Financial activity based on natural peril events

• US 20090024543A1
• Filed: 08/06/2008
• Published: 01/22/2009
• Est. Priority Date: 12/21/2004
• Status: Active Grant

• Current Assignee: Weather RISK Solutions LLC
• Original Assignee: Weather RISK Solutions LLC
• Inventors: Bequillard, Alfredo L., Protter, Philip Elliott, Wilks, Daniel S., Nyren, Alexander P., Horowitz, Kenneth A.
• Granted Patent: US 8,266,042 B2
• Time in Patent Office: Days
• US Class Current: 705/400
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06Q 50/34 Betting or bookmaking, e.g. Internet betting

Abstract

A computer implemented method and system for automatically setting prices of financial products in a financial activity having a plurality of possible outcomes, includes receiving a first request from a participant terminal to purchase a financial product for one of the possible outcomes, i; and electronically computing a price for the requested financial product, in response to the first request, based at least in part on a first formula. Also included is a method for automatically updating the prices for all other outcomes, other than the outcome, i, based at least in part on a second formula. In one example, the financial products include contracts in a one-sided market of buyer participants where the outcomes are mutually exclusive and collectively exhaustive

Claims

1. A computer implemented method for automatically setting prices of financial products in a financial activity having a plurality of possible outcomes, comprising the steps of:

receiving a first request from a participant terminal to purchase a financial product for one of the possible outcomes, i; and

electronically computing a price for the requested financial product, in response to the first request, based at least in part on the following first formula

Pⁱ_t=Kπ

ⁱ_t=K[π

ⁱ<sub>t−

1</sub>+α

_tπ

ⁱ<sub>t−

1</sub>(1−

π

ⁱ<sub>t−

1</sub>)]

where

i is the outcome requested by the participant,

t is a time index counter designating the current transaction,

t−

1 is a time index counter designating the last previous transaction,

Pⁱ_t is the price for the purchase requested by the participant

K=c exp [rj/365],

c is a scaling constant,

r is a constant proportional to the short term annualized interest rate,

j is the relative Julian date since the financial activity was started,

π

ⁱ<sub>t−

1</sub> is the last previously calculated pricing probability for outcome i

Kπ

ⁱ<sub>t−

1</sub> is the last previously calculated price for outcome i, and

α

_t is a price adjustment parameter, having a value between 0 and 0.1

2. The method according to claim 1 further comprising automatically updating the prices for all other outcomes, other than the outcome, i, based at least in part on the following second formula

P^k_t=Kπ

^k_t=K[π

^k<sub>t−

1</sub>(1−

α

_tπ

ⁱ<sub>t−

1</sub>)], k≠

i

where

k, where k≠

i represents the set of all other outcomes,

t is a time index counter designating the current transaction,

t−

1 is a time index counter designating the last previous transaction,

P^k_t where k≠

i is the set of all other prices, updated to take into account the purchase requested by the participant,

π

^k<sub>t−

1</sub> is the latest calculated pricing probability for outcome k.

Kπ

^k<sub>t−

1</sub> is the latest calculated price for outcome k.

K=c exp [rj/365],

c is a scaling constant,

r is a constant proportional to the short term cost of money,

j is the relative Julian date since the financial activity was started, and

α

_t is a price adjustment parameter, having a value between 0 and 0.1.

3. The method according to claim 2 further comprising the steps of:

receiving a second request from a participant terminal to purchase a financial product for one of the possible outcomes;

delivering a price for the second request to the user terminal after the price for the requested financial product is computed and the prices for all other outcomes are updated, in response to the first request.

4. The method according to claim 1 further comprising the step of setting an initial price for each outcome, prior to receiving an initial request.

5. The method according to claim 1 further comprising the step of adding a null outcome that is deemed to have occurred when none of the other outcomes have occurred.

6. The method according to claim 2 further comprising the step of processing a request for multiple financial products for the same outcome by successively repeating the steps of calculating a price for each financial product and updating prices for all other outcomes for each financial product, taken one at a time.

7. The method according to claim 1 wherein the outcomes are mutually exclusive.

8. The method according to claim 1 wherein the outcomes are collectively exhaustive

9. The method according to claim 1 wherein the outcomes are mutually exclusive and collectively exhaustive

10. The method according to claim 1 wherein the financial products comprise contracts in a one-sided market of buyer participants.

