JP2017157033A - Information processing device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a derivative capable of more suitably hedging a business profit risk having no correlation to weather.SOLUTION: A pricing device 10 relating to this invention is an information processing device for pricing a derivative with a population as a target, and includes a contract condition storage part 12 for storing a contract condition of a derivative, a demographic information storage part 11 for storing demographic information, a population index generation part 14 for generating a population index to be used for pricing on the basis of the demographic information, and a premium calculation part 15 for calculating a premium of the derivative on the basis of a fluctuation in the population in a population index relating to the contract condition.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an information processing apparatus that performs derivative pricing.

  A weather derivative pricing system is known as a tool for hedging the risk of diminishing business profits of oneself or business partners due to unseasonable weather such as cold summer, warm winter, and heavy rain, or abnormal weather (see, for example, Patent Document 1). The system calculates premiums for weather derivatives that cover temperature and precipitation.

JP 2002-288437 A

  However, the weather derivative according to the above prior art has the following problems. That is, the weather derivative according to the above-mentioned conventional technology can hedge business profit risk correlated with the weather, but cannot hedge business profit risk that is difficult to correlate with the weather. As an example, with weather derivatives, it is difficult to hedge the risk that business revenue will diminish due to reputational damage in the tourism industry. In light of the above, there is a need for derivatives that can better hedge business revenue risk that has no correlation with the weather.

  The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a derivative capable of more appropriately hedging a business profit risk having no correlation with the weather.

  An information processing apparatus according to an aspect of the present invention is an information processing apparatus that performs derivative pricing for behavior data, and stores a contract condition storage unit that stores a contract condition of the derivative and behavior data statistical information. Based on the behavior data statistical information storage unit, the behavior data index generation unit that generates the behavior data index used for pricing based on the behavior data statistical information, and the behavior data index in the behavior data index related to the contract condition, A premium calculation unit for calculating a premium.

  In this information processing apparatus, pricing of derivatives targeting behavior data is performed. By setting the target of the derivative as action data, it is possible to suitably hedge business profit risk due to a decline in the tourism industry that has no correlation with the weather. In this information processing apparatus, the premium is calculated based on the behavior data fluctuation of the behavior data index related to the contract conditions of the derivative. As a result, it is possible to provide a derivative in which an appropriate premium is set according to changes in behavior data. As described above, it is possible to provide a derivative capable of more appropriately hedging the business profit risk that has no correlation with the weather.

  The information processing apparatus further includes a strike value deriving unit for deriving a strike value that is a reference value for occurrence of payment in the derivative based on the behavior data index, and the behavior data statistical information storage unit includes behavior data statistical information, The first behavioral data statistical information based on the declaration of the derivative contractor and the second behavioral data statistical information not based on the declaration of the derivative contractor are stored. The behavior data index generation unit generates a first behavior data index, which is a behavior data index, based on the first behavior data statistical information, and the second behavior data statistical information. A second behavior data index, which is a behavior data index, is generated based on the data statistical information, and the strike value deriving unit uses the first behavior data index to generate a second row. The data index using the correction information, may derive strike value. Since the strike value of the derivative is a value that is used as a reference value for the occurrence of payment, it is preferably derived from behavior data statistical information that is not based on the declaration of the contractor of the derivative. However, since it is necessary to consider behavioral data statistics over a long period of time in order to obtain an accurate strike value, it has been accumulated by derivative contractors, which are considered to have been accumulated over the longest period. It is preferable to consider behavior data statistical information. From this point of view, this information processing apparatus derives a strike value using information obtained by correcting the second behavior data index by the first behavior data index. As a result, when the strike value is derived, the behavior data statistical information based on the declaration of the derivative contractor (that is, over the long term) Accumulated behavior data statistical information) can be taken into consideration, and a more appropriate strike value can be derived.

  The behavior data statistical information storage unit may store a plurality of types of behavior data statistical information, and the behavior data index generation unit may generate a behavior data index by combining a plurality of types of behavior data statistical information. . By combining multiple behavioral data statistics to generate behavioral data indexes, behavioral data indexes that are difficult to generate from a single behavioral data statistical information and that are highly correlated with business revenue It can be easily generated.

