WO2019218518A1 - 债券etf的构建方法、装置及计算机可读存储介质 - Google Patents

债券etf的构建方法、装置及计算机可读存储介质 Download PDF

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WO2019218518A1
WO2019218518A1 PCT/CN2018/102136 CN2018102136W WO2019218518A1 WO 2019218518 A1 WO2019218518 A1 WO 2019218518A1 CN 2018102136 W CN2018102136 W CN 2018102136W WO 2019218518 A1 WO2019218518 A1 WO 2019218518A1
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bonds
sample
bond
data
duration
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PCT/CN2018/102136
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French (fr)
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李海疆
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Definitions

  • the present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, and a computer readable storage medium for constructing a bond ETF.
  • ETF Exchange-Traded Funds
  • ETFs are classified into stock ETFs, bond ETFs, etc. according to different underlying assets.
  • the existing bond ETFs are basically bond-oriented ETFs and credit bonds. ETFs are very scarce. Therefore, in order to promote the development of bond ETFs and fill the gaps in the market, there is a real need to develop credit debt ETFs.
  • the present application provides a method, an apparatus, and a computer readable storage medium for constructing a bond ETF, the main purpose of which is to solve the technical problem of a construction scheme of a bond ETF that is not applicable to a credit bond in the prior art.
  • the present application also provides a method for constructing a single market bond ETF, the method comprising:
  • sampling factor includes duration, Cumulative volume and issue size
  • sample bonds are combined according to the calculated weights to form a single market bond ETF corresponding to the target time interval.
  • the present application further provides a device for constructing a single-market bond ETF, the device comprising a memory and a processor, wherein the memory stores an ETF construction program executable on the processor,
  • the ETF build program is implemented by the processor to implement the following steps:
  • sampling factor includes duration, Cumulative volume and issue size
  • sample bonds are combined according to the calculated weights to form a single market bond ETF corresponding to the target time interval.
  • the present application further provides a computer readable storage medium having an ETF construction program stored thereon, the ETF construction program being executable by one or more processors to implement The steps of the method of constructing a single market bond ETF as described above.
  • the method, device and computer readable storage medium for constructing the bond ETF proposed by the present application obtain the duration data of the component coupon of the single market bond index in the target time interval, according to the obtained duration data and the preset duration interval
  • the component voucher is divided into a plurality of sets of bonds; the data corresponding to the sampling factor of the component voucher in the target time interval is obtained, and the component voucher in each group of bonds is scored according to the data corresponding to the obtained sampling factor and the preset scoring rule, wherein
  • the sampling factor includes duration, cumulative volume and issue size; determine the sample size of each group of bonds; sample bonds are drawn from each group of bonds according to the scores and sample quantities of the constituent bonds in each group of bonds; and the sample bonds are obtained in the target time interval.
  • the income deviation quadratic programming model is used to calculate the weight of the sample bonds; the sample weights are combined according to the calculated weights to form a single market bond ETF corresponding to the target time interval.
  • the solution of the present application is a new bond copying logic, which scores the component coupons and extracts a certain amount of sample coupons for tracking, and uses the income deviation quadratic programming model to assign appropriate weights to the sample coupons according to the transaction income data.
  • FIG. 1 is a schematic flowchart of a method for constructing a single-market bond ETF according to an embodiment of the present application
  • FIG. 2 is a schematic diagram of an internal structure of a device for constructing a single-market bond ETF according to an embodiment of the present application
  • FIG. 3 is a schematic diagram of a module of an ETF construction program in a device for constructing a single-market bond ETF according to an embodiment of the present application.
  • FIG. 1 is a schematic flowchart diagram of a method for constructing a single-market bond ETF according to an embodiment of the present application.
  • the method can be performed by a device that can be implemented by software and/or hardware.
  • the method for constructing a single market bond ETF includes:
  • Step S10 Obtain the duration data of the component coupon of the single market bond index in the target time interval, and divide the component coupon into a plurality of groups of bonds according to the obtained duration data and the preset duration interval.
  • the single market bond index of the Shanghai Stock Exchange is used as an analysis object, and a certain number of sample coupons are extracted from the bond coupons of the bond index as objects for tracking and copying, and a single market bond ETF of the Shanghai Stock Exchange is established. .
  • the constituent bonds of the SSE's single market bond index are bonds that are selected from all of its listed bonds and meet the pre-set screening criteria. For example, statistically check all the bonds listed before the deadline, and select bonds with a remaining period of time above the preset duration, subject ratings and credit ratings above the preset level, and issue sizes above the preset amount.
  • the selection of the above-mentioned preset duration, preset level and preset quantity can be reasonably set according to the actual situation of the exchange listed bonds.
  • the preset duration is 1 year
  • the preset number is 2 billion
  • the preset level is AA.
  • the index's target selection is more than 2 billion bond issuance, which provides a realistic operational space for the later bond ETF replication.
  • the bonds selected according to the above conditions will be used as the constituent bonds of the Shanghai Stock Exchange single market bonds.
  • the duration data of the component coupons in the target time interval is obtained from the preset database, and in this embodiment, one month is used as a time unit. Obtaining the duration data of the constituent coupons in one month, and dividing the constituent coupons into a plurality of sets of bonds according to the obtained duration data and the preset duration interval, wherein the preset duration interval is preset by the user, The basis for grouping the coupons.
  • Step S10 may include the following refinement steps:
  • the duration data is used as the duration data of the component coupon in the target time interval; the component coupons are divided into groups of bonds according to the duration data of the component coupons in the target time interval and the preset duration interval.
  • the following content uses December 2016 as the target time unit, with the data example for this month.
  • the data is the revised duration.
  • the source of the CSI index company uses the arithmetic average calculation method for each
  • the bond calculates the monthly average duration. According to the monthly average duration, all the constituent coupons are divided into modified groups of 0-3 years, 3-6 years, 6-9 years, and 9 years, respectively, with a total of four groups.
