JP6002588B2 - Curve estimation method and apparatus - Google Patents

Description

  The present technology relates to a technology for estimating a curve representing a relationship between a spot rate and a remaining period.

  The spot rate (SR) is an average yield according to a period (remaining period) until the maturity date, which is currently predicted by the market. Strictly speaking, this is the average yield of bonds (discounted bonds) without interest payments until the maturity date. Generally, it is calculated from the price of government bonds without default risk.

  However, in the case of Japan, a government bond is an interest-bearing bond that has multiple interest payments until the maturity date, so it is not possible to obtain a spot rate directly from a government bond. Therefore, the interest rate bond is divided into the interest and the principal part, and the spot rate is calculated on the assumption that the bond is a combination of bonds that mature at the time of each payment and do not pay interest on the way.

  There are approximately 300 Japanese government bonds, with a maximum period of 40 years. A problem is that the number of issues of government bonds (for example, 30 to 40 years) with a long period until maturity is small. Interest and redemption amounts are determined at the time of issuance and are not changed midway.

FIG. 1 schematically shows interest payment and redemption of a bond of issue α and calculation of present value. For example, every half year, a constant interest c _α continues L _α times until the maturity date, and the redemption amount R _α is also paid on the maturity date. More specifically, the interest payment amount c _α is paid from the first interest payment to L _α −1, and c _α + R _α is paid at the last L _α time.

On the other hand, the present value of these payment amounts is a value obtained by multiplying the value shown at the bottom of each payment period. Note that r (t) represents the spot rate after time t, and in the case of time t, the discount rate 1 / (1 + r (t)) ^t is multiplied. In other words, the present value increased by compound interest is considered as the future value, and the present value is calculated backward from the future value. Note that t _k ^α represents a period until the k-th payment time of the brand α.

Assuming that the market value of government bonds = the sum of the present value of interest and principal, the market price p _α is expressed as follows.

  Further, the relationship between the discount rate d (t) and the spot rate is defined as follows. That is, discount rate d (t) = ratio between present value and future value. Therefore, it is specifically shown as follows.

  Conventionally, the spot rate is calculated by the following procedure. That is, transaction data of government bonds is collected, and a discount rate is calculated based on a statistical model of market prices of government bonds. The statistical model is represented by the following formula.

なお、ε_αは正規分布Ｎ（０，σ²）の誤差を表している。また、Ｎは銘柄数を表す。

Note that ε _α represents an error of the normal distribution N (0, σ ² ). N represents the number of brands.

  However, as mentioned above, Japanese government bonds have a small number of issues with a long remaining period. FIG. 2 shows the relationship between the remaining period of government bonds and the price of government bonds. As can be seen from the part surrounded by the dotted line, the number of samples decreases for the part with a longer remaining period. For this reason, if a curve (that is, a yield curve) is drawn based on these data, the curve will meander unnaturally in a portion where the remaining period is long as shown in FIG. A yield curve (also called a spot rate curve) is a curve representing the relationship between the remaining period and the spot rate.

Kawasaki Noritori, Yasuchi Tomohiro, Yield Curve Estimation by Regularized Nonlinear Regression Model, Statistical Mathematics, Vol. 50, No. 2, 149-164, 2002 Mizuho Report “Understanding the Structure of Government Bond Spot Rate Curves by Principal Component Analysis and Examining its Predictability: Using a Common Factor Model Based on Macroeconomic and Financial Variables” published on September 21, 2010, Mizuho Research Institute Konishi Sadanori, Kitagawa Genshiro, Information Criterion, Asakura Shoten, 2004, pages 80-106 Genshiro Kitagawa and Sadanori Konishi, “Generalized Information Criterion GIC and Bootstrap”, Statistical Mathematics, Vol. 47, No. 2, pp.375-394, 1999.

  Therefore, an object of the present technology is to provide a technology for enabling a stable and appropriate yield curve to be drawn from interest-bearing bond data as one aspect.

  The curve estimation method according to one aspect of the present technology includes: (A) one issue per maturity date from a data storage unit that stores the maturity date, interest, redemption amount, and market price including paid interest for each issue of interest-bearing bonds. (B) Generate a matrix that lists the payment amount at each payment period for each extracted interest-bearing bond issue from the maturity date, interest and redemption amount for the extracted interest-bearing bond issue (C) Converting the generated matrix into a regular matrix, and discounting the maturity date for each of the extracted interest-bearing bond issues from the extracted matrix including the market price for each interest-bearing bond issue and the regular matrix Execute the processing to calculate the rate, (D) calculate the spot rate at the maturity date for each of the extracted interest-bearing bonds from the discount rate at the calculated maturity date, and (E) at the calculated maturity date Time from spot rate It includes a process for calculating the curve. The parameter group of the regression model used for calculating the regression curve described above indicates that the index value indicating the stability of the regression curve is stable at a predetermined level or more and is related to the parameter group. Is calculated so that the value of the generalized information criterion specified in the above is minimized.

  A stable and appropriate yield curve can be drawn from interest-bearing bond data.

FIG. 1 is a diagram for explaining interest payment and redemption of government bonds and calculation of present value. FIG. 2 is a diagram schematically showing the relationship between the remaining period of government bonds and the price. FIG. 3 is a diagram showing an example of a yield curve drawn in the prior art. FIG. 4 is a functional block diagram of the information processing apparatus according to the embodiment of the present technology. FIG. 5 is a diagram illustrating an example of data stored in the first data storage unit. FIG. 6 is a diagram showing a main processing flow according to the present embodiment. FIG. 7 is a diagram illustrating a processing flow of the filtering process. FIG. 8 is a diagram illustrating an example of a cash flow matrix. FIG. 9 is a diagram showing a processing flow of discount rate calculation processing. FIG. 10 is a diagram for explaining a conversion process into a regular matrix. FIG. 11 is a diagram showing an example of government bond compound yield data. FIG. 12 is a diagram illustrating an example of a discount rate curve. FIG. 13 is a diagram illustrating an example of a regular matrix. FIG. 14A is a diagram illustrating a numerical example of the spot rate. FIG. 14B is a diagram illustrating an example of a spot rate plot. FIG. 15 is a diagram for explaining the center value and spread of the basis function used in the regression model. FIG. 16 is a diagram illustrating the relationship between s ² _k and μ _k . FIG. 17 is a diagram illustrating a process flow of the curve estimation process. FIG. 18 is a diagram illustrating an example of output data. FIG. 19 is a diagram illustrating another example of output data. FIG. 20 is a diagram for explaining another example of the curve estimation process. FIG. 21 is a diagram illustrating another process flow of the curve estimation process. FIG. 22 is a diagram illustrating a processing flow of stability index value calculation processing. FIG. 23 is a diagram for describing stability index value calculation processing. FIG. 24 is a diagram for describing stability index value calculation processing. FIG. 25 is a diagram for describing stability index value calculation processing. FIG. 26 is a diagram showing still another processing flow of the curve estimation processing. FIG. 27 is a functional block diagram of a computer.