  In addition, the information processing apparatus further includes an occurrence event storage unit that stores details of an event that has occurred, and the contract condition storage unit stores a payment condition as the contract condition, and the content of the event to be paid as the payment condition May be stored. Thereby, payment is performed only when a predetermined event occurs. This makes it possible to calculate a cheaper premium and to calculate a premium according to the needs of the derivative.

  The premium calculation unit may obtain a probability distribution of the behavior data index, and may calculate a premium from the probability distribution by a Monte Carlo method. Thereby, it is possible to derive a more accurate premium reflecting the trend of the behavior data statistical information.

  The behavior data index generation unit may generate the behavior data index by correcting the uncorrected index derived based on the behavior data statistical information according to the temporal change of the index before correction. Thereby, the time-sequential trend of the behavior data statistical information can be reflected, and a more accurate premium can be derived.

  ADVANTAGE OF THE INVENTION According to this invention, the derivative which can hedge more appropriately the business profit risk which does not have correlation with the weather can be provided.

It is a figure which shows the function structure of the pricing apparatus which concerns on 1st Embodiment of this invention. It is a figure which shows the hardware constitutions of the pricing apparatus shown in FIG. It is a figure which shows a demographic information table. It is a figure which shows a contract condition table. It is a figure which shows the table used for the process of a population index production | generation part, Fig.5 (a) is a demographic information table input into a population index production | generation part, FIG.5 (b) is output from a population index production | generation part. Each population index table is shown. It is a figure which shows the table used for the process of a premium calculation part, Fig.6 (a) is a population index table input into a premium calculation part, FIG.6 (b) is the calculated contract conditions output from a premium calculation part. Each table is shown. It is a figure which shows the table used for the process of a premium calculation part, Fig.7 (a) has shown the population index table input into a premium calculation part, FIG.7 (b) has each shown the population matrix. It is a figure which shows the table used for the process of a premium calculation part, Fig.8 (a) has shown the representative value vector, FIG.8 (b) has each shown the population change rate matrix. It is a figure which shows the table used for the process of a premium calculation part, Fig.9 (a) has shown the class vector, FIG.9 (b) has each shown the population change rate frequency distribution table | surface. It is a figure which shows the table used for the process of a premium calculation part, FIG. 10 (a) is a relative frequency distribution table, FIG.10 (b) is a payment amount table | surface, FIG.10 (c) is an expectation value table | surface, respectively. Show. It is a flowchart which shows the pricing process of the pricing apparatus which concerns on 1st Embodiment. It is a figure which shows the function structure of the pricing apparatus which concerns on 2nd Embodiment of this invention. It is a figure which shows the function structure of the pricing apparatus which concerns on 4th Embodiment of this invention. It is a figure which shows an occurrence event table. It is a figure which shows a contract condition table. It is a figure for demonstrating the premium calculation which concerns on 5th Embodiment of this invention. It is a figure for demonstrating the population index production | generation which concerns on 6th Embodiment of this invention.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same reference numerals are used for the same or equivalent elements, and redundant descriptions are omitted.

[First Embodiment]
FIG. 1 is a diagram illustrating a functional configuration of the pricing apparatus according to the first embodiment. The pricing device 10 (information processing device) is a server that performs derivative pricing for behavior data. Behavior data is data indicating behavior, activity, and dynamics. For example, population, traffic volume (number of packets, number of bytes, number of communications, communication time, number of communication terminals), number of base stations, contracts with telecommunications carriers The number of users, the amount of communication consumption (communication cost, content settlement amount), and the like. In the present embodiment, the pricing device 10 will be described as a server that performs pricing of a population derivative that is a derivative targeted at the population. Derivatives in a broad sense refer to financial products derived from original financial products such as bonds and stocks. In the present embodiment, a derivative refers to a financial instrument that trades a right to be paid a compensation amount to a contractor of a derivative when it exceeds or falls below a standard related to an event. An example of such a derivative is a weather derivative for weather fluctuations. In the population derivative of the present embodiment, a standard (strike value) relating to the population is defined, and when the standard is exceeded or below the standard, a compensation amount is paid to the derivative contractor. Pricing a derivative is to calculate the premium of the derivative.