  • Step S20 Acquire data corresponding to the sampling factor of the component voucher in the target time interval, and score the component voucher in each group of bonds according to the data corresponding to the obtained sampling factor and the preset scoring rule, wherein the sampling factor includes Duration, cumulative volume and issue size.
  • step S20 may include the following refinement steps:
  • the data corresponding to each sampling factor is scored according to a preset scoring rule corresponding to each sampling factor, and the score corresponding to each sampling factor is obtained;
  • the weighted average score of the first score is calculated according to the score corresponding to each sampling factor and the preset proportion of each sampling factor, and the weighted score is used as the score of the constituent coupon.
  • the preset proportion of each sampling factor may be set according to the importance degree of each sampling factor.
  • the preset proportion of the monthly accumulated volume factor is preferably 50%
  • the bond duration factor is preferably 25%
  • the default ratio of bond issuance scale factors is 25%.
  • the component coupons in the group are scored according to the scoring rules corresponding to the above three sampling factors, and the three scores of each bond are weighted and summed according to the score result and the proportion of each sampling factor. As the final score.
  • the scoring rules can be set by the user as needed.
  • the modified duration of the bond is 0.5-1.1, the score is 10 points, and if the modified duration of the bond is 1.1-1.7, The score is 8 points. If the modified duration of the bond is 1.7-2.3, the score is 6 points. If the modified duration of the bond is 2.3-3, the score is 4 points.
  • the bond's revised duration is 3-3.7, the score is 10 points. If the bond's revised duration is 3.7-4.4, the score is 8 points.
  • the modified duration of the bond is 4.4-5.1, the score is 6 points, and if the modified duration of the bond is 5.1-6, the score is 4 points.
  • the score is 10 points. If the bond's modified duration is 7-7.5, the score is 8 points. The bond's revised duration is 7.5-8.1, and the score is 6 points. If the bond's revised duration is 8.1-9, the score is 4 points.
  • the scoring rules of the issue size and the monthly cumulative volume you can set similar scoring rules by referring to the above-mentioned revised duration scoring rules, and will not repeat them here. The larger the issue size, the higher the score; the larger the monthly cumulative volume The higher the score.
  • the coupons are scored according to the scoring rules of each sampling factor, and three scores can be obtained respectively.
  • the preset proportion is 50%
  • the bond duration factor is preset. 25%
  • the pre-determination ratio of the bond issuance scale factor is 25%
  • the three scores are weighted and summed, and the summation result is used as the final score.
  • the vouchers in each group of bonds are sorted in descending order of final score.
  • step S30 the number of samples of each group of bonds is determined.
  • the steps of determining the sample quantity of each group of bonds include: determining the total number of constituent vouchers of the single market bond index, the target copy rate, and the copy ratio of each group of bonds; calculating the total sample data according to the total number of the constituent vouchers and the target copy rate Calculating the number of samples of each group of bonds based on the total sampled data and the copy ratio of the groups of bonds.
  • the target copy rate of the single market bond ETF and the copy ratio of each group of bonds are set in advance.
  • the target replication rate is 20%
  • the replication ratio of the bond group with a modified duration of 0-3 years is 50.94%
  • the replication ratio of the bond group with a modified duration of 3-6 years is 40.57%
  • the revised duration is 6-
  • the copy ratio of the 9-year bond group is 7.55%
  • the copy ratio of the bond group with the revised duration of 9 years or more is 0.94%.
  • the copy ratio of each group of bonds is the ratio of the number of sample coupons to be selected in each group of bonds to the total number of sampled bonds.
  • the proportion of the number of sample coupons to be selected in each group of bonds in the total number of constituent bonds of the group of bonds can be calculated.
  • the total number of constituent vouchers in each group of bonds can be determined, and the ratio of the number of sample vouchers to be selected in each group of bonds to the total number of constituent vouchers of the group of bonds can be calculated. The number of samples.
  • step S40 sample bonds are drawn from each group of bonds according to the scores of the constituent coupons in each group of bonds and the number of samples.
  • the highest score in each group is selected as the sample coupon of the sample quantity, that is, the coupon for constructing the single market bond ETF.
  • Step S50 Acquire transaction income data of the sample bond in the target time interval, and calculate a weight of the sample bond by using the income deviation quadratic programming model according to the transaction income data.
  • Step S60 combining the sample bonds according to the calculated weights to form a single market bond ETF corresponding to the target time interval.
  • the process function for calculating the optimal weight of the sample coupon using the income deviation quadratic programming model can be expressed as the following expression 1:
  • Q i is the number of positions opened by bond i at the beginning of the month, and it is also the solution object of this quadratic programming problem. It is calculated to find the optimal number of positions opened at the beginning of each month, that is, the optimal weight of bond i in the bond portfolio. . Assuming a position is opened in early January 2017, December 2016 is the target time interval, and sample coupons are selected based on this month's data and weights are calculated.
  • R T+1 is the bond index of the T-day closing to the T+1 day closing
  • P i T is the closing price of the bond i on the T trading day
  • the P i T+1 bond i is trading at the T+1.
  • n is the total number of trading days in a month
  • m is the total number of sample coupons selected.
  • the bond index can be calculated according to the calculation rules of the bond index.
  • the bond index can be calculated according to the total market value of all the bonds of the bond index on the current trading day, the total market value of all the constituent bonds on the previous trading day, the value of the bond index on the previous trading day, and the preset index calculation.
  • the formula calculates the bond index for the current trading day.
  • R T+1 is the bond index of the current trading day T+1
  • R T is the bond index of the previous trading day T of the current trading day
  • S T+1 is the total market value of all the constituent bonds on the current trading day
  • S T is the total market value of all constituent bonds on the previous trading day.
  • w i is the optimal weight of the sample coupon i, which is the solution object of the expression
  • w i of all sample coupons satisfy the condition: Where w i ⁇ 1,
  • R T+1 is the bond index of T+1 on the trading day.
  • the scheme uses the exhaustive method to calculate the optimal weight of each sample coupon, wherein the exhaustive unit is set to 0.01.