  FIG. 4 is a functional block diagram of the information processing apparatus 100 that estimates the yield curve according to the present embodiment. The information processing apparatus 100 includes a first data storage unit 101, an extraction unit 102, a second data storage unit 103, a matrix generation unit 104, a third data storage unit 105, a discount rate calculation unit 106, and a fourth The data storage unit 113, the fifth data storage unit 107, the spot rate calculation unit 108, the sixth data storage unit 109, the curve estimation unit 110, the seventh data storage unit 111, and the output unit 112 are included.

  The first data storage unit 101 stores, for example, government bond data as shown in FIG. In the example of FIG. 5, data such as a brand identifier, maturity date, interest (or interest rate), redemption amount, payment interval, market price, transaction volume, and bidding date are stored for each brand of the government bond. The market price is an amount obtained by adding accrued interest (paid interest) to the market price. In some cases, the number of transactions is also stored.

  The extraction unit 102 extracts data used in the following processing from the government bond data stored in the first data storage unit 101 and stores the data in the second data storage unit 103. The matrix generation unit 104 generates a cash flow matrix described below from the government bond data stored in the second data storage unit 103 and stores it in the third data storage unit 105.

  The discount rate calculation unit 106 includes a regular conversion unit 1061, and uses the data stored in the second data storage unit 103 and the fourth data storage unit 113 to store the cache stored in the third data storage unit 105. The flow matrix is converted into a regular matrix and a discount rate matrix is calculated and stored in the fifth data storage unit 107. The fourth data storage unit 113 stores data for calculating a forward discount factor, such as government bond compound yield data.

  The spot rate calculation unit 108 calculates the spot rate at each maturity date from the discount rate matrix stored in the fifth data storage unit 107 using the data stored in the second data storage unit 103, and calculates The spot rate thus obtained is stored in the sixth data storage unit 109. The curve estimation unit 110 uses the data stored in the second data storage unit 103 to execute a process of estimating a yield curve from the spot rate stored in the sixth data storage unit 109, and displays the processing result. Stored in the seventh data storage unit 111. The output unit 112 outputs the yield curve data to an output device (display device, printing device, other computer connected via a network, etc.).

  Next, processing contents of the information processing apparatus 100 will be described with reference to FIGS. 6 to 19.

  First, the extraction unit 102 performs a filtering process of extracting one brand of government bonds for each maturity date or generating one brand of virtual government bonds from the government bond data stored in the first data storage unit 101, The processing result is stored in the second data storage unit 103 (step S1). For example, a processing flow as shown in FIG. 7 is performed.

  First, the extraction unit 102 sorts the government bond data in the order of early maturity dates (step S21). And the extraction part 102 groups for every government bond of the same maturity date (step S23). Thereafter, the extraction unit 102 selects one brand of government bonds for each group according to the rules described below, or generates one brand of virtual government bonds as described below, and is selected or generated. The government bond data is stored in the second data storage unit 103 (step S25).

  For step S25, various variations are conceivable. As a first variation, one issue of a government bond with a maximum transaction volume is selected. This is because it is estimated that the larger the transaction volume, the smaller the error from the appropriate price. In addition, as a second variation, one issue is selected for a government bond with the latest bid date. This is also because the fluidity is generally high.

Furthermore, as a third variation, for a market price, redemption amount, and interest of a government bond with the same maturity date, one issue of a virtual government bond is generated by a weighted sum. The transaction amount is used for the weight. For example, if the weight of the brand i is a _i , the market price of the brand i is p _i , the redemption amount of the brand i is R _i, and the interest of the brand i is c _i , the following is expressed.

ｐは仮想的な国債の市場価格を表し、ｃは仮想的な国債の利子を表し、Ｒは仮想的な国債の償還額を表している。

p represents the market price of the virtual government bond, c represents the interest of the virtual government bond, and R represents the redemption amount of the virtual government bond.

  As for the first to third variations, one kind of government bond is extracted for government bonds that exceed the threshold for at least one of the transaction volume, the number of transactions, and the remaining period (period until the maturity date). Also good. For example, those with a remaining period of less than 2 months are excluded, or those with a transaction volume of less than a threshold are excluded.

In addition, as a fourth variation, sorting may be performed according to the following procedure.
(1) Limit the types of government bonds to be selected for each remaining period zone.
If the remaining period is 2 years or less, only the 2-year bond is selected. If the remaining period is 2 years or more and 5 years or less, only the 5-year bond is selected. If the remaining period is 5 years or more and 10 years or less, the 10-year bond If only the remaining option period exceeds 10 years and is 20 years or less, only the 20-year bond period is longer than 20 years and if it is 30 years or less, only the 30-year bond period is longer than 30 years and less than 40 years. If so, only 40-year bonds are selected. Japanese government bonds are not newly issued, so if there are no 5-year bonds, 10-year bonds may be used instead. Also, 2-year bonds with less than 30 days remaining are excluded.
(2) After limiting as in (1), if a plurality of issues of government bonds remain for the same maturity date, the issue of the latest bid date is selected.

  Returning to the description of the processing in FIG. 6, the matrix generation unit 104 generates a cash flow matrix (CF matrix) for the government bonds included in the filtering processing result stored in the second data storage unit 103, and generates third data Store in the storage unit 105 (step S3).

  Each row of the cash flow matrix corresponds to one issue of government bonds, and each column corresponds to the payment period of either interest or redemption amount (principal). In the cash flow matrix ij, a payment amount (interest or interest and redemption amount) to be paid for the issue i of the government bond at the payment time j is set.

For example, a cash flow matrix as shown in FIG. 8 is generated. The example of FIG. 8 shows an example in which N issues of government bonds are extracted, and these N issues of government bonds have M payment periods in total. For example, interest payments on issue N government bonds are made at payment periods t ₁ and t _M-3 , and interest and redemption payments on issue N government bonds are made at payment time t _M. . For other payment periods, “0” is set because there is no payment for the national bond of the issue N. The same applies to other brands.

The payment time t _i is calculated by maturity date−payment interval × n (n is a natural number) for each issue of the government bond. However, the calculation is stopped when the payment time t _i becomes a negative value. In other words, starting from the maturity date, the payment date interval is traced back to the present. The payment amount is the interest of each issue itself, or the interest plus the redemption amount at the maturity date. In addition, since it is used in the processing described below, t _{1 to} t _M are also stored in the third data storage unit 105. However, the method is not limited to this method, and a method of storing the interest payment date for each brand in a database or the like may be adopted.

  Next, the discount rate calculation unit 106 executes a discount rate calculation process for calculating the discount rate at each maturity date from the cash flow matrix stored in the third data storage unit 105, and stores the processing result in the fifth data storage. The data is stored in the unit 107 (step S5). The discount rate calculation process will be described with reference to FIGS. 9 to 13.

  First, the regular conversion unit 1061 of the discount rate calculation unit 106 converts the cash flow matrix into a regular matrix using the initial forward discount factor (FD) (step S31).