  As shown in FIG. 1, the pricing device 10 includes a demographic information storage unit 11 (behavior data statistical information storage unit), a contract condition storage unit 12, an acquisition unit 13, and a population index generation unit 14 (behavior data index). Generation unit) and a premium calculation unit 15.

  FIG. 2 is a diagram showing a hardware configuration of the pricing apparatus 10 shown in FIG. As shown in FIG. 2, the pricing device 10 physically includes one or more CPUs 101, a RAM 102 and a ROM 103 that are main storage devices, an input device 104 such as a keyboard and a mouse that are input devices, and an output device such as a display. 105, a computer including a communication module 106 which is a data transmission / reception device such as a network card, an auxiliary storage device 107 such as a semiconductor memory, and the like.

  Each function of the pricing device 10 is obtained by reading one or a plurality of predetermined computer software on hardware such as the CPU 101 and the RAM 102 shown in FIG. 2, so that the input device 104, the output device 105, This is realized by operating the communication module 106 and reading and writing data in the RAM 102 and the auxiliary storage device 107.

  Returning to FIG. 1, the demographic information storage unit 11 is a database that stores demographic information. The demographic information is information indicating the population for each predetermined condition. The predetermined condition is uniquely determined by, for example, a target index, a target area, and a target period. The target index is information indicating items to be counted. The target area is information indicating a target area for which the population is counted. The target period is information indicating a target period for which the population is counted. The demographic information storage unit 11 stores a demographic information table 110 (see FIG. 3) as demographic information.

  FIG. 3 is a diagram showing a demographic information table 110 stored in the demographic information storage unit 11. As shown in FIG. 3, in the demographic information table 110, a target index, a target area, a target period, and a population are associated. The demographic information table 110 shown in FIG. 3 indicates that the target index is “the number of monthly guests”, the target area is “Hakone-machi”, and the target period is “June 2015”. Has been. The demographic information storage unit 11 stores a plurality of such demographic information tables 110 for each combination of the target index, the target area, and the target period.

  Returning to FIG. 1, the contract condition storage unit 12 is a database that stores contract conditions of derivatives. Contract terms are information regarding the occurrence of payments entered into with derivative contractors. The contract terms include a strike value, which is the standard value for payments to derivative contractors. More specifically, the contract terms include a target index, a target region, a target period, a strike value, payment terms, and a unit payment amount. The target index is information indicating items for which payment is generated. The target area is information indicating the area where payment is generated. The target period is information indicating a period during which payment is generated. The strike value is information indicating a reference value at which payment occurs. The payment condition is information indicating a condition under which payment occurs. The unit payment amount is an amount paid to the contractor when the payment condition is satisfied and payment is generated. The contract condition storage unit 12 stores a contract condition table 120 (see FIG. 4) for each contract.

  FIG. 4 is a diagram showing the contract condition table 120 stored in the contract condition storage unit 12. As shown in FIG. 4, in the contract condition table 120, a target index, a target region, a target period, a strike value, a payment condition, and a unit payment amount are associated. In the contract condition table 120 shown in FIG. 4, the target index is “number of monthly guests”, the target area is “Hakone-machi”, the target period is “June 2015”, the strike value is “70% compared to the same month last year”, the payment When the condition is “below the strike value”, the unit payment amount is “100,000 yen”. The strike value is not indicated by the ratio of a certain reference value as described above, but may be a fixed value such as “100 people”.

  Returning to FIG. 1, the acquisition unit 13 acquires predetermined demographic information from the demographic information storage unit 11. Specifically, the acquisition unit 13 acquires information of the demographic information table 110 stored in the demographic information storage unit 11. The acquisition unit 13 performs processing when calculating the premium of the derivative, and acquires information in the demographic information table 110, for example, at predetermined time intervals or triggered by an input of an acquisition instruction from the user. The acquisition unit 13 acquires information on the contract condition table 120 stored in the contract condition storage unit 12 and the demographic information table 110 in which the target index, the target area, and the target period match. The acquisition unit 13 outputs the acquired information of the demographic information table 110 to the population index generation unit 14.