  • the sample coupons When the position is opened in early January 2017, December 2016 will be used as the target time interval. According to the data of this month, the sample coupons will be selected to constitute the holding basket of January 2017, and the optimal weight of each standard ticket in the basket will be calculated according to the above model. The sample coupons in the combination basket according to the calculated optimal weight constitute the January 2017 single market bond ETF.
  • each month of the month can be adjusted according to the transaction situation of the previous month, a new position basket is established, and each sample coupon in the basket is recalculated. Weight, build the new monthly single market bond ETF.
  • the method for constructing the single-market bond ETF proposed in this embodiment obtains the duration data of the component coupon of the single-market bond index in the target time interval, and divides the component coupon according to the obtained duration data and the preset duration interval.
  • Group bond obtain the data corresponding to the sampling factor of the component voucher in the target time interval, and score the component voucher in each group of bonds according to the data corresponding to the obtained sampling factor and the preset scoring rule, wherein the sampling factor includes the duration , cumulative volume and issue size; determine the sample size of each group of bonds; draw sample bonds from each group of bonds according to the scores and sample quantities of the constituent bonds in each group of bonds; obtain the transaction income data of the sample bonds in the target time interval, according to The transaction income data uses the income deviation quadratic programming model to calculate the weight of the sample bonds; the sample weights are combined according to the calculated weights to form a single market bond ETF corresponding to the target time interval.
  • the solution of the present application is a new bond copying logic, which scores the component coupons and extracts a certain amount of sample coupons for tracking, and uses the income deviation quadratic programming model to assign appropriate weights to the sample coupons according to the transaction income data.
  • FIG. 2 is a schematic diagram showing the internal structure of a device for constructing a single-market bond ETF according to an embodiment of the present application.
  • the construction device 1 of the single-market bond ETF may be a PC (Personal Computer), or may be a terminal device such as a smart phone, a tablet computer, or a portable computer.
  • the construction apparatus 1 of the single market bond ETF includes at least a memory 11, a processor 12, a network interface 13, and a communication bus 14.
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like.
  • the memory 11 may in some embodiments be an internal storage unit of the construction device 1 of the single market bond ETF, such as the hard disk of the construction device 1 of the single market bond ETF.
  • the memory 11 may also be an external storage device of the construction device 1 of the single-market bond ETF in other embodiments, such as a plug-in hard disk equipped with a single-market bond ETF, and a smart memory card (Smart Media Card, SMC). ), Secure Digital (SD) card, Flash Card, etc.
  • SD Secure Digital
  • the memory 11 may also include an internal storage unit of the construction device 1 of the single market bond ETF and an external storage device.
  • the memory 11 can be used not only for storing application software and various types of data of the construction apparatus 1 installed in the single-market bond ETF, such as code of the ETF construction program 01, but also for temporarily storing data that has been output or is to be output.
  • the processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing stored in the memory 11. Data, such as executing ETF build program 01 and the like.
  • CPU Central Processing Unit
  • controller microcontroller
  • microprocessor or other data processing chip for running program code or processing stored in the memory 11.
  • Data such as executing ETF build program 01 and the like.
  • the network interface 13 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is typically used to establish a communication connection between the device 1 and other electronic devices.
  • a standard wired interface such as a WI-FI interface
  • Communication bus 14 is used to implement connection communication between these components.
  • the device 1 may further include a user interface
  • the user interface may include a display
  • an input unit such as a keyboard
  • the optional user interface may further include a standard wired interface and a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like.
  • the display may also be suitably referred to as a display screen or display unit for displaying information processed in the construction device 1 of the single market bond ETF and a user interface for displaying visualizations.
  • FIG. 1 shows only the construction apparatus 1 of the single market bond ETF having the components 11-14 and the ETF construction program 01. It will be understood by those skilled in the art that the structure shown in FIG. 1 does not constitute a single market bond ETF.
  • the definition of the building device 1 may include fewer or more components than those illustrated, or some components may be combined, or different component arrangements.
  • the ETF construction program 01 is stored in the memory 11; when the processor 12 executes the ETF construction program 01 stored in the memory 11, the following steps are implemented:
  • the single market bond index of the Shanghai Stock Exchange is used as an analysis object, and a certain number of sample coupons are extracted from the bond coupons of the bond index as objects for tracking and copying, and a single market bond ETF of the Shanghai Stock Exchange is established. .
  • the constituent bonds of the SSE's single market bond index are bonds that are selected from all of its listed bonds and meet the pre-set screening criteria. For example, statistically check all the bonds listed before the deadline, and select bonds with a remaining period of time above the preset duration, subject ratings and credit ratings above the preset level, and issue sizes above the preset amount.
  • the selection of the above-mentioned preset duration, preset level and preset quantity can be reasonably set according to the actual situation of the exchange listed bonds.
  • the preset duration is 1 year
  • the preset number is 2 billion
  • the preset level is AA.
  • the index's target selection is more than 2 billion bond issuance, which provides a realistic operational space for the later bond ETF replication.
  • the bonds selected according to the above conditions will be used as the constituent bonds of the Shanghai Stock Exchange single market bonds.
  • the duration data of the component coupons in the target time interval is obtained from the preset database, and in this embodiment, one month is used as a time unit. Obtaining the duration data of the constituent coupons in one month, and dividing the constituent coupons into a plurality of sets of bonds according to the obtained duration data and the preset duration interval, wherein the preset duration interval is preset by the user, The basis for grouping the coupons.
  • Obtaining the duration data of the component voucher of the single market bond index in the target time interval, and dividing the component voucher into a plurality of sets of bonds according to the obtained duration data and the preset duration interval may include the following refinement steps:
  • the duration data is used as the duration data of the component coupon in the target time interval; the component coupons are divided into groups of bonds according to the duration data of the component coupons in the target time interval and the preset duration interval.
  • the following content uses December 2016 as the target time unit, with the data example for this month.
  • the data is the revised duration.