In this step, first of all, in column j (j = 1, 2,..., M) of the cash flow matrix, the corresponding payment time t _j is the maturity of the issue i (i = 1,..., N). Day T = {T _i | i = 1,. . . , N} is specified. In the example schematically shown in FIG. 10, t ₁ , t ₂ , and t ₅ are identified because they are not maturity dates.

Then, the column identified in this way is added to the column of the maturity date immediately after taking the forward discount factor D into consideration. For the columns t ₁ and t ₂ , add to the column of t ₃ = T ₁ , and for the column of t ₅ add to the column of t ₆ = T ₃ .

  Further, the inverse value 1 / D (t, T) of the forward discount factor is multiplied by the column of t and added to the column of T so as to approximately store the present value of the cash flow at the payment time t. . D (t, T) represents a forward discount factor from the payment time t to the payment time T.

  The initial forward discount factor is calculated, for example, from government bond compound yield data stored in the fourth data storage unit 113. The government bond compound yield data is, for example, data as shown in FIG. 11, and the discount rate is calculated from the government bond compound yield using the above-described equation (1). Then, the forward discount factor D is calculated from the discount rate by the following formula.

ｄ（τ_t）は、現時点から時点ｔまでの期間τ_tについての割引率である。同様に、ｄ（τ_T）は、現時点から時点Ｔまでの期間τ_Tについての割引率である。

d (τ _t ) is a discount rate for the period τ _t from the present time to the time t. Similarly, d (τ _T ) is a discount rate for the period τ _T from the current time point to the time point T.

However, as shown in FIG. 11, since only a discrete discount rate can be obtained, the forward discount factor D (t ₁ , t ₂ ) used to convert the cash flow matrix described above into a regular matrix is used. In some cases, d (τ _t1 ) and d (τ _t2 ) may not be directly calculated. In such a case, the current discount rate is set to “1” in addition to the calculated discount rate at each time point, and a discount rate curve is drawn by an interpolation method such as cubic spline interpolation, for example, and τ _t1 and discount rate obtain d (tau _t1) and d (tau _t2) for tau _t2.

The discount rate curve is a curve representing a monotone decreasing function as shown in FIG. 12, for example. FIG. 12 shows only the points plotted from the multi-yield data for government bonds, as shown in FIG. 12, which shows the change in the discount rate from the remaining period “0” (ie, the current time) to the longer remaining period. The value between is calculated by interpolation calculation. In FIG. 12, the discount rate for the period τ _t from the current time corresponding to the time point t is d (τ _t ), and the discount rate for the time period τ _T from the current time point T is d (τ _T ).

In the example of FIG. 10, for the row of t _1, and adds the multiplied by _{1 / D (t 1, t} 3 = T 1) = d (τ t1) / d (τ t3) to a column of T _1. Furthermore, for the columns of t _2, and adds the multiplied by _{1 / D (t 2, t} 3) = d (τ t2) / d (τ t3) to the columns of T _3. For the column of t ₅ , 1 / D (t ₅ , t ₆ = T ₃ ) = d (τ _t5 ) / d (τ _t6 ) is multiplied and added to the column of T ₃ .

  If a column other than the maturity date is deleted, a regular matrix of N rows and N columns as shown in FIG. 13 is obtained from the cash flow matrix as shown in FIG. In other words, the payment timing of the extracted issues of government bonds is converted into a cash flow matrix that is aggregated on the maturity date of any of these issues of government bonds.

  Since the initial forward discount factor is a temporary value, it is not necessary to use compound yield data on government bonds. For example, an arbitrary curve that decreases monotonically from 1 as schematically shown in FIG. 12 may be prepared and used.

  Returning to the description of the processing in FIG. 9, the discount rate calculation unit 106 calculates a discount rate matrix from the regular matrix and stores it in the fifth data storage unit 107 (step S33).

The price of a government bond is a value obtained by discounting each cash flow by a discount rate and adding up the discount rate matrix d based on the following relationship. It is assumed that the regular matrix is C.
Cd = p
d = (d ₁ , d ₂ ,..., d _N ) ^T
d _k is a discount rate at time T _k .
p = (p ₁ , p ₂ ,..., p _N ) ^T
p _k is the market price at time T _k . Such data is stored in the third data storage unit 105.

Therefore, the discount rate matrix d can be obtained by modifying as follows.
d = C ^-1 p

  Then, the discount rate calculation unit 106 determines whether the difference between the previous discount rate matrix d ′ stored in the fifth data storage unit 107 and the currently calculated discount rate matrix d is within an allowable range. (Step S35). The requirement is that the absolute value of the difference in discount rates at any maturity date is less than the threshold. The first time does not satisfy this condition. If this condition is satisfied, the process returns to the caller process.

  On the other hand, when this condition is not satisfied, the regular conversion unit 1061 converts the cash flow matrix into a regular matrix using the forward discount factor calculated from the discount rate matrix calculated in step S33 (step S37). ). The conversion method is the same as in step S31, but only the value of the forward discount factor used is different. Then, the process returns to step S33.

  In this way, steps S33 to S37 are repeated until the discount rate converges. That is, the regular matrix and the discount rate matrix are reset until the market price is correctly calculated.

  Returning to the description of the processing in FIG. 6, the spot rate calculation unit 108 uses the data stored in the second data storage unit 103 to calculate the government bond from the discount rate matrix stored in the fifth data storage unit 107. The spot rate for the maturity date of each brand is calculated and stored in the sixth data storage unit 109 (step S7).

  Similar to the equation (2) described above, it is calculated by the following equation.

但し、ｒ_iは満期日Ｔ_iにおけるスポットレートである。またｄ_iはＴ_iにおける割引率を表す。

Here, r _i is the spot rate on the maturity date T _i . D _i represents a discount rate at T _i .

The maturity date T _i can be converted into a remaining period from the present. Then, for example, data as shown in FIG. 14A is obtained. In the example of FIG. 14A, data of the remaining period and the spot rate (SR) are included. If the data of FIG. 14A is made into a graph, it will become like FIG. 14B. In the example of FIG. 14B, the horizontal axis represents the remaining period, and the vertical axis represents the spot rate. Thus, it can be seen that spot rates are not calculated for some remaining periods, particularly between 30 and 40 years. In the state where the spot rate is not calculated for 30 to 40 years, generally, the regression curve becomes unstable in the section where there is no data. However, in this embodiment, the data is in such a state. However, a stable curve can be calculated by the regression calculation described below.

  Then, the curve estimation unit 110 executes curve estimation processing on the spot rate stored in the sixth data storage unit 109, and stores the processing result in the seventh data storage unit 111 (step S9). This curve estimation process will be described with reference to FIGS.

In this embodiment, for example, the yield curve is estimated using a regression model including a weighted sum of Gaussian basis functions. More specifically, the spot rate r _α after the period T _α is represented by the following regression model.

ε _α represents an error of the normal distribution N (0, σ ² ). σ ² represents the error variance. W _k represents the weight of the k th basis function. Furthermore, Equation (4) represents a Gaussian basis function. In Equation (4), μ _k represents the center value of the kth basis function, and ν controls the spread of the basis function. It is a parameter for. s ² _k is a parameter representing the spread of the kth basis function.