  The population index generation unit 14 generates a population index used for pricing based on demographic information. Specifically, the population index generation unit 14 generates a population index from information in the demographic information table 110 input from the acquisition unit 13. The population index generation unit 14 may use the information in the demographic information table 110 input from the acquisition unit 13 as the population index as it is. Alternatively, the population index generation unit 14 may generate a population index by performing predetermined processing on the information in the demographic information table 110 input from the acquisition unit 13. The predetermined processing is, for example, four arithmetic operations.

  An example of generating a population index by four arithmetic operations will be described with reference to FIG. As shown in FIG. 5 (a), the target index is “the number of overnight guests”, the target area is “Hakone-machi”, and the target period is “June 1, 2015” to “June 30, 2015”. Are input to the population index generation unit 14. From these pieces of information, the population index generation unit 14 generates a population index table 141 (FIG. 5B) whose target index is “the number of monthly guests”. Specifically, the population index generation unit 14 adds the populations of the plurality of demographic information tables 110 so that the target index is “the number of monthly guests” as shown in FIG. Is the population index table 141 for “Hakone-machi” and the target period “June 2015”. In the population index table 141, the target index is “monthly number of guests”, the target area is “Hakone-machi”, the target period is “June 2015”, and the population is stored in a plurality of demographic information tables 110. It is said to be “9000” with the population added.

  Note that the population index generation unit 14 may generate a population index by performing predetermined processing other than the four arithmetic operations. For example, the population index generation unit 14 may generate a population index using the median value, average value, maximum value, or minimum value of the population in the demographic information table 110 input from the acquisition unit 13. The population index generation unit 14 outputs information of the generated population index table 141 to the premium calculation unit 15.

  The premium calculation unit 15 applies a contract condition to the past population index generated by the population index generation unit 14 and calculates a premium of the derivative. Specifically, the premium calculation unit 15 calculates a premium based on the population change in the population index related to the contract conditions stored in the contract condition storage unit 12. Based on the information of the population index table 141 input to the population index generation unit 14, the premium calculation unit 15 calculates a premium for contract conditions in which the population index matches the target index, the target area, and the target period. For example, if the information of the population index table 141 shown in FIG. 6A is input to the population index generation unit 14, the premium calculation unit 15 includes the target index (“monthly accommodation” included in the information of the population index table 141. The number of persons ”), the target area (“ Hakone Town ”), the target period (“ June 2015 ”), and the population (“ 9000 people ”) are calculated. Then, as shown in FIG. 6B, for example, the premium calculation unit 15 includes a calculated contract condition table 151 in which premium information is added to information on contract conditions that match the target index, the target area, and the target period. Stored in the contract condition storage unit 12.

  An example of the premium calculation process will be described with reference to FIGS. In an example of the premium calculation process, first, from the population index generation unit 14, the target index is “the number of monthly guests”, the target area is “Hakone-machi”, and the target period is “January 1997” to “2014/12”. Information of the population index table 153 of “Month” is input to the premium calculation unit 15 (FIG. 7A). The information of the population index table 153 can be represented as a population matrix 154 (FIG. 7B) where the vertical axis is the year, the horizontal axis is the month, and the value is the population. In the following description, an example of premium calculation processing related to “December” in the target period will be described.

  The premium calculation unit 15 derives the representative value vector 155 shown in FIG. 8A based on the population matrix 154. In the representative value vector 155, for example, the average value of the population of all the years is obtained every month, and the obtained average value is set as the representative value of the month. Further, the premium calculation unit 15 derives a population change rate matrix 156 based on the population matrix 154 and the representative value vector 155. In the population change rate matrix 156, for each population shown in the population matrix 154, the difference from the representative value is shown in%.

  The premium calculation unit 15 derives the population change rate frequency distribution table 157 (FIG. 9B) based on the population change rate matrix 156 and the class vector 180 (FIG. 9A). The class vector 180 is determined in advance according to the payment condition of the contract condition for calculating the premium. For example, the class vector 180 in FIG. 9A corresponds to the payment condition that payment is generated when the population becomes less than −10% of the strike value. In the class vector 180 of FIG. 9A, “population is −10% or more of the strike value” is set as a class in which payment is not generated, and “population is −10 in strike value” as the class in which payment is generated. “Less than% -20% or more” and “population is less than −20% of the strike value” are set. Such a class vector 180 is set when the payment condition is divided into two stages and the payment amount changes according to the deviation from the strike value. As shown in FIG. 9B, the frequency distribution of the population of each class is shown in the population change rate frequency distribution table 157.