  • the source of the CSI index company uses the arithmetic average calculation method for each
  • the bond calculates the monthly average duration. According to the monthly average duration, all the constituent coupons are divided into modified groups of 0-3 years, 3-6 years, 6-9 years, and 9 years, respectively, with a total of four groups.
  • the bond duration, the monthly accumulated volume, and the bond issuance scale are used as sampling factors, and other factors may be added as sampling factors in other embodiments.
  • the data of the above-mentioned sampling factor of the constituent coupons in each group of bonds in December 2016 is obtained from the database, that is, the duration data of the constituent coupons, the issuance scale data, and the monthly cumulative volume data.
  • the coupons in each group of bonds are scored based on these data and preset scoring rules. Specifically, in an embodiment, the data corresponding to the sampling factor of the component voucher is obtained in the target time interval, and the component voucher in each group of bonds is scored according to the data corresponding to the obtained sampling factor and the preset scoring rule.
  • the steps can include the following refinement steps:
  • the data corresponding to each sampling factor is scored according to a preset scoring rule corresponding to each sampling factor, and the score corresponding to each sampling factor is obtained;
  • the weighted average score of the first score is calculated according to the score corresponding to each sampling factor and the preset proportion of each sampling factor, and the weighted score is used as the score of the constituent coupon.
  • the preset proportion of each sampling factor may be set according to the importance degree of each sampling factor.
  • the preset proportion of the monthly accumulated volume factor is preferably 50%
  • the bond duration factor is preferably 25%
  • the default ratio of bond issuance scale factors is 25%.
  • the component coupons in the group are scored according to the scoring rules corresponding to the above three sampling factors, and the three scores of each bond are weighted and summed according to the score result and the proportion of each sampling factor. As the final score.
  • the scoring rules can be set by the user as needed.
  • the modified duration of the bond is 0.5-1.1, the score is 10 points, and if the modified duration of the bond is 1.1-1.7, The score is 8 points. If the modified duration of the bond is 1.7-2.3, the score is 6 points. If the modified duration of the bond is 2.3-3, the score is 4 points.
  • the bond's revised duration is 3-3.7, the score is 10 points. If the bond's revised duration is 3.7-4.4, the score is 8 points.
  • the modified duration of the bond is 4.4-5.1, the score is 6 points, and if the modified duration of the bond is 5.1-6, the score is 4 points.
  • the score is 10 points. If the bond's modified duration is 7-7.5, the score is 8 points. The bond's revised duration is 7.5-8.1, and the score is 6 points. If the bond's revised duration is 8.1-9, the score is 4 points.
  • the scoring rules of the issue size and the monthly cumulative volume you can set similar scoring rules by referring to the above-mentioned revised duration scoring rules, and will not repeat them here. The larger the issue size, the higher the score; the larger the monthly cumulative volume The higher the score.
  • the coupons are scored according to the scoring rules of each sampling factor, and three scores can be obtained respectively.
  • the preset proportion is 50%
  • the bond duration factor is preset. 25%
  • the pre-determination ratio of the bond issuance scale factor is 25%
  • the three scores are weighted and summed, and the summation result is used as the final score.
  • the vouchers in each group of bonds are sorted in descending order of final score.
  • the steps of determining the sample quantity of each group of bonds include: determining the total number of constituent vouchers of the single market bond index, the target copy rate, and the copy ratio of each group of bonds; calculating the total sample data according to the total number of the constituent vouchers and the target copy rate Calculating the number of samples of each group of bonds based on the total sampled data and the copy ratio of the groups of bonds.
  • the target copy rate of the single market bond ETF and the copy ratio of each group of bonds are set in advance.
  • the target replication rate is 20%
  • the replication ratio of the bond group with a modified duration of 0-3 years is 50.94%
  • the replication ratio of the bond group with a modified duration of 3-6 years is 40.57%
  • the revised duration is 6-
  • the copy ratio of the 9-year bond group is 7.55%
  • the copy ratio of the bond group with the revised duration of 9 years or more is 0.94%.
  • the copy ratio of each group of bonds is the ratio of the number of sample coupons to be selected in each group of bonds to the total number of sampled bonds.
  • the proportion of the number of sample coupons to be selected in each group of bonds in the total number of constituent bonds of the group of bonds can be calculated.
  • the total number of constituent vouchers in each group of bonds can be determined, and the ratio of the number of sample vouchers to be selected in each group of bonds to the total number of constituent vouchers of the group of bonds can be calculated. The number of samples.
  • Sample bonds are drawn from each group of bonds based on the scores and sample sizes of the constituent bonds in each group of bonds.
  • the highest score in each group is selected as the sample coupon of the sample quantity, that is, the coupon for constructing the single market bond ETF.
  • the transaction income data of the sample bond in the target time interval is obtained, and according to the transaction income data, the weight deviation quadratic programming model is used to calculate the weight of the sample bond.
  • sample bonds are combined according to the calculated weights to form a single market bond ETF corresponding to the target time interval.
  • the process function for calculating the optimal weight of the sample coupon using the income deviation quadratic programming model can be expressed as the following expression 1:
  • Q i is the number of positions opened by bond i at the beginning of the month, and it is also the solution object of this quadratic programming problem. It is calculated to find the optimal number of positions opened at the beginning of each month, that is, the optimal weight of bond i in the bond portfolio. . Assuming a position is opened in early January 2017, December 2016 is the target time interval, and sample coupons are selected based on this month's data and weights are calculated.
  • R T+1 is the bond index of the T-day closing to the T+1 day closing
  • P i T is the closing price of the bond i on the T trading day
  • the P i T+1 bond i is trading at the T+1.
  • n is the total number of trading days in a month
  • m is the total number of sample coupons selected.
  • the bond index can be calculated according to the calculation rules of the bond index.
  • the bond index can be calculated according to the total market value of all the bonds of the bond index on the current trading day, the total market value of all the constituent bonds on the previous trading day, the value of the bond index on the previous trading day, and the preset index calculation.
  • the formula calculates the bond index for the current trading day.