11. The method according to claim 2 wherein the first request from a participant terminal is sent to a computer having memory with a data structure stored in said memory, said data structure comprising the first and the second formula.

12. The method according to claim 11 wherein the prices corresponding to the possible outcomes are stored in said memory.

13. A computer implemented method for automatically pricing financial products in a financial activity for a plurality of possible outcomes, consistent with the risks as perceived at any given time by the activity participants, comprising the steps of:

receiving a first request from a participant terminal to purchase a financial product for one of the possible outcomes, i;

electronically computing a price for the one possible outcome based at least in part on the following first formula

Pⁱ_t=Kπ

ⁱ_t=K[π

ⁱ<sub>t−

1</sub>+α

_tπ

ⁱ<sub>t−

1</sub>(1−

π

ⁱ<sub>t−

1</sub>)]

where

i is the outcome requested by the participant,

t is a time index counter designating the current transaction,

t−

1 is a time index counter designating the last previous transaction,

Pⁱ_t is the price for the purchase requested by the participant

K=c exp [rj/365],

c is a scaling constant,

r is a constant proportional to the short term annualized interest rate,

j is the relative Julian date since the financial activity was started,

π

ⁱ<sub>t−

1</sub> is the last previously calculated pricing probability for outcome I,

Kπ

ⁱ<sub>t−

1</sub> is the last previously calculated price for outcome i, and

α

_t is a price adjustment parameter, having a value between 0 and 0.1; and

updating all other prices based at least in part on the following second formula

P^k_t=Kπ

^k_t=K[π

^k<sub>t−

1</sub>(1−

α

_tπ

ⁱ<sub>t−

1</sub>)], k≠

i

where

k, where k≠

i represents the set of all other outcomes,

t is a time index counter designating the current transaction,

t−

1 is a time index counter designating the last previous transaction,

P^k_t where k≠

i is the set of all other prices, updated to take into account the purchase requested by the participant,

π

^k<sub>t−

1</sub> is the latest calculated pricing probability for outcome k.

Kπ

^k<sub>t−

1</sub> is the latest calculated price for outcome k.

K=c exp [rj/365],

c is a scaling constant,

r is a constant proportional to the short term cost of money,

j is the relative Julian date since the financial activity was started, and

α

_t is a price adjustment parameter, having a value between 0 and 0.1.

14. The method according to claim 13 wherein the first request from a participant terminal is sent to a computer having memory with a data structure stored in said memory, said data structure comprising the first and the second formulae.

15. The method according to claim 13 wherein the prices corresponding to the possible outcomes are stored in said memory.

16. The method according to claim 13 further comprising the step of setting an initial price for each outcome, prior to receiving an initial request.

17. The method according to claim 13 further comprising the step of adding a null outcome that is deemed to have occurred when none of the other outcomes have occurred.

18. The method according to claim 13 further comprising the steps of:

receiving a second request from a participant terminal to purchase a financial product for one of the possible outcomes;

delivering a price for the second request to the user terminal after the price for the requested financial product is computed and the prices for all other outcomes are updated, in response to the first request.

19. The method according to claim 13 further comprising the step of processing a request for multiple financial products for the same outcome by successively repeating the steps of calculating a price for each financial product and updating prices for all other outcomes for each financial product, taken one at a time.

20. The method according to claim 13 wherein the outcomes are mutually exclusive and collectively exhaustive

21. A computer implemented system for automatically setting prices of financial products in a financial activity having a plurality of possible outcomes, comprising:

a network for receiving a first request from a participant terminal to purchase a financial product for one of the possible outcomes, i;

a computer having memory with a data structure stored in said memory, said data structure comprising the following first formula

Pⁱ_t=Kπ

ⁱ_t=K[π

ⁱ<sub>t−

1</sub>+α

_tπ

ⁱ<sub>t−

1</sub>(1−

π

ⁱ<sub>t−

1</sub>)]

where

i is the outcome requested by the participant,

t is a time index counter designating the current transaction,

t−

1 is a time index counter designating the last previous transaction,

Pⁱ_t is the price for the purchase requested by the participant

K=c exp [rj/365],

c is a scaling constant,

r is a constant proportional to the short term annualized interest rate,

j is the relative Julian date since the financial activity was started,

π

ⁱ<sub>t−

1</sub> is the last previously calculated pricing probability for outcome I,

Kπ

ⁱ<sub>t−

1</sub> is the last previously calculated price for outcome i, and

α

_t is a price adjustment parameter, having a value between 0 and 0.1; and

the computer electronically computes a price for the requested financial product, in response to the first request, based at least in part on the first formula.