In the present embodiment, the center value of the basis function for k = 1 is set to a period that is 10 years shorter than the minimum value T ₁ of the remaining period, and the center value of the basis function for k = m is set by Equation (5). The period is set to a period 10 years longer than the maximum value T _N of the remaining period. This is schematically shown in FIG. In this way, a stable yield curve can be obtained by expanding the domain of the basis function and sufficiently satisfying the range of the remaining period with the basis function. In particular, the basis function for k = 1 and the basis function for k = m, where the center value is set outside the range from the minimum value T ₁ to the maximum value T _N of the remaining period, the distortion of other basis functions Compensation can be performed. Note that 10 years is an example, and different values may be set depending on circumstances.

Further, the center value of each basis function is arranged at a position obtained by equally dividing the range from a period 10 years shorter than the minimum value T _{1 of the} remaining period to a period 10 years longer than the maximum value T _{N of the} remaining period. However, m is set in advance or determined by the following processing.

Furthermore, the expression (6) indicates that the spread becomes larger as the basis function has a center value for which a longer remaining period is set. Specifically, the relationship between s _k ² and μ _k draws a curve as shown in FIG. If μ _k is a large value, it corresponds to a basis function arranged at a position where the remaining period is long. As can be seen from FIG. 16, the basis function placed at a position where the remaining period is longer has a larger value of s _k ² . That is, the spread of the basis function is increased.

  This is because, due to market trends, the spot rate where the remaining period is short varies relatively (due to the difference in remaining period within the spot rate on the same trading day), and the spot rate where the remaining period is long is Considering the fact that there is little change, it is appropriate to use, as a basis function, a portion with a short remaining period and a portion with a long remaining period, and a portion with a long remaining period that has a large spread.

  And the probability density function for such a regression model is expressed as follows.

  A logarithmic likelihood with a penalty including a penalty term from such a probability density function is defined as follows.

なお、λは、正則化パラメータを表す。また、（８）式の第１及び第２項は回帰モデルの誤差の対数尤度を表しており、第３項は重み行列ｗが滑らかに変化するように設定された罰則項を表している。

Note that λ represents a regularization parameter. In addition, the first and second terms in equation (8) represent the log likelihood of the regression model error, and the third term represents a penalty term set so that the weight matrix w changes smoothly. .

The weighting matrix w and sigma ² to maximize such penalized log-likelihood estimate of w w ^ ( "^" represents the hat superscript) and sigma ² estimate σ ² ^ ( "^ "Represents a superscript hat)) and is obtained by solving the following.

  Then, the solution is expressed as follows.

なお、スポットレートベクトルｒ＝（ｒ₁，ｒ₂，．．．，ｒ_i，．．．，ｒ_N）^T（図１４Ａ）である。

Note that the spot rate vector r = (r ₁ , r ₂ ,..., R _i ,..., R _N ) ^T (FIG. 14A).

Parameters m, ν, and λ such as a regression model are determined so as to minimize the value of the generalized information criterion GIC defined by the weight matrices w ^ and σ ² ^ obtained in this way.

  Note that tr in the third term on the right side of equation (10) represents the sum of the diagonal components.

In summary, the curve estimation unit 110 performs a process as shown in FIG. First, the curve estimation unit 110 sets initial values of m, ν, and λ (step S41). Then, the curve estimation unit 110 calculates the estimated values of the weight matrix w and σ ² using m, ν, and λ (step S43). The estimated value is calculated using equation (9).

Then, the curve estimation unit 110 calculates a generalized information criterion value from the estimated values of the weight matrix w and σ ² (step S45). The value of the generalized information criterion is calculated using equation (10).

  Thereafter, the curve estimation unit 110 determines whether or not the end condition of the curve estimation process is satisfied (step S47). The termination condition for minimizing the value of the generalized information criterion is determined according to the nonlinear optimization method to be used. If the end condition is satisfied, the process returns to the caller process.

  On the other hand, when the process termination condition is not satisfied, the curve estimation unit 110 sets new values of m, ν, and λ using a predetermined algorithm (step S49). For example, the values of m, ν, and λ are determined according to a well-known nonlinear optimization method such as a genetic algorithm. Then, the process returns to step S43. However, the nonlinear optimization method is not limited to the genetic algorithm.

When m, ν, and λ are set in advance by some method, the estimated values of the weight matrices w and σ ² are obtained using the values as they are without performing the processing of FIG. Is calculated and used.

By performing the processing as described above, in addition to m, ν, and λ that minimize the value of the generalized information criterion, the weight matrices w and σ ² at that time are also obtained. Therefore, the spot rate for an arbitrary remaining period can be calculated by the following equation and equations (4) to (6).

If there are m, ν and λ, weight matrix w, σ ² , center position μ _k (1 ≦ k ≦ m) of each basis function, and parameter s ² _k (1 ≦ k ≦ m) representing the spread, 14) Since the equation can be calculated, the curve estimation unit 110 stores these data in the seventh data storage unit 111.

  Returning to the description of the processing in FIG. 6, the output unit 112 outputs the data itself stored in the seventh data storage unit 111, or the data stored in the expression (14) and the seventh data storage unit 111. Then, the remaining period is changed in a predetermined increment, the spot rate for the remaining period is calculated, and numerical data is output according to the calculation result or the curve itself is output (step S11).

  For example, data as shown in FIG. 18 may be calculated and output as it is. In the example of FIG. 18, the remaining periods are listed in increments of 0.01, and the spot rates of these remaining periods are listed.

  When the yield curve is used, a curve as shown in FIG. 19 is obtained. In the example of FIG. 19, the horizontal axis represents the remaining period, and the vertical axis represents the spot rate (SR). In the circled part, the number of government bonds is small (see Fig. 2), so in the prior art, only curves that changed greatly as shown in Fig. 3 could be drawn. A changing curve is drawn.

[Modification]
In the embodiment described above, the values of the parameter groups m, ν, and λ are determined on the condition that the value of the generalized information criterion is minimized. However, there are cases where an unstable parameter group in which the yield curve undulates or undershoot occurs is selected due to the large influence of the error included in the spot rate vector (FIG. 14A). Therefore, in this modification, a parameter group that is stable to a certain degree is adopted.

  For this reason, in this modification, the process as shown in FIG. 20 is performed. As shown in FIG. 20A, for example, m, ν, and λ are set in a lattice shape in the parameter space, and the stability index value described below is calculated for each parameter set as shown in FIG. 20B. . In this example, the smaller the stability index value, the higher the stability. Here, a parameter set equal to or less than the threshold value (for example, 0.01 or less) is selected as a candidate, and a generalized information criterion value GIC is calculated as shown in FIG. Finally, as shown in FIG. 20 (d), the parameter set (m, ν, λ) having the smallest value among the values of the calculated generalized information criterion is selected.