  • R T+1 is the bond index of the current trading day T+1
  • R T is the bond index of the previous trading day T of the current trading day
  • S T+1 is the total market value of all the constituent bonds on the current trading day
  • S T is the total market value of all constituent bonds on the previous trading day.
  • w i is the optimal weight of the sample coupon i, which is the solution object of the expression
  • w i of all sample coupons satisfy the condition: Where w i ⁇ 1,
  • R T+1 is the bond index of T+1 on the trading day.
  • the scheme uses the exhaustive method to calculate the optimal weight of each sample coupon, wherein the exhaustive unit is set to 0.01.
  • the sample coupons When the position is opened in early January 2017, December 2016 will be used as the target time interval. According to the data of this month, the sample coupons will be selected to constitute the holding basket of January 2017, and the optimal weight of each standard ticket in the basket will be calculated according to the above model. The sample coupons in the combination basket according to the calculated optimal weight constitute the January 2017 single market bond ETF.
  • each month of the month can be adjusted according to the transaction situation of the previous month, a new position basket is established, and each sample coupon in the basket is recalculated. Weight, build the new monthly single market bond ETF.
  • the apparatus for constructing the single-market bond ETF proposed in the embodiment obtains the duration data of the component coupon of the single-market bond index in the target time interval, and divides the component coupon according to the acquired duration data and the preset duration interval.
  • Group bond obtain the data corresponding to the sampling factor of the component voucher in the target time interval, and score the component voucher in each group of bonds according to the data corresponding to the obtained sampling factor and the preset scoring rule, wherein the sampling factor includes the duration , cumulative volume and issue size; determine the sample size of each group of bonds; draw sample bonds from each group of bonds according to the scores and sample quantities of the constituent bonds in each group of bonds; obtain the transaction income data of the sample bonds in the target time interval, according to The transaction income data uses the income deviation quadratic programming model to calculate the weight of the sample bonds; the sample weights are combined according to the calculated weights to form a single market bond ETF corresponding to the target time interval.
  • the solution of the present application is a new bond copying logic, which scores the component coupons and extracts a certain amount of sample coupons for tracking, and uses the income deviation quadratic programming model to assign appropriate weights to the sample coupons according to the transaction income data.
  • the ETF construction program may also be divided into one or more modules, and one or more modules are stored in the memory 11 and are composed of one or more processors (this embodiment is The processor 12) is executed to complete the application.
  • a module referred to in the present application refers to a series of computer program instruction segments capable of performing a specific function for describing an execution process of an ETF construction program in a construction apparatus of a single-market bond ETF.
  • FIG. 3 it is a schematic diagram of a program module of an ETF construction program in an embodiment of a device for constructing a single-market bond ETF.
  • the ETF construction program may be divided into a data acquisition module 10, and the quantity is determined.
  • Module 20, sample extraction module 30, weight distribution module 40, and bond combination module 50 by way of example:
  • the data obtaining module 10 is configured to: obtain the duration data of the component coupon of the single market bond index in the target time interval, and divide the component coupon into a plurality of groups of bonds according to the obtained duration data and the preset duration interval.
  • sampling factor includes a long time Period, cumulative volume and issue size.
  • the quantity determining module 20 is configured to: determine the number of samples of each group of bonds.
  • the sample extraction module 30 is configured to: extract sample bonds from each group of bonds according to the scores and sample quantities of the component coupons in each group of bonds.
  • the weight distribution module 40 is configured to: obtain transaction income data of the sample bond in the target time interval, and calculate a weight of the sample bond by using the income deviation quadratic programming model according to the transaction income data.
  • the bond combination module 50 is configured to: combine the sample bonds according to the calculated weights to form a single market bond ETF corresponding to the target time interval.
  • the embodiment of the present application further provides a computer readable storage medium, where the ETF construction program is stored on the computer readable storage medium, and the ETF construction program may be executed by one or more processors to implement the following operations:
  • sampling factor includes duration, Cumulative volume and issue size
  • sample bonds are combined according to the calculated weights to form a single market bond ETF corresponding to the target time interval.
  • the specific embodiment of the computer readable storage medium of the present application is substantially the same as the embodiment of the apparatus and method for constructing the single-market bond ETF described above, and is not described herein.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.
  • a terminal device which may be a mobile phone, a computer, a server, or a network device, etc.

