Financial activity based on natural peril events

• US 8,266,042 B2
• Filed: 08/06/2008
• Issued: 09/11/2012
• Est. Priority Date: 12/21/2004
• Status: Active Grant

• Current Assignee: Weather RISK Solutions LLC
• Original Assignee: Weather RISK Solutions LLC
• Inventors: Horowitz, Kenneth A., Bequillard, Alfredo L., Nyren, Alexander P., Protter, Philip Elliott, Wilks, Daniel S.
• Primary Examiner(s): Kazimi, Hani M
• Assistant Examiner(s): SCARITO, JOHN D
• Application Number:
  US12/221,935
• Publication Number:
  US 20090024543A1
• Time in Patent Office: 1,497 Days
• Field of Search: 705/35, 705/37
• US Class Current: 705/37
• 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 first participant terminal of a plurality of participant terminals to purchase a financial product for one outcome of the possible outcomes, i; and

electronically computing a price for the 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 designates the one outcome requested by the first participant,

t is a time index counter designating the current purchase requested by the first participant,

t−

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

Pⁱ_t is the price for the financial product,

K=c exp[rj/365],

c is a money amount used as a scaling constant,

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

j is a number of days since the financial activity was started,

π

ⁱ_t is the current pricing probability for outcome i,

π

ⁱ<sub>t−

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

α

_t is a price adjustment parameter, having a value greater than 0 and less than 0.1, and

delivering the price for the financial product of the first request to the first participant terminal.

2. The method according to claim 1, further comprising the step of:

automatically updating prices for all other outcomes of the possible outcomes 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 a set of said all other outcomes of the possible outcomes,

t is the time index counter designating the current purchase requested by the first participant,

t−

1 is the time index counter designating the last previous purchase,

P^k_t where k≠

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

π

^k_t is the current pricing probability for outcome k,

π

^k<sub>t−

1</sub> is the latest computed pricing probability for outcome k,

π

ⁱ<sub>t−

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

K=c exp[rj/365],

c is the money amount used as the scaling constant,

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

j is the number of days since the financial activity was started, and

α

_t is a price adjustment parameter, having a value greater than 0 and less than 0.1.

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

receiving a second request from a second participant terminal of the plurality of participant terminals to purchase a financial product for another outcome of the possible outcomes; and

delivering a price for the financial product of the second request to the second participant terminal after the prices for said all other outcomes of the possible outcomes are automatically updated and the price for the financial product of the second requests is computed based at least in part on the first formula.

4. The method according to claim 1, further comprising the step of:

setting an initial price for each outcome of the possible outcomes, prior to receiving any request.

5. The method according to claim 1, further comprising the step of:

adding a null outcome as an outcome of the possible outcomes, wherein the null outcome is deemed to have occurred when no other possible outcomes have occurred.

6. The method according to claim 2, further comprising the step of:

processing a second request to purchase multiple financial products for a same outcome of the possible outcomes by successively repeating, for each financial product of the requested multiple financial products, the steps of (a) electronically computing a price for said each financial product based at least in part on the first formula and (b) automatically updating prices for all other outcomes of the possible outcomes based at least in part on the second formula.

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

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

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

10. The method according to claim 1, wherein the financial product comprises a contract in a one-sided market of buyer participants.

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

12. The method according to claim 11, wherein the computed and updated 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 first participant terminal of a plurality of participant terminals to purchase a financial product for one outcome of the possible outcomes, i;

electronically computing a price for the financial product 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 designates the one outcome requested by the first participant,

t is a time index counter designating the current purchase requested by the first participant,

t−

1 is a time index counter designating a last previous purchase,

Pⁱ_t is the price for the financial product,

K=c exp[rj/365],

c is a money amount used as a scaling constant,

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

j is a number of days since the financial activity was started,

π

ⁱ_t is the current pricing probability for outcome i,

π

ⁱ<sub>t−

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

α

_t is a price adjustment parameter, having a value greater than 0 and less than 0.1;

automatically updating prices for all other outcomes of the possible outcomes 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 a set of said all other outcomes of the possible outcomes,

t is the time index counter designating the current purchase requested by the first participant,

t−

1 is the time index counter designating the last previous purchase,

P^k_t, where k≠

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

^k_t is the current pricing probability for outcome k, π

^k<sub>t−

1</sub> is the latest computed pricing probability for outcome k, π

ⁱ<sub>t−

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

K=c exp[rj/365],

c is the money amount used as the scaling constant,

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

j is the number of days since the financial activity was started, and

α

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

delivering the price for the financial product of the first request to the first participant terminal.