For such a process, in this modification, a process as shown in FIG. 21 is executed in place of the curve estimation process of FIG. The curve estimation unit 110 generates a predetermined number of lattice points in the space of the parameters (m, ν, and λ) according to a predetermined rule (step S51). As shown in FIG. 20A, lattice points are generated by varying each of m, ν, and λ with a predetermined width in a predetermined value range. Then, the curve estimation unit 110 calculates the estimated values of the weight matrix w and σ ² for each lattice point using the parameter set (m, ν, λ) of the lattice point (step S53). The estimated value is calculated using equation (9).

  Thereafter, the curve estimation unit 110 executes, for each grid point, a yield index stability index value calculation process corresponding to the grid point (step S55). The stability index value calculation process shown in FIG. 22 is executed for each grid point.

The curve estimation unit 110 uses the spot rate vector r for the first curve (according to the equation (14)) determined by the parameters (including the weight matrix w and σ ² here) and the spot rate vector r for a specific grid point. The spot rate SR in each remaining period t _i (1 ≦ i ≦ N) is calculated (step S61). The first curve determined by the parameter for the specific lattice point and the spot rate vector r is expressed as, for example, the curve a shown in FIG. 23 on the plane spanned by the remaining period and the spot rate SR. The spot rate SR (= r (t _i )) of each remaining period t _{i on} a is calculated. In FIG. 23, the points in the remaining periods represent the values of the elements of the spot rate vector r (FIG. 14A).

Then, the curve estimation unit 110 calculates an error vector ε that is a difference between the spot rate vector r and a calculated SR vector having the calculated spot rate SR as an element (step S63). In the example of FIG. 23, the difference ε _i (the length of the line parallel to the SR axis) between the value of the point and the value on the curve in the same remaining period is calculated. That is, the error vector ε = {ε ₁ , ε ₂ ,. . . , Ε _i ,. . ε _N ).

Thereafter, the curve estimation unit 110 generates R types (for example, 100 types) of bootstrap sample vectors from the error vector ε (step S65). Specifically, an average value ε _a of each element of the error vector ε is calculated, and a vector d (each element ε _di = ε is a difference between the error vector ε and an average vector having all the elements having the average value ε _{a. i} −ε _a ). However, the error vector ε itself may be used as the vector d. Then, N elements are selected by restoration extraction from all elements of the vector d, and adopted as N elements of the vector d2. That is, N elements are randomly selected from all the elements of the vector d, and adopted from the first element to the Nth element of the vector d2 in the selected order. By performing this R times, R vectors d2 are generated. Then, a bootstrap sample vector is generated by adding the calculated SR vector to each vector d2. That is, each element of the bootstrap sample vector is r (t _j ) + ε _dj . Here, ε _dj represents the element selected j-th among all elements of the vector d. In this way, the spot rate vector is swung by placing a pseudo error on each element of the spot rate vector.

Next, the curve estimation unit 110 uses the parameters for a specific lattice point (m, ν, and λ are the same as step S61. W and σ ² are recalculated using the bootstrap sample vector) and the bootstrap sample vector. For each of the determined second curves (according to the equation (14)), the square value of the difference at the M location from the first curve is calculated (step S67). Since one second curve is determined for each bootstrap sample vector, R types of second curves are obtained. In FIG. 24, each element of the bootstrap sample vector is represented by a point, and when this is fitted with a parameter for a specific lattice point, a second curve b is obtained.

Then, as shown in FIG. 25, for example, with a remaining period of 0.5 years, the square value e of the difference e _k (1 ≦ k ≦ M) between the value of the first curve a and the value of the second curve b. _k ² is calculated. As described above, since R lines of the second curve b are drawn, R × M e _k ² are obtained.

Thereafter, the curve estimation unit 110 calculates the average value of the calculated squared differences as a stability index value (step S69). That is, the sum total of R × M e _k ² may be calculated and divided by R × M. As the stability index value, the sum of the square values of the calculated differences may be employed. In addition, the absolute value of the difference may be used instead of the square value of the difference.

  Such a process is executed for each grid point, and the process returns to the caller process.

  Returning to the description of the processing in FIG. 21, the curve estimation unit 110 selects a grid point whose stability index value is equal to or less than a threshold value (step S <b> 57). This is because the smaller the stability index value, the more stable. However, if the stability index value is more stable as it is larger, such as the reciprocal of the value obtained above, a grid point equal to or higher than the threshold value is selected.

Thereafter, the curve estimation unit 110 calculates the value GIC of the generalized information criterion according to the equation (10) from the estimated values of w and σ ² calculated in step S53 for the selected lattice point (step S58). Finally, the curve estimation unit 110 identifies the parameter of the lattice point that minimizes the generalized information criterion value GIC (step S59).

By performing such processing, the parameter set (m, ν, λ) and w and σ ² representing that the stability index value is equal to or higher than a predetermined level can be specified.

  Note that the processing flow of FIG. 21 can be changed to a processing flow as shown in FIG. 26, for example.

First, the curve estimation unit 110 sets initial values of m, ν, and λ (step S71). Then, the curve estimation unit 110 calculates the estimated values of the weight matrix w and σ ² using m, ν, and λ (step S73). The estimated value is calculated using equation (9).

Then, the curve estimation unit 110 performs a stability index value calculation process for a yield curve determined by parameters (including the weight matrix w and σ ² here) and the spot rate vector r (step S75). The stability index value calculation process is the same as the process shown in FIG.

Then, the curve estimation unit 110 determines whether the stability index value is equal to or less than a threshold value (step S77). If the stability index value is less than or equal to the threshold value, the curve estimation unit 110 calculates the value of the generalized information criterion from the estimated values of the weight matrix w and σ ² calculated in step S73 (step S79). The value of the generalized information criterion is calculated using equation (10).

  Thereafter, the curve estimation unit 110 determines whether or not the end condition of the curve estimation process is satisfied (step S81). The termination condition for minimizing the value of the generalized information criterion is determined according to the nonlinear optimization method to be used. If the end condition is satisfied, the process returns to the caller process.

  On the other hand, when the stability index value exceeds the threshold value or when it is determined in step S81 that the processing termination condition is not satisfied, the curve estimation unit 110 sets new m, ν, and λ values using a predetermined algorithm. Set (step S83). For example, the values of m, ν, and λ are determined according to a well-known nonlinear optimization method such as a genetic algorithm. Then, the process returns to step S73. However, the nonlinear optimization method is not limited to the genetic algorithm.

  Such processing searches for the GIC minimum value while confirming the stability index value.

  Although the embodiment of the present technology has been described above, the present technology is not limited to this. For example, the functional block diagram shown in FIG. 4 is an example, and implementation in which the program module configurations do not match is possible.

  Furthermore, the processing flow is also an example, and as long as the processing result does not change, the order of the processing steps may be changed or a plurality of processing steps may be executed in parallel.