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Abstract

本申请公开了一种单市场债券ETF的构建方法，该方法包括：获取单市场债券指数的成分券的久期数据，将成分券分为多组债券；获取成分券抽样因子对应的数据，并根据抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分；确定各组债券的抽样数量；根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；根据样本债券的交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；按照计算得到的权重组合样本债券，构成单市场债券ETF。本申请还提出一种单市场债券ETF的构建装置以及一种计算机可读存储介质。本申请解决了现有技术中没有适用于信用债的债券ETF的构建方案的技术问题。

Description

债券ETF的构建方法、装置及计算机可读存储介质
本申请基于巴黎公约申明享有2018年05月16日递交的申请号为2018104699742、名称为“债券ETF的构建方法、装置及计算机可读存储介质”的中国专利申请的优先权，该中国专利申请的整体内容以参考的方式结合在本申请中。
技术领域
本申请涉及数据处理技术领域，尤其涉及一种债券ETF的构建方法、装置及计算机可读存储介质。
背景技术
交易所交易基金(Exchange-Traded Funds)，简称ETF，是一种在交易所上市交易的、基金份额可变的开放式基金。ETF按标的资产不同，分为股票ETF，债券ETF等，目前国内市场上，股票ETF较多，债券ETF存在着巨大的空白，而且现有的债券ETF基本是国债为主的债券ETF，信用债ETF非常匮乏，因此，市场上为了推动债券ETF研发，填补市场空白，开发信用债ETF就有了现实的需求。
这就需要一种针对信用债的债券ETF构建方案，实现对该信用债的债券指数的跟踪，但是由于目前的市场上的债券ETF的构建方案主要是针对国债设计的，并不适用于信用债，因此，目前亟需能够适用于信用债的债券ETF构建方案。
发明内容
本申请提供一种债券ETF的构建方法、装置及计算机可读存储介质，其主要目的在于解决现有技术中没有适用于信用债的债券ETF的构建方案的技术问题。
为实现上述目的，本申请还提供一种单市场债券ETF的构建方法，该方法包括：
获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取 的久期数据和预设的久期区间将成分券分为多组债券；
获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模；
确定各组债券的抽样数量；
根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；
获取样本债券在目标时间区间的交易收益数据，根据所述交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；
按照计算得到的权重组合所述样本债券，构成与所述目标时间区间对应的单市场债券ETF。
此外，为实现上述目的，本申请还提供一种单市场债券ETF的构建装置，该装置包括存储器和处理器，所述存储器中存储有可在所述处理器上运行的ETF构建程序，所述ETF构建程序被所述处理器执行时实现如下步骤：
获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券；
获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模；
确定各组债券的抽样数量；
根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；
获取样本债券在目标时间区间的交易收益数据，根据所述交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；
按照计算得到的权重组合所述样本债券，构成与所述目标时间区间对应的单市场债券ETF。
此外，为实现上述目的，本申请还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有ETF构建程序，所述ETF构建程序可被一个或者多个处理器执行，以实现如上所述的单市场债券ETF的构建方法的步骤。
本申请提出的债券ETF的构建方法、装置及计算机可读存储介质，获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券；获取成分券在目标时间区间内 抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模；确定各组债券的抽样数量；根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；获取样本债券在目标时间区间的交易收益数据，根据交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；按照计算得到的权重组合样本债券，构成与目标时间区间对应的单市场债券ETF。本申请的方案一种新的债券复制逻辑，通过对成分券分组评分并抽取一定量的样本券用于跟踪，并且根据交易收益数据，采用收益偏差二次规划模型为样本券分配合适的权重，通过计算的权重组合样本券作为单市场债券ETF，解决了现有技术中没有适用于信用债的债券ETF的构建方案的技术问题。
附图说明
图1为本申请一实施例提供的单市场债券ETF的构建方法的流程示意图；
图2为本申请一实施例提供的单市场债券ETF的构建装置的内部结构示意图；
图3为本申请一实施例提供的单市场债券ETF的构建装置中ETF构建程序的模块示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请提供一种单市场债券ETF的构建方法。参照图1所示，为本申请一实施例提供的单市场债券ETF的构建方法的流程示意图。该方法可以由一个装置执行，该装置可以由软件和/或硬件实现。
在本实施例中，单市场债券ETF的构建方法包括：
步骤S10，获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券。
本申请实施例中以上海证券交易所的单市场债券指数作为分析对象，从该债券指数的成分券中抽取出一定数量的样本券作为跟踪复制的对象，建立上海证券交易所的单市场债券ETF。
其中，上交所的单市场债券指数的成分券是从其所有的上市债券中筛选出的符合预设筛选条件的债券。例如，统计上交所在截止日期之前上市的所有债券，从中筛选出剩余期限在预设时长以上、主体评级和信用评级均在预设级别以上、发行规模在预设数量以上的债券。上述预设时长、预设级别以及预设数量的选择可以根据交易所的上市债券的实际情况进行合理设置。优选地，在一实施例中，预设时长为1年，预设数量为20亿，预设级别为AA级。关于发行规模，基于债券规模越大，资产配置容量越高的逻辑，指数的标的选在债券发行规模20亿以上，为后期的债券ETF复制提供了现实的操作空间。将按照上述条件中筛选出的债券作为上交所单市场债券的成分券。
从预设数据库中获取这些成分券在目标时间区间内的久期数据，在本实施例中，以一个月作为一个时间单元。获取这些成分券在一个月内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券，其中，预设的久期区间是用户预先设置的、用于对成分券分组的依据。