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

15. The method according to claim 13, wherein the computed and updated 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 of the possible outcomes, prior to receiving any request.

17. The method according to claim 13, further comprising the step of:

adding a null outcome as an outcome of the possible outcomes, wherein the null outcome is deemed to have occurred when no other possible outcomes have occurred.

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

receiving a second request from a second participant terminal of the plurality of participant terminals to purchase a financial product for another outcome of the possible outcomes; and

delivering a price for the financial product of the second request to the second participant terminal after the prices for said all other outcomes of the possible outcomes are automatically updated and the price for the financial product of the second request is computed based at least in part on the first formula.

19. The method according to claim 13, further comprising the step of:

processing a second request to purchase multiple financial products for a same outcome of the possible outcomes by successively repeating, for each financial product of the requested multiple financial products, the steps of (a) electronically computing a price for said each financial product based at least in part on the first formula and (b) automatically updating prices for all other outcomes of the possible outcomes based at least in part on the second formula.

20. The method according to claim 13, wherein the possible 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 configured to receive a first request from a first participant terminal of a plurality of participant terminals to purchase a financial product for one outcome 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>(1−

π

ⁱ<sub>t−

1</sub>)]

where

i designates the one outcome requested by the first participant,

t is a time index counter designating the current purchase requested by the first participant,

t−

1 is a time index counter designating a last previous purchase,

ⁱ_t is the price for the financial product,

K=c exp[rj/365],

c is a money amount used as a scaling constant,

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

j is a number of days since the financial activity was started,

π

ⁱ_t is the current pricing probability for outcome i,

π

ⁱ<sub>t−

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

α

_t is a price adjustment parameter, having a value greater than 0 and less than 0.1; and

wherein the computer executes instructions to electronically compute a price for the financial product, in response to the first request, based at least in part on the first formula and to deliver the price for the financial product of the first request to the first participant terminal.

22. The system 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 a set of all other outcomes of the possible outcomes,

t is the time index counter designating the current purchase requested by the first participant,

t−

1 is the time index counter designating the last previous purchase,

P^k_t, where k≠

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

π

^k_t is the current pricing probability for outcome k,

π

^k<sub>t−

1</sub> is the latest computed pricing probability for outcome k,

π

ⁱ<sub>t−

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

K=c exp[rj/365],

c is the money amount used as the scaling constant,

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

j is the number of days since the financial activity was started, and

α

_t is a price adjustment parameter, having a value greater than 0 and less than 0.1; and

wherein the computer executes further instructions to automatically update prices for said all other outcomes of the possible outcomes based at least in part on the second formula.

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

24. The system according to claim 22, wherein initial prices for each outcome of the possible outcomes are stored in said memory, prior to receiving any request.

25. An article of manufacture including a non-transitory 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, the non-transitory machine readable medium comprising:

instructions to receive a first request from a first participant terminal of a plurality of participant terminals to purchase a financial product for one outcome of the possible outcomes, i;

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 designates the one outcome requested by the first participant,

t is a time index counter designating the current purchase requested by the first participant,

t−

1 is a time index counter designating a last previous purchase,

Pⁱ_t is a price for the financial product,

K=c exp[rj/365],

c is a money amount used as a scaling constant,

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

j is a number of days since the financial activity was started,

π

ⁱ_t is current pricing probability for outcome i,

π

ⁱ<sub>t−

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

α

_t is a price adjustment parameter, having a value greater than 0 and less than 0.1; and

instructions to electronically compute the price for the financial product, in response to the first request, based at least in part on the first formula and to deliver the price for the financial product of the first request to the first participant terminal.

26. The article of manufacture according to claim 25, 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 a set of all other outcomes of the possible outcomes,

t is the time index counter designating the current purchase requested by the first participant,

t−

1 is the time index counter designating the last previous purchase,

P^k_t, where k≠

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

π

^k_t is the current pricing probability for outcome k,

π

^k<sub>t−

1</sub> is the latest computed pricing probability for outcome k,

π

ⁱ<sub>t−

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

K=c exp[rj/365],

c is the money amount used as the scaling constant,

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

j is the number of days since the financial activity was started,

α

_t is a price adjustment parameter, having a value greater than 0 and less than 0.1; and

wherein said non-transitory machine readable medium further comprises instructions to automatically update prices for said all other outcomes of the possible outcomes based at least in part on the second formula.

27. The article of manufacture according to claim 26, wherein the non-transitory machine readable medium further comprises instructions to store the computed and updated prices corresponding to the of possible outcomes.

28. The article of manufacture according to claim 27, wherein the non-transitory machine readable medium further comprises instructions to store initial prices for each outcome of the possible outcomes, prior to receiving any request.