value at risk being no greater than the acceptable value at risk, and (ii) the actual percentage confidence level being no less than the acceptable percentage confidence level.

25. A computer program in accordance with claim 24 further comprising at least one code segment that causes the server computer device to store at least one of a historical bond price from six months preceding the at least one historical bond liquidity event, a historical bond price on a first day of the event, a historical bond price on a last day of the event, and a historical bond price from six months proceeding the at least one historical bond liquidity event, wherein the at least one historical bond liquidity event comprises a period of time wherein a decline in a bond market index was greater than any other time period being considered.

26. A computer program in accordance with claim 24 further comprising at least one code segment that causes the server computer device to perform Monte Carlo trials on the historical bond issue data to generate the plurality of simulated subordinate debt warehouses and the historical loss distribution.

27. A computer program in accordance with claim 24 further comprising at least one code segment that causes the server computer device to generate a baseline warehouse loss distribution and an adjusted warehouse loss distribution by adjusting the baseline warehouse loss distribution to account for at least one of assumed underwriting fees and assumed demand coupon values.

28. A computer program in accordance with claim 27 further comprising at least one code segment that causes the server computer device to:

calculate an effective deal size of each potential bond deal included in the candidate warehouse;

calculate a base value at risk of each potential bond deal; and

adjust the base value at risk of each potential bond deal to determine a final value at risk of each of the potential bond deals.

29. A computer program in accordance with claim 28 further comprising at least one code segment that causes the server computer device to calculate the effective deal size by increasing a deal commitment value to account for differences between at least one of actual underwriting fees and the assumed underwriting fees and actual demand coupon values and the assumed demand coupon values.

30. A computer program in accordance with claim 28 further comprising at least one code segment that causes the server computer device to adjust the base value at risk by calculating underwriting fees that exceed the assumed value and reducing the base value at risk to account for the excess underwriting fees.

31. A method for assessing underwriting and distribution risks associated with a portfolio of subordinate debt, said method performed using a server computer device coupled to a database, said method comprising:

identifying at least one historical bond liquidity event impacting bonds included within a bond market index, the bond liquidity event defined at least in part by a predefined decline in the bond market index;

storing in the database historical bond issue data for previously issued high yield bonds, including actual bond prices, for a predetermined period of time before and after the at least one historical bond liquidity event;

generating, by the server computer device, a plurality of simulated subordinate debt warehouses using the historical bond issue data stored in the database, wherein each simulated subordinate debt warehouse includes a plurality of the previously issued high yield bonds randomly selected from the historical bond issue data based on criteria including selecting only bond issues with available market pricing for the first day and last day of the bond liquidity event, a predetermined limit on the number of bonds in each of the simulated subordinate debt warehouses, and a value of the bonds in each of the simulated subordinate debt warehouses, and wherein the high yield bonds in each of the simulated subordinate debt warehouses are different from each other;

calculating, by the server computer device, a historical loss in value of each of the simulated subordinate debt warehouses based on the available market pricing for the first day and last day of the bond liquidity event, wherein the available market pricing is automatically extracted by the server computer device from the database for each of the previously issued high yield bonds associated with the respective simulated subordinate debt warehouse;

generating, by the server computer device, a probability curve representing a historical loss distribution based of the plurality of simulated subordinate debt warehouses based on a percentage of the historical loss in value of each simulated subordinated debt warehouse relative to an initial value of the respective simulated subordinate debt warehouse;

receiving, by the server computer device, from a user using a client computer device, a user input signal, the user input signal including candidate warehouse data and a user risk criteria, the candidate warehouse data including data representing a candidate warehouse having a plurality of candidate bonds for assessment, the user risk criteria including an acceptable value at risk resulting from a potential liquidity event and an acceptable percentage confidence level associated with the acceptable value at risk;

determining, by the server computer device, at least one of an actual value at risk and an actual percentage confidence level for the candidate warehouse resulting from the potential liquidity event by applying the historical loss distribution to the candidate warehouse along with the user risk criteria, wherein the actual value at risk represents a value that an erosion of the candidate warehouse will not exceed based on the historical loss distribution and the acceptable percentage confidence level, and wherein the actual percentage confidence level represents a probability based on the historical loss distribution that the erosion of the candidate warehouse will not exceed the acceptable value at risk; and

in response to determining the at least one of the actual value at risk and the actual percentage confidence level, reporting to the user, by the server computer device via a web-based interface, an indication of whether the user risk criteria has been satisfied, wherein the user risk criteria is determined by the server computer device to be satisfied in response to at least one of (i) the actual value at risk being no greater than the acceptable value at risk, and (ii) the actual percentage confidence level being no less than the acceptable percentage confidence level.