  When calculating the frequency distribution of the population, the premium calculation unit 15 derives the relative frequencies as shown in the relative frequency distribution table 158 of FIG. Then, the premium calculation unit 15 multiplies the relative frequency by the payment amount for each class in the payment amount table 181 shown in FIG. 10B, thereby making the payment shown in the expected value table 159 in FIG. The expected value is derived. As shown in FIG. 10 (c), the expected payment value with a class of −10% or more is relative frequency (15/18) × payment amount (0 yen) = 0 yen. In addition, the expected payment value when the class is less than −10% and −20% or more is relative frequency (1/18) × payment amount (20,000 yen) = 1111 yen. Further, the expected payment value of the class less than −20% is relative frequency (2/18) × payment amount (40,000 yen) = 4444 yen.

  The premium calculation unit 15 derives a value obtained by summing up the expected payment values of each class as the total expected payment value (0 + 1111 + 4444 = 5555 yen). Finally, the premium calculation unit 15 derives a premium by multiplying the total expected payment value by the safety factor and adding the margin. The safety factor here is a predetermined value determined in accordance with the population fluctuation assumed in the derivative for deriving the premium. In addition, the margin is a value determined in consideration of expenses etc. spent on selling derivatives. As described above, the premium calculation unit 15 stores, in the contract condition storage unit 12, the calculated contract condition table 151 in which premium information is added to the contract condition information from which the premium is derived.

  Next, the pricing process of the pricing device 10 will be described. FIG. 11 is a flowchart showing the pricing process of the pricing device 10.

  In the pricing process, first, predetermined demographic information is acquired from the demographic information storage unit 11 by the acquiring unit 13 (step S1). The acquisition unit 13 acquires information in the demographic information table 110 stored in the demographic information storage unit 11 and outputs the information to the population index generation unit 14.

  Subsequently, a population index used for pricing is generated from the information in the demographic information table 110 by the population index generation unit 14 (step S2). The population index generation unit 14 may directly use the information in the demographic information table 110 input from the acquisition unit 13 as a population index, or perform predetermined processing such as four arithmetic operations on the information in the demographic information table 110. A population index may be generated. The population index generation unit 14 outputs information of the generated population index table 141 to the premium calculation unit 15.

  Subsequently, the premium calculation unit 15 calculates the premium of the derivative based on the population change in the population index related to the contract condition stored in the contract condition storage unit 12 (step S3). Based on the information of the population index table 141 input to the population index generation unit 14, the premium calculation unit 15 calculates a premium for contract conditions in which the population index matches the target index, the target area, and the target period. The premium calculation unit 15 stores in the contract condition storage unit 12 a calculated contract condition table 151 in which premium information is added to the contract condition information from which the premium is derived.

  Next, the effect of the pricing device 10 described above will be described.

  The pricing device 10 is an information processing device that performs derivative pricing for a population, and includes a contract condition storage unit 12 that stores contract conditions for derivatives, a demographic information storage unit 11 that stores demographic information, A population index generation unit 14 that generates a population index used for pricing based on demographic information, and a premium calculation unit 15 that calculates a premium of a derivative based on a population change in the population index related to the contract condition. .

  In the pricing device 10, pricing of derivatives for the population is performed. By setting the target of the derivative as the population, it is possible to suitably hedge the business profit risk of population fluctuations due to a decline in the tourism industry that has no correlation with the weather. In the pricing device 10, the premium is calculated based on the population fluctuation of the population index related to the contract conditions of the derivative. As a result, it is possible to provide a derivative with an appropriate premium set according to population changes. As described above, it is possible to provide a derivative capable of more appropriately hedging the business profit risk that has no correlation with the weather.

[Second Embodiment]
Next, with reference to FIG. 12, the pricing apparatus 10A according to the second embodiment will be described. In the description of the present embodiment, differences from the first embodiment will be mainly described.