  The information processing apparatus 100 that is the curve estimation apparatus described above is a computer apparatus, and as shown in FIG. 27, a memory 2501, a CPU (Central Processing Unit) 2503, a hard disk drive (HDD). 2505, a display control unit 2507 connected to the display device 2509, a drive device 2513 for the removable disk 2511, an input device 2515, and a communication control unit 2517 for connecting to a network are connected by a bus 2519. An operating system (OS) and an application program for executing the processing in this embodiment are stored in the HDD 2505, and are read from the HDD 2505 to the memory 2501 when executed by the CPU 2503. The CPU 2503 controls the display control unit 2507, the communication control unit 2517, and the drive device 2513 according to the processing content of the application program, and performs a predetermined operation. Further, data in the middle of processing is mainly stored in the memory 2501, but may be stored in the HDD 2505. In an embodiment of the present technology, an application program for performing the above-described processing is stored in a computer-readable removable disk 2511 and distributed, and installed from the drive device 2513 to the HDD 2505. In some cases, the HDD 2505 may be installed via a network such as the Internet and the communication control unit 2517. Such a computer apparatus realizes various functions as described above by organically cooperating hardware such as the CPU 2503 and the memory 2501 described above and programs such as the OS and application programs. .

  The above-described embodiment can be summarized as follows.

  The curve estimation method according to the present embodiment stores (A) a maturity date, interest (may be an interest rate), redemption amount, and market price including paid interest for each issue of interest-bearing bonds (for example, government bonds). From the data storage unit, for each maturity date extracted from the maturity date, interest and redemption amount for each maturity date, and (B) the step of extracting one maturity bond for each maturity date A step of generating a matrix enumerating payment amounts at each payment period; and (C) converting the generated matrix into a regular matrix, and from a matrix including a market price for each symbol of the interest-bearing bond and a regular matrix, A step of calculating a discount rate at the maturity date for each of the extracted interest-bearing bond issues, and (D) a maturity date for each of the extracted interest-bearing bond issues from the calculated discount rate at the maturity date. Spot in It comprises calculating the rate, and calculating a regression curve from the spot rate at maturity date calculated (E). The parameter group of the regression model used for calculating the regression curve described above indicates that the index value indicating the stability of the regression curve is stable at a predetermined level or more and is related to the parameter group. Is calculated so that the value of the generalized information criterion specified in the above is minimized.

  By carrying out such processing, a stable and appropriate curve can be obtained even when the interest rate data for a certain period is insufficient.

  In addition, the index value indicating the stability of the regression curve described above includes a first regression curve estimated from the calculated spot rate on the maturity date and a specific parameter group, and the calculated spot rate on the maturity date. There may be a case where the bootstrap sample obtained from the error from the first regression curve and the second error from the second regression curve estimated from the specific parameter group may be used. The absolute value or square value of the second error, or an average value of any of them can be used. In addition, by generating a plurality of sets of bootstrap samples, the stability can be determined with higher accuracy.

  Further, the above-described processing for calculating the regression curve calculates (e1) an index value representing the stability of the regression curve for each candidate value of the parameter group, and (e2) an index representing the stability of the regression curve. Identifying a candidate value representing that the value is stable above a predetermined level, (e3) calculating a generalized information criterion value for each of the identified candidate values, and (e4) calculating the generalization You may make it include the process which specifies the candidate value from which the value of an information criterion is the minimum. Thereby, candidate values can be narrowed down efficiently.

  Further, the process of calculating the regression curve described above determines (e5) a parameter group candidate value, (e6) calculates an index value representing the stability of the regression curve for the parameter group candidate value, and (e7 ) When the index value indicating the stability of the regression curve indicates that the index value is stable at a predetermined level or more, the value of the generalized information criterion is calculated for the candidate value, and (e8) the generalized information criterion You may make it include the process which determines whether a value becomes the minimum. Candidate values can be searched while considering the stability.

  Further, the regression model used in calculating the regression curve may represent the spot rate on the maturity date as a weighted sum of a plurality of basis functions. In that case, the second maturity from the present to the latest maturity date from the first period that is shorter than the period from the present to the earliest first maturity date by a predetermined period. In some cases, the interval is set at an equal interval from a period up to a second period longer than a period up to the day. In some cases, the parameter for controlling the spread of each of the plurality of basis functions is set to be larger as the center value of the basis function is larger. According to such a regression model, a more reliable smooth regression curve can be obtained.

  The parameter group described above may include a parameter related to the interval between the center values of the basis function in the regression model, a parameter for controlling the spread of the basis function, and a regular parameter included in the penalty term.

  In addition, the data storage unit described above may further store a transaction amount or a bid date for each brand of interest-bearing bonds. In this case, in the step of extracting one brand of interest-bearing bond for each maturity date, one brand of interest-bearing bond may be selected for each maturity date based on the transaction amount or the bidding date. According to the non-obvious knowledge of the inventor, a more appropriate regression curve can be obtained by performing such selection.

  Further, in the step of extracting one issue of interest-bearing bonds for each maturity date described above, if there are multiple issues of interest-bearing bonds with the same maturity date, a hypothetical 1 You may make it calculate the interest, the redemption amount, and the market price of the interest-bearing bond of one brand. Statistically appropriate data can be generated.

  In the process of calculating the discount rate described above, the payment amount at the maturity date immediately after the payment date is calculated from the payment amount at the payment date other than the maturity date of the extracted interest-bearing bonds in the generated matrix. Then, it may be converted into a regular matrix in which payment amounts for each maturity date are listed for each of the extracted interest-bearing bonds. In this way, the interest payment timing of the extracted interest-bearing bonds is appropriately aggregated on the maturity date of any issue of the interest-bearing bonds.

  Further, the processing for calculating the discount rate described above is (c1) In the generated matrix, from the payment amount at the payment date other than the maturity date of the extracted interest-bearing bond issue, A process of calculating a payment amount at the maturity date immediately after the payment date using the first interest rate at the maturity date, and converting the payment amount at the maturity date for each of the extracted interest-bearing bonds into a regular matrix; (C2) Processing for calculating the second interest rate at the maturity date for each of the extracted interest-bearing bond issues from the product of the inverse matrix of the regular matrix and the matrix including the market price for each extracted interest-bearing bond issue And (c3) replacing the first interest rate with the second interest rate until the difference between the first interest rate and the second interest rate is all equal to or less than a predetermined threshold, converting the regular matrix and the second interest rate To include the process of repeatedly performing the calculation It may be.

  The second interest rate obtained by performing such processing is an appropriate discount rate. Note that when calculating the payment amount at the maturity date, the interest rate for a period not included in the first interest rate may be calculated by interpolation calculation (for example, cubic spline interpolation).

  Note that a program for causing a computer to execute the processing described above can be created, and the program includes, for example, a flexible disk, an optical disk such as a CD-ROM, a magneto-optical disk, and a semiconductor memory (for example, ROM). Or a computer-readable storage medium such as a hard disk or a storage device. Note that data being processed is temporarily stored in a storage device such as a RAM.

  The following supplementary notes are further disclosed with respect to the embodiments including the above examples.