22. The method according to claim 21 wherein said data structure further comprises the following second formula

P^k_t=Kπ

^k_t=K[π

^k<sub>t−

1</sub>(1−

α

_tπ

ⁱ<sub>t−

1</sub>)], k≠

i

where

k, where k≠

i represents the set of all other outcomes,

t is a time index counter designating the current transaction,

t−

1 is a time index counter designating the last previous transaction,

P^k_t where k≠

i is the set of all other prices, updated to take into account the purchase requested by the participant,

π

^k<sub>t−

1</sub> is the latest calculated pricing probability for outcome k.

Kπ

^k<sub>t−

1</sub> is the latest calculated price for outcome k.

K=c exp [rj/365],

c is a scaling constant,

r is a constant proportional to the short term cost of money,

j is the relative Julian date since the financial activity was started, and

α

_t is a price adjustment parameter, having a value between 0 and 0.1; and

the computer electronically automatically updates the prices for all other outcomes, other than the outcome, i, based at least in part on the second formula

23. The system according to claim 22 wherein the prices corresponding to the possible outcomes are stored in said memory.

24. The system according to claim 22 wherein initial prices corresponding to the possible outcomes are stored in said memory, prior to receiving an initial request.

25. An article of manufacture including a machine readable medium for causing a computer system to automatically set prices of financial products in a financial activity having a plurality of possible outcomes, comprising:

an input for receiving a first request from a participant terminal to purchase a financial product for one of the possible outcomes, i; and

a data structure comprising the following first formula

Pⁱ_t=Kπ

ⁱ_t=K[π

ⁱ<sub>t−

1</sub>+α

_tπ

ⁱ<sub>t−

1</sub>(1−

π

ⁱ<sub>t−

1</sub>)]

where

i is the outcome requested by the participant,

t is a time index counter designating the current transaction,

t−

1 is a time index counter designating the last previous transaction,

Pⁱ_t is the price for the purchase requested by the participant

K=c exp [rj/365],

c is a scaling constant,

r is a constant proportional to the short term annualized interest rate,

j is the relative Julian date since the financial activity was started,

π

ⁱ<sub>t−

1</sub> is the last previously calculated pricing probability for outcome I,

Kπ

ⁱ<sub>t−

1</sub> is the last previously calculated price for outcome i, and

α

_t is a price adjustment parameter, having a value between 0 and 0.1; and

the computer electronically computes a price for the requested financial product, in response to the first request, based at least in part on the first formula.

26. The article of manufacture according to claim 25 wherein said machine readable medium further comprises the following second formula

P^k_t=Kπ

^k_t=K[π

^k<sub>t−

1</sub>(1−

α

_tπ

ⁱ<sub>t−

1</sub>)], k≠

i

where

k, where k≠

i represents the set of all other outcomes,

t is a time index counter designating the current transaction,

t−

1 is a time index counter designating the last previous transaction,

P^k_t where k≠

i is the set of all other prices, updated to take into account the purchase requested by the participant,

π

^k<sub>t−

1</sub> is the latest calculated pricing probability for outcome k.

Kπ

^k<sub>t−

1</sub> is the latest calculated price for outcome k.

K=c exp [rj/365],

c is a scaling constant,

r is a constant proportional to the short term cost of money,

j is the relative Julian date since the financial activity was started, and

α

_t is a price adjustment parameter, having a value between 0 and 0.1; and

the computer electronically automatically updates the prices for all other outcomes, other than the outcome, i, based at least in part on the second formula

27. The article according to claim 26 wherein the machine readable medium further comprises instructions for the computer to store prices corresponding to the possible outcomes in said memory.

28. The article according to claim 27 wherein the machine readable medium further comprises instructions for the computer to store initial prices corresponding to the possible outcomes, prior to receiving an initial request.