步骤S10可以包括以下细化步骤：
获取单市场债券指数的成分券在目标时间区间内每个交易日的日度久期数据，根据获取的每个交易日的日度久期数据计算平均日度久期数据，将所述平均日度久期数据作为成分券在目标时间区间的久期数据；按照所述成分券在目标时间区间的久期数据和预设的久期区间将成分券分为多组债券。
以下内容以2016年12月作为目标时间单元，以这个月的数据示例。获取2016年12月1日到2016年12月31日中所有指数成分券在交易日的日度久期数据，该数据为修正久期，来源中证指数公司，采用算术平均计算方法对每只债券计算月度平均久期，根据月度平均久期将所有的成分券分为修正久期分别为0-3年、3-6年、6-9年、9年以上，共四个分组。
步骤S20，获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模。
关于抽样因子的设置，本实施例中将债券久期、月度累计成交量以及债券发行规模作为抽样因子，在其他实施例中也可以增加其他的因素作为抽样因子。从数据库中获取每组债券中成分券的上述抽样因子在2016年12月的数据，即成分券的久期数据、发行规模数据和月度累计成交量数据。接下来 根据这些数据和预设的评分规则对各组债券中的成分券进行评分。具体地，在一实施例中，步骤S20可以包括以下细化步骤：
获取成分券在所述目标时间区间内抽样因子对应的数据；
分别按照各抽样因子对应的预设评分规则对各抽样因子对应的数据进行评分，得到各抽样因子对应的评分；
根据各抽样因子对应的评分以及各抽样因子的预设占比，计算第一评分的加权平均分数，将所述加权评分分数作为成分券的评分。
其中，各抽样因子的预设占比可以根据各抽样因子的重要程度来设置，例如，在一实施例中，月度累计成交量因子的预设占比优选地为50％，债券久期因子和债券发行规模因子的预设占比分别为25％。针对每个债券分组，分别按照上述三个抽样因子对应的评分规则对分组内的成分券进行评分，根据评分结果以及各个抽样因子的占比，对每个债券的三个评分值进行加权求和，作为最终的评分。其中，评分规则可以由用户根据需要设置。以修正久期为例，对于修正久期为0-3年的债券分组来说，若债券的修正久期为0.5-1.1，则评分为10分，若债券的修正久期为1.1-1.7，则评分为8分，若债券的修正久期为1.7-2.3，则评分为6分，若债券的修正久期为2.3-3，则评分为4分。对于修正久期为3-6年的债券分组来说，若债券的修正久期为3-3.7，则评分为10分，若债券的修正久期为3.7-4.4，则评分为8分，若债券的修正久期为4.4-5.1，则评分为6分，若债券的修正久期为5.1-6，则评分为4分。对于修正久期为6-9年的债券分组来说，若债券的修正久期为6-7，则评分为10分，若债券的修正久期为7-7.5，则评分为8分，若债券的修正久期为7.5-8.1，则评分为6分，若债券的修正久期为8.1-9，则评分为4分。关于发行规模和月度累计成交量的评分规则，可以参照上述修正久期的评分规则设置类似的评分规则，在此不再赘述，其中，发行规模越大，分数越高；月度累计成交量越大，分数越高。
对于每组债券来说，按照各个抽样因子的评分规则对成分券进行评分，可以分别得到三个评分，根据月度累计成交量因子的预设占比50％、债券久期因子的预设占比25％、债券发行规模因子的预设占比25％对三个评分进行加权求和，将求和结果作为最终评分。将各组债券中的成分券按照最终评分由高至低的顺序排序。
步骤S30，确定各组债券的抽样数量。
确定各组债券的抽样数量的步骤包括：确定单市场债券指数的成分券总数量、目标复制率以及各组债券的复制比例；根据所述成分券总数量和所述目标复制率计算总抽样数据；根据所述总抽样数据和所述各组债券的复制比例计算各组债券的抽样数量。
预先设置好单市场债券ETF的目标复制率以及各组债券的复制比例。假设目标复制率为20％，修正久期为0-3年的债券分组的复制比例为50.94％，修正久期为3-6年的债券分组的复制比例为40.57％，修正久期为6-9年的债券分组的复制比例为7.55％，修正久期为9年以上的债券分组的复制比例为0.94％。上述各组债券的复制比例为各个分组债券中要抽选出的样本券的数量占抽样债券总数的比例。根据目标复制率以及各组债券的复制比例可以计算得到每组债券中要抽选出的样本券的数量在该组债券的成分券总数中所占的比例。根据分组结果可以确定每组债券中成分券的总数量，再结合每组债券中要抽选出的样本券的数量在该组债券的成分券总数中所占的比例，可以计算出各组债券的抽样数量。
步骤S40，根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券。
根据各组债券中成分券的评分，选择每个分组中评分最高的数量等于抽样数量的成分券作为样本券，即用于构建单市场债券ETF的标的券。
步骤S50，获取样本债券在目标时间区间的交易收益数据，根据所述交易收益数据，采用收益偏差二次规划模型计算样本债券的权重。
步骤S60，按照计算得到的权重组合所述样本债券，构成与所述目标时间区间对应的单市场债券ETF。
具体地，采用收益偏差二次规划模型计算样本券的最优权重的过程用函数可以表示为下述表达式1：
Figure PCTCN2018102136-appb-000001
其中，Q i为月初建仓时债券i的建仓数量，也是这个二次规划问题的求解对象，通过计算找出每个月初建仓时最优的建仓数量，即债券i在债券组合中的最优权重。假设在2017年1月初建仓，则2016年12月为目标时间区间， 根据这一个月的数据选择样本券并计算权重。上述表达式1中，R T+1为T日收盘到T+1日收盘的债券指数，P i T为债券i在T交易日的收盘价，P i T+1债券i在T+1交易日的收盘价。n为一个月度内交易日的总数量，m为筛选出的样本券的总数量。
其中，债券指数可以根据债券指数的计算规则计算得到。债券指数的计算规则可以为：根据债券指数的全部成分券在当前交易日的总市值、全部成分券在前一个交易日的总市值、前一个交易日的债券指数的数值以及预设的指数计算公式计算当前交易日的债券指数。
Figure PCTCN2018102136-appb-000002
其中，R T+1为当前交易日T+1的债券指数，R T为当前交易日的前一个交易日T的债券指数，S T+1为全部成分券在当前交易日的总市值，S T为全部成分券在前一个交易日的总市值。
进一步地，该方案中，为了提高计算机处理数据的效率，同时避免求解绝对数量的参数解，求得更加准确的权重数值，对上述表达式1进行等价变换，变换过程如下：
Figure PCTCN2018102136-appb-000003
同理：
Figure PCTCN2018102136-appb-000004
上述转换中，
Figure PCTCN2018102136-appb-000005
为每月初建(调)仓时，债券i的最优权重，即为要求解的对象，对于所有样本券来说，满足条件：
Figure PCTCN2018102136-appb-000006
经过上述转换，将原来的二次规划求解问题由求绝对数量的参数变成求解相对数量的参数，可以将上述表达式1转换为如下表达式2：
Figure PCTCN2018102136-appb-000007
其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
Figure PCTCN2018102136-appb-000008
其中，w i≥1，
Figure PCTCN2018102136-appb-000009
为样本券i在交易日T的收益率，
Figure PCTCN2018102136-appb-000010