(Appendix 1)
From the data storage unit that stores the maturity date, interest, redemption amount, and market price including paid interest for each issue of interest-bearing bonds, extract one issue of interest-bearing bonds for each maturity date,
From the maturity date, the interest, and the redemption amount for the extracted interest-bearing bond issue, generate a matrix listing the payment amount for each payment date for each extracted interest-bearing issue issue,
The generated matrix is converted into a regular matrix, and the discount on the maturity date for each of the extracted interest-bearing bond issues is extracted from the extracted matrix including the market price for each interest-bearing bond issue and the regular matrix. Execute the process to calculate the rate,
From the calculated discount rate at the maturity date, calculate the spot rate at the maturity date for each of the extracted interest-bearing bond issues,
Including a process of calculating a regression curve from the calculated spot rate on the maturity date, and is executed by a computer,
The parameter group of the regression model used for calculating the regression curve represents that the index value indicating the stability of the regression curve is stable at a predetermined level or more and is defined in relation to the parameter group. A curve estimation method that calculates the standardized information criterion to minimize the value.

(Appendix 2)
An index value representing the stability of the regression curve is
A bootstrap sample obtained from an error between the calculated spot rate on the maturity date and a first regression curve estimated from a specific parameter group, and the calculated spot rate on the maturity date and the first regression curve The curve estimation method according to claim 1, wherein the curve estimation method is calculated by a second error from the second regression curve estimated from the specific parameter group.

(Appendix 3)
The process of calculating the regression curve includes:
For each of the parameter group candidate values, calculate an index value representing the stability of the regression curve,
Identify a candidate value indicating that the index value representing the stability of the regression curve is stable above a predetermined level;
Calculating a value of the generalized information criterion for each of the identified candidate values;
The curve estimation method according to supplementary note 1 or 2, including a process of identifying a candidate value that minimizes the calculated value of the generalized information criterion.

(Appendix 4)
The process of calculating the regression curve includes:
Determining candidate values for the parameter group;
Calculating an index value representing the stability of the regression curve for the candidate values of the parameter group;
When the index value indicating the stability of the regression curve indicates that the index value is stable over a predetermined level, the value of the generalized information criterion is calculated for the candidate value,
The curve estimation method according to appendix 1 or 2, including a process of determining whether or not the value of the generalized information criterion is minimum.

(Appendix 5)
The regression model used in the calculation of the regression curve is
The spot rate on the maturity date is expressed as a weighted sum of a plurality of basis functions,
The second maturity from the present to the latest maturity date from the first period shorter than the period from the present to the earliest first maturity date by the center value of the plurality of basis functions. Set at an interval equally divided into a period up to the second period that is longer than the period up to the day,
The curve estimation method according to any one of appendices 1 to 4, wherein a parameter that controls the spread of each of the plurality of basis functions is set to have a larger value as the center value of the basis function is larger.

(Appendix 6)
The curve according to claim 5, wherein the parameter group includes a parameter related to an interval between center values of the basis functions in the regression model, a parameter for controlling a spread of the basis function, and a regular parameter included in the penalty term. Estimation method.

(Appendix 7)
The data storage unit further stores a transaction amount or a bid date for each issue of the interest-bearing bond,
In the process of extracting one issue of interest-bearing bonds for each maturity date,
The curve estimation method according to any one of appendices 1 to 6, wherein one interest-bearing bond is selected for each maturity date based on the transaction amount or the bidding date.

(Appendix 8)
In the process of extracting one issue of interest-bearing bonds for each maturity date,
When there are multiple interest-bearing bonds with the same maturity date, the interest, redemption amount, and market price of a hypothetical interest-bearing bond are calculated from the data of interest-bearing bonds of the multiple issues 6. The curve estimation method according to any one of 6.

(Appendix 9)
The process of calculating the discount rate is:
In the generated matrix, the payment amount at the maturity date immediately after the payment date is calculated from the payment amount at the payment date other than the maturity date of the extracted interest-bearing bond issue, and for each of the extracted interest-bearing bond issues The curve estimation method according to any one of appendices 1 to 8, wherein the payment amount for each maturity date is converted into a regular matrix that lists the payment amounts.

(Appendix 10)
The process of calculating the discount rate is:
In the generated matrix, immediately after the payment date using the first interest rate on the maturity date of the extracted interest-bearing bond issue from the payment amount at the payment date other than the maturity date of the extracted interest-bearing bond issue. To calculate a payment amount at the maturity date, and convert it into a regular matrix enumerating the payment amounts at the maturity date for each of the extracted interest-bearing bonds.
A second interest rate at the maturity date is calculated for each of the extracted interest-bearing bond issues from the product of the inverse matrix of the regular matrix and the extracted matrix including the market price for each interest-bearing bond issue. ,
The first interest rate is replaced with the second interest rate until the difference between the first interest rate and the second interest rate is equal to or less than a predetermined threshold value, and the transformation of the regular matrix and the second interest rate are performed. The curve estimation method according to any one of appendices 1 to 8, including a process of repeatedly executing the calculation.

(Appendix 11)
From the data storage unit that stores the maturity date, interest, redemption amount, and market price including paid interest for each issue of interest-bearing bonds, extract one issue of interest-bearing bonds for each maturity date,
From the maturity date, the interest, and the redemption amount for the extracted interest-bearing bond issue, generate a matrix listing the payment amount for each payment date for each extracted interest-bearing issue issue,
The generated matrix is converted into a regular matrix, and the discount on the maturity date for each of the extracted interest-bearing bond issues is extracted from the extracted matrix including the market price for each interest-bearing bond issue and the regular matrix. Execute the process to calculate the rate,
From the calculated discount rate at the maturity date, calculate the spot rate at the maturity date for each of the extracted interest-bearing bond issues,
Causing the computer to execute a process of calculating a regression curve from the calculated spot rate on the maturity date,
The parameter group of the regression model used for calculating the regression curve represents that the index value indicating the stability of the regression curve is stable at a predetermined level or more and is defined in relation to the parameter group. A curve estimation program that calculates the standardized information criterion to minimize the value.

(Appendix 12)
An extractor for extracting one issue of interest-bearing bonds for each maturity date from a data storage unit for storing maturity date, interest, redemption amount and market price including paid interest for each issue of interest-bearing bonds;
A generation unit that generates a matrix that lists payment amounts at each payment period for each of the extracted interest-bearing bonds from the maturity date, the interest, and the redemption amount for the extracted interest-bearing bonds;
The generated matrix is converted into a regular matrix, and the discount on the maturity date for each of the extracted interest-bearing bond issues is extracted from the extracted matrix including the market price for each interest-bearing bond issue and the regular matrix. A discount rate calculation unit that executes processing for calculating a rate;
A spot rate calculation unit for calculating a spot rate at the maturity date for each of the extracted interest-bearing bond issues from the calculated discount rate at the maturity date;
A curve calculation unit for calculating a regression curve from the calculated spot rate on the maturity date;
Have
The parameter group of the regression model used for calculating the regression curve represents that the index value indicating the stability of the regression curve is stable at a predetermined level or more and is defined in relation to the parameter group. A curve estimator that calculates the standardized information criterion to minimize the value.