  The pricing device 10 </ b> A includes a strike value deriving unit 16 in addition to the components of the pricing device 10 described above. The strike value deriving unit 16 is configured to derive a strike value, which is a reference value for generating payment in the derivative, based on the population index.

  In the present embodiment, the demographic information storage unit 11 uses, as demographic information, report information (first demographic information) and non-report information (second demographic information) in which the target index and the target area are the same. ) Is remembered. The report information is demographic information (based on the report) based on the report of the derivative contractor. Non-declared information is demographic information that is not based on the declaration of the contractor of the derivative (does not depend on the declaration). The demographic information based on the report of the contractor is, for example, tourism statistics counted by the tourism association when the contractor is a tourist association in Hakone. As another example, the demographic information based on the contractor's declaration is tourism statistics that the accommodation facility may be involved in when the contractor is an accommodation facility in Hakone. The demographic information that is not based on the declaration of the contractor is, for example, demographic information that is uniquely counted by an insurance company that is a derivative seller or a communication carrier that provides a pricing device. More specifically, the demographic statistics uniquely counted by the telecommunications carrier are, for example, mobile space statistics using a mobile phone network mechanism provided by the telecommunications carrier.

  The declaration information includes data acquired by the contractor before the derivative contract was concluded with the contractor. On the other hand, the non-declared information is, for example, data that has started to be acquired on the insurance company side (or on the telecommunications carrier side) after the derivative contract is concluded with the contractor. For this reason, the reporting information includes data for a longer period than the non-reporting information.

  The population index generation unit 14 generates a population index based on each demographic information. That is, the population index generation unit 14 generates a reported population index (first population index) based on the reporting information, and generates a non-reported population index (second population index) based on the non-reported information.

  The strike value deriving unit 16 derives a strike value using information obtained by correcting the non-reported population index by the reported population index. Since the strike value of the derivative is a value that is used as a reference value for generating payment, it is preferable that the derivative is derived by the non-declared population index rather than the declared population index that can manipulate information at the discretion of the contractor. However, as described above, since non-declared information is data obtained after a derivative contract is concluded, it may be difficult to derive an appropriate strike value from only the non-declared population index. That is, for example, when the strike value is “a value that is 70% of the representative value”, the representative value cannot be appropriately derived only by short-term data, and therefore, there is a possibility that an appropriate strike value cannot be derived.

In view of this point, the strike value deriving unit 16 derives a strike value using information obtained by correcting the non-reported population index by the reported population index. Specifically, the strike value deriving unit 16 derives a representative value of the non-declared population index from the population on a predetermined day in the non-declared population index, and the population and representative value on the predetermined day of the declared population index. The strike value is derived from the representative value of the reporting index. Since population changes on the same day are considered to be approximately the same in the two population indices, the following relationship holds.
Representative value of declared population index: Population on a given day of declared population index = Representative value of non-declared population index: Population on a given day of non-declared population index (1)

  From the above formula (1), if the representative value of the declared population index, the population on the prescribed date of the declared population index, and the population on the prescribed date of the non-reported population index are known, the representative value of the non-reported population index can be derived. . The strike value deriving unit 16 derives, for example, a value that is 70% of the representative value of the non-reported population index derived as described above as a strike value. The strike value deriving unit 16 stores the derived strike value in the contract condition of the contract condition storage unit 12.

  As described above, by using the non-declared population index that is not based on the declaration of the contractor of the derivative by deriving the strike value using the information obtained by correcting the non-declared population index by the declared population index, An appropriate strike value can be derived in consideration of the information on the reported population index from which the value can be appropriately derived.

[Third Embodiment]
Next, a pricing device according to the third embodiment will be described. In the description of this embodiment, differences from the first embodiment and the second embodiment will be mainly described.

  In the present embodiment, the demographic information storage unit 11 stores a plurality of types of demographic information. Here, the plural types of demographic information are not demographic information having the same target index and target area as described in the second embodiment, but a plurality of demographic information having at least different target indices. . That is, the demographic information storage unit 11 stores, for example, demographic information whose target index is “the number of guests” and demographic information whose target index is “the number of tourists” for the same target area. Yes.