101 first data storage unit 102 extraction unit 103 second data storage unit 104 matrix generation unit 105 third data storage unit 106 discount rate calculation unit 113 fourth data storage unit 107 fifth data storage unit 108 spot rate calculation unit 109 sixth Data storage unit 110 Curve estimation unit 111 Seventh data storage unit 112 Output unit 1061 Regular conversion unit

Claims (12)

From the data storage unit that stores the maturity date, interest, redemption amount, and market price including paid interest for each issue of interest-bearing bonds, extract one issue of interest-bearing bonds for each maturity date,
From the maturity date, the interest, and the redemption amount for the extracted interest-bearing bond issue, generate a matrix listing the payment amount for each payment date for each extracted interest-bearing issue issue,
The generated matrix is converted into a regular matrix, and the discount on the maturity date for each of the extracted interest-bearing bond issues is extracted from the extracted matrix including the market price for each interest-bearing bond issue and the regular matrix. Execute the process to calculate the rate,
From the calculated discount rate at the maturity date, calculate the spot rate at the maturity date for each of the extracted interest-bearing bond issues,
Including a process of calculating a regression curve from the calculated spot rate on the maturity date, and is executed by a computer,
The parameter group of the regression model used for calculating the regression curve represents that the index value indicating the stability of the regression curve is stable at a predetermined level or more and is defined in relation to the parameter group. A curve estimation method that calculates the standardized information criterion to minimize the value. An index value representing the stability of the regression curve is
A bootstrap sample obtained from an error between the calculated spot rate on the maturity date and a first regression curve estimated from a specific parameter group, and the calculated spot rate on the maturity date and the first regression curve The curve estimation method according to claim 1, wherein the curve estimation method is calculated by a second error from the second regression curve estimated from the specific parameter group. The process of calculating the regression curve includes:
For each of the parameter group candidate values, calculate an index value representing the stability of the regression curve,
Identify a candidate value indicating that the index value representing the stability of the regression curve is stable above a predetermined level;
Calculating a value of the generalized information criterion for each of the identified candidate values;
The curve estimation method according to claim 1, further comprising: identifying a candidate value that minimizes the calculated value of the generalized information criterion. The process of calculating the regression curve includes:
Determining candidate values for the parameter group;
Calculating an index value representing the stability of the regression curve for the candidate values of the parameter group;
When the index value indicating the stability of the regression curve indicates that the index value is stable over a predetermined level, the value of the generalized information criterion is calculated for the candidate value,
The curve estimation method according to claim 1, further comprising a process of determining whether or not the value of the generalized information criterion is minimum. The regression model used in the calculation of the regression curve is
The spot rate on the maturity date is expressed as a weighted sum of a plurality of basis functions,
The second maturity from the present to the latest maturity date from the first period shorter than the period from the present to the earliest first maturity date by the center value of the plurality of basis functions. Set to a position that equally divides the range up to the second period longer than the period up to the day,
The curve estimation method according to any one of claims 1 to 4, wherein a parameter that controls the spread of each of the plurality of basis functions is set to have a larger value as the center value of the basis function is larger. It said parameter group, and parameters related to the spacing of the center value of the basis functions in the regression model, the parameters that control the spread of the basis functions, according to claim 5, further comprising a regularization parameter included in punishment Sokuko Curve estimation method. The data storage unit further stores a transaction amount or a bid date for each issue of the interest-bearing bond,
In the process of extracting one issue of interest-bearing bonds for each maturity date,
The curve estimation method according to any one of claims 1 to 6, wherein a single interest-bearing bond is selected for each maturity date based on the transaction amount or a bid date. In the process of extracting one issue of interest-bearing bonds for each maturity date,
When there are multiple issues of interest-bearing bonds with the same maturity date, the interest, redemption amount, and market price of a hypothetical interest-bearing bond are calculated from the data of interest-bearing bonds of the multiple issues. The curve estimation method as described in any one of thru | or 6. The process of calculating the discount rate is:
In the generated matrix, the payment amount at the maturity date immediately after the payment date is calculated from the payment amount at the payment date other than the maturity date of the extracted interest-bearing bond issue, and for each of the extracted interest-bearing bond issues The curve estimation method according to any one of claims 1 to 8, wherein the payment amount for each maturity date is converted into a regular matrix that lists the payment amounts. The process of calculating the discount rate is:
In the generated matrix, immediately after the payment date using the first interest rate on the maturity date of the extracted interest-bearing bond issue from the payment amount at the payment date other than the maturity date of the extracted interest-bearing bond issue. To calculate a payment amount at the maturity date, and convert it into a regular matrix enumerating the payment amounts at the maturity date for each of the extracted interest-bearing bonds.
A second interest rate at the maturity date is calculated for each of the extracted interest-bearing bond issues from the product of the inverse matrix of the regular matrix and the extracted matrix including the market price for each interest-bearing bond issue. ,
The first interest rate is replaced with the second interest rate until the difference between the first interest rate and the second interest rate is equal to or less than a predetermined threshold value, and the transformation of the regular matrix and the second interest rate are performed. The curve estimation method according to any one of claims 1 to 8, further comprising a process of repeatedly executing the calculation. From the data storage unit that stores the maturity date, interest, redemption amount, and market price including paid interest for each issue of interest-bearing bonds, extract one issue of interest-bearing bonds for each maturity date,
From the maturity date, the interest, and the redemption amount for the extracted interest-bearing bond issue, generate a matrix listing the payment amount for each payment date for each extracted interest-bearing issue issue,
The generated matrix is converted into a regular matrix, and the discount on the maturity date for each of the extracted interest-bearing bond issues is extracted from the extracted matrix including the market price for each interest-bearing bond issue and the regular matrix. Execute the process to calculate the rate,
From the calculated discount rate at the maturity date, calculate the spot rate at the maturity date for each of the extracted interest-bearing bond issues,
Causing the computer to execute a process of calculating a regression curve from the calculated spot rate on the maturity date,
The parameter group of the regression model used for calculating the regression curve represents that the index value indicating the stability of the regression curve is stable at a predetermined level or more and is defined in relation to the parameter group. A curve estimation program that calculates the standardized information criterion to minimize the value. An extractor for extracting one issue of interest-bearing bonds for each maturity date from a data storage unit for storing maturity date, interest, redemption amount and market price including paid interest for each issue of interest-bearing bonds;
A generation unit that generates a matrix that lists payment amounts at each payment period for each of the extracted interest-bearing bonds from the maturity date, the interest, and the redemption amount for the extracted interest-bearing bonds;
The generated matrix is converted into a regular matrix, and the discount on the maturity date for each of the extracted interest-bearing bond issues is extracted from the extracted matrix including the market price for each interest-bearing bond issue and the regular matrix. A discount rate calculation unit that executes processing for calculating a rate;
A spot rate calculation unit for calculating a spot rate at the maturity date for each of the extracted interest-bearing bond issues from the calculated discount rate at the maturity date;
A curve calculation unit for calculating a regression curve from the calculated spot rate on the maturity date;
Have
The parameter group of the regression model used for calculating the regression curve represents that the index value indicating the stability of the regression curve is stable at a predetermined level or more and is defined in relation to the parameter group. A curve estimator that calculates the standardized information criterion to minimize the value.

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