  And the population index production | generation part 14 produces | generates a population index by combining several types of demographic information. Specifically, combining means performing four arithmetic operations using a plurality of demographic information. In the above example, for example, the population index generation unit 14 divides the population of demographic information whose target index is “the number of guests” by the population of demographic information whose target index is “the number of tourists”. Thus, a population index indicating an index corresponding to the accommodation rate is generated.

  As another example, for example, by subtracting the population of demographic information whose target index is “resident” from the population of demographic information whose target index is “total population”, it correlates with business revenue in the tourism sector. A population index showing an index equivalent to the number of tourists can be generated.

  In this way, by combining multiple demographic information to generate a population index, it is also easy for a population index that is difficult to generate from a single demographic information and has a high correlation with business revenue Can be generated.

[Fourth Embodiment]
Next, a pricing device 10B according to the fourth embodiment will be described with reference to FIGS. In the description of the present embodiment, differences from the first to third embodiments will be mainly described.

  The pricing device 10B includes an occurrence event storage unit 17 in addition to the components of the pricing device 10 described above. The occurrence event storage unit 17 is a database that stores the contents of an event that has occurred. Events that have occurred include, for example, natural disasters, incidents, accidents, infectious diseases, terrorism, and urban development. The generated event storage unit 17 stores the contents of the generated event in the generated event table 170 (see FIG. 14).

  FIG. 14 is a diagram illustrating an example of the occurrence event table 170. As shown in FIG. 14, in the occurrence event table 170, the occurrence time, the occurrence area, and the occurrence event are associated with each other. The occurrence event table 170 shown in FIG. 14 indicates that the occurrence time is “May 6, 2015”, the occurrence area is “Hakone-machi”, and the occurrence event is “Turn peripheral warning announcement”. The occurrence event storage unit 17 stores a plurality of such occurrence event tables 170 for each combination of occurrence time, occurrence region, and occurrence event. The occurrence event table 170 may be written manually, for example, or automatically when a disaster occurs from a server or the like that manages the disaster.

  The contract condition storage unit 12 stores the contents of the payment target event as the payment condition of the contract condition. For example, the contract condition storage unit 12 stores a contract condition table 125 shown in FIG. In the contract condition table 125, the payment condition is “when the value falls below the strike value within 6 months after the crater vicinity warning issuance”.

  As described above, the occurrence event is stored in the occurrence event storage unit 17, and the contents of the event to be paid are stored as the payment conditions in the contract condition storage unit 12. It is possible to process that payment is performed only when the generated event is a predetermined event. For example, by setting the payment to be “only for six months after the crater vicinity warning issuance”, payment associated with other events (for example, damage to the reputation of an earthquake) becomes unnecessary. This lowers the expected value in the premium calculation, so it is possible to derive a cheaper premium, and to respond to various derivative needs such as limiting the situation where payments are made and making the premium cheaper. It becomes possible.

[Fifth Embodiment]
Next, a pricing device according to the fifth embodiment will be described with reference to FIG. In the description of the present embodiment, differences from the first to fourth embodiments will be mainly described.

  In the present embodiment, the premium calculation unit 15 obtains a probability distribution of the population index, and calculates a premium from the probability distribution by the Monte Carlo method. The premium calculation unit 15 applies a probability distribution such as a normal distribution, an exponential distribution, a Poisson distribution, a gamma distribution, and a beta distribution to the population index input from the population index generation unit 14 to obtain a probability distribution of the population index (see FIG. 16). Thereafter, the premium calculation unit 15 calculates the expected payment value of the derivative by performing a simulation using a random number by the Monte Carlo method based on the probability distribution (for example, 10,000 times), and considers the safety factor and the margin. And calculate the premium.

  As described above, the premium calculation unit 15 calculates a premium from the probability distribution of the population index by the Monte Carlo method, so that a more accurate premium reflecting the trend of the demographic information can be calculated.

[Sixth Embodiment]
Next, a pricing device according to the sixth embodiment will be described with reference to FIG. In the description of this embodiment, differences from the first to fifth embodiments will be mainly described.

  In the present embodiment, the population index generation unit 14 generates a population index by correcting the uncorrected index derived based on the demographic information according to the temporal change of the uncorrected index. As shown in FIG. 17A, the population index generation unit 14 arranges the uncorrected index, which is a population index before correction, in time series based on the demographic information, and applies a linear regression model. In the example shown in FIG. 17 (a), when the horizontal axis is the time axis and the vertical axis is the population index (specifically, the population index before correction), a linear regression model is applied and a straight line y = 0.5x + 10 is obtained. Has been derived.

  The population index generation unit 14 corrects the straight line derived by applying the linear regression model so that the slope of the straight line becomes zero (see FIG. 17B). That is, assuming that time t = 2 (that is, x = 2), x = 2 is assigned to the above-described straight line y = 0.5x + 10, the straight line is set to y = 11, and the slope becomes zero. The value of the index before correction is corrected. The corrected value is used as a population index.

  Thus, with respect to a straight line derived by applying a linear regression model, demographic information is obtained by correcting the value of the index before correction so that the slope is zero with the value of y as the current value (time t = 2). It is possible to derive a population index that reflects the time-series trend of, and to derive a more accurate premium. In other words, for example, when the number of tourists increases year by year in tourist spots where theme parks that grow year by year are invited, the population index can be derived in consideration of the population increase. A more appropriate premium can be derived.

  As mentioned above, although embodiment of this invention was described, this invention is not limited to the said embodiment. For example, although the pricing device 10 has been described as a single information processing device, it may be configured by combining a plurality of servers.

  Moreover, although the function for calculating a premium was demonstrated as a function of the pricing apparatus 10, the pricing apparatus 10 may have a function which judges the presence or absence of payment generation | occurrence | production.

  Moreover, although the pricing apparatus 10 was demonstrated as a server which performs the pricing of the derivative for population, it is not limited to this. That is, the pricing device 10 is traffic data other than the population, such as traffic volume (number of packets, number of bytes, number of communication, communication time, number of communication terminals), number of base stations, number of subscribers with communication carriers, or communication. Pricing of derivatives targeting the consumption amount (communication cost, content settlement amount) related to.

  DESCRIPTION OF SYMBOLS 10,10A, 10B ... Pricing apparatus, 11 ... Demographic information storage part, 12 ... Contract condition storage part, 14 ... Population index production | generation part, 15 ... Premium calculation part, 16 ... Strike value derivation part, 17 ... Occurrence event storage part .

Claims (6)

An information processing device for pricing derivatives for behavior data,
A contract condition storage unit for storing the contract conditions of the derivative;
A behavior data statistical information storage unit for storing behavior data statistical information;
Based on the behavior data statistical information, a behavior data index generation unit that generates a behavior data index used for pricing,
An information processing apparatus, comprising: a premium calculation unit that calculates a premium of the derivative based on a change in behavior data in the behavior data index related to the contract condition. A strike value deriving unit for deriving a strike value, which is a reference value for generating payment in the derivative, based on the behavior data index;
The behavior data statistical information storage unit
As the behavior data statistical information, the first behavior data statistical information based on the declaration of the derivative contractor and the second behavior data statistical information not based on the declaration of the derivative contractor are stored.
The first behavior data statistical information is longer-term data than the second behavior data statistical information,
The behavior data index generation unit
A first behavior data index that is the behavior data index is generated based on the first behavior data statistical information, and a second behavior data index that is the behavior data index is generated based on the second behavior data statistical information. And
The strike value deriving unit
The information processing apparatus according to claim 1, wherein the strike value is derived using information obtained by correcting the second behavior data index by the first behavior data index. The behavior data statistical information storage unit stores a plurality of types of behavior data statistical information,
The information processing apparatus according to claim 1, wherein the behavior data index generation unit generates the behavior data index by combining a plurality of types of behavior data statistical information. An event storage unit that stores the contents of the event that has occurred;
The information processing apparatus according to any one of claims 1 to 3, wherein the contract condition storage unit stores a payment condition as the contract condition, and stores contents of an event to be paid as the payment condition. .   The information processing apparatus according to any one of claims 1 to 4, wherein the premium calculation unit calculates a probability distribution of the behavior data index and calculates the premium from the probability distribution by a Monte Carlo method.   The behavior data index generation unit generates the behavior data index by correcting an uncorrected index derived based on the behavior data statistical information according to a temporal change of the uncorrected index. Information processing apparatus as described in any one of -5.

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