为样本券i在交易日T+1的收益率，可以直接从数据库中提取。R T+1为交易日T+1的债券指数。对于上述表达式的求解，本方案采用穷举法计算出各个样本券的最优权重，其中，穷举的单位设置为0.01。
在2017年1月初建仓时，将2016年12月作为目标时间区间，根据这个月的数据筛选出样本券构成2017年1月的持仓篮子，按照上述模型计算篮子中各个标的券的最优权重，按照计算出的最优权重组合持仓篮子中的样本券构成2017年1月度的单市场债券ETF。
进一步地，由于在月度之间可能存在调仓的变动，因此，每个月度的月初可以根据上个月的交易情况进行一次调仓，建立新的持仓篮子，并重新计算篮子中各个样本券的权重，构建该月度新的单市场债券ETF。
本实施例提出的单市场债券ETF的构建方法，获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券；获取成分券在目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模；确定各组债券的抽样数量；根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；获取样本债券在目标时间区间的交易收益数据，根据交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；按照计算得到的权重组合样本债券，构成与目标时间区间对应的单市场债券ETF。本申请的方案一种新的债券复制逻辑，通过对成分券分组评分并抽取一定量的样本券用于跟踪，并且根据交易收益数据，采用收益偏差二次规划模型为样本券分配合适的权重，通过计算的权重组合样本券作为单市场债券ETF，解决了现有技术中没有适用于信用债的债券ETF的构建方案的技术问题。
本申请还提供一种单市场债券ETF的构建装置。参照图2所示，为本申请一实施例提供的单市场债券ETF的构建装置的内部结构示意图。
在本实施例中，单市场债券ETF的构建装置1可以是PC(Personal Computer，个人电脑)，也可以是智能手机、平板电脑、便携计算机等终端设备。该单市场债券ETF的构建装置1至少包括存储器11、处理器12，网络接 口13，以及通信总线14。
其中，存储器11至少包括一种类型的可读存储介质，所述可读存储介质包括闪存、硬盘、多媒体卡、卡型存储器(例如，SD或DX存储器等)、磁性存储器、磁盘、光盘等。存储器11在一些实施例中可以是单市场债券ETF的构建装置1的内部存储单元，例如该单市场债券ETF的构建装置1的硬盘。存储器11在另一些实施例中也可以是单市场债券ETF的构建装置1的外部存储设备，例如单市场债券ETF的构建装置1上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。进一步地，存储器11还可以既包括单市场债券ETF的构建装置1的内部存储单元也包括外部存储设备。存储器11不仅可以用于存储安装于单市场债券ETF的构建装置1的应用软件及各类数据，例如ETF构建程序01的代码等，还可以用于暂时地存储已经输出或者将要输出的数据。
处理器12在一些实施例中可以是一中央处理器(Central Processing Unit,CPU)、控制器、微控制器、微处理器或其他数据处理芯片，用于运行存储器11中存储的程序代码或处理数据，例如执行ETF构建程序01等。
网络接口13可选的可以包括标准的有线接口、无线接口(如WI-FI接口)，通常用于在该装置1与其他电子设备之间建立通信连接。
通信总线14用于实现这些组件之间的连接通信。
可选地，该装置1还可以包括用户接口，用户接口可以包括显示器(Display)、输入单元比如键盘(Keyboard)，可选的用户接口还可以包括标准的有线接口、无线接口。可选地，在一些实施例中，显示器可以是LED显示器、液晶显示器、触控式液晶显示器以及OLED(Organic Light-Emitting Diode，有机发光二极管)触摸器等。其中，显示器也可以适当的称为显示屏或显示单元，用于显示在单市场债券ETF的构建装置1中处理的信息以及用于显示可视化的用户界面。
图2仅示出了具有组件11-14以及ETF构建程序01的单市场债券ETF的构建装置1，本领域技术人员可以理解的是，图1示出的结构并不构成对单市场债券ETF的构建装置1的限定，可以包括比图示更少或者更多的部件，或者组合某些部件，或者不同的部件布置。
在图2所示的装置1实施例中，存储器11中存储有ETF构建程序01； 处理器12执行存储器11中存储的ETF构建程序01时实现如下步骤：
获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券。
本申请实施例中以上海证券交易所的单市场债券指数作为分析对象，从该债券指数的成分券中抽取出一定数量的样本券作为跟踪复制的对象，建立上海证券交易所的单市场债券ETF。
其中，上交所的单市场债券指数的成分券是从其所有的上市债券中筛选出的符合预设筛选条件的债券。例如，统计上交所在截止日期之前上市的所有债券，从中筛选出剩余期限在预设时长以上、主体评级和信用评级均在预设级别以上、发行规模在预设数量以上的债券。上述预设时长、预设级别以及预设数量的选择可以根据交易所的上市债券的实际情况进行合理设置。优选地，在一实施例中，预设时长为1年，预设数量为20亿，预设级别为AA级。关于发行规模，基于债券规模越大，资产配置容量越高的逻辑，指数的标的选在债券发行规模20亿以上，为后期的债券ETF复制提供了现实的操作空间。将按照上述条件中筛选出的债券作为上交所单市场债券的成分券。
从预设数据库中获取这些成分券在目标时间区间内的久期数据，在本实施例中，以一个月作为一个时间单元。获取这些成分券在一个月内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券，其中，预设的久期区间是用户预先设置的、用于对成分券分组的依据。
获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券的步骤可以包括以下细化步骤：
获取单市场债券指数的成分券在目标时间区间内每个交易日的日度久期数据，根据获取的每个交易日的日度久期数据计算平均日度久期数据，将所述平均日度久期数据作为成分券在目标时间区间的久期数据；按照所述成分券在目标时间区间的久期数据和预设的久期区间将成分券分为多组债券。
以下内容以2016年12月作为目标时间单元，以这个月的数据示例。获取2016年12月1日到2016年12月31日中所有指数成分券在交易日的日度久期数据，该数据为修正久期，来源中证指数公司，采用算术平均计算方法对每只债券计算月度平均久期，根据月度平均久期将所有的成分券分为修正 久期分别为0-3年、3-6年、6-9年、9年以上，共四个分组。
获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模。
关于抽样因子的设置，本实施例中将债券久期、月度累计成交量以及债券发行规模作为抽样因子，在其他实施例中也可以增加其他的因素作为抽样因子。从数据库中获取每组债券中成分券的上述抽样因子在2016年12月的数据，即成分券的久期数据、发行规模数据和月度累计成交量数据。接下来根据这些数据和预设的评分规则对各组债券中的成分券进行评分。具体地，在一实施例中，获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分的步骤可以包括以下细化步骤：
获取成分券在所述目标时间区间内抽样因子对应的数据；
分别按照各抽样因子对应的预设评分规则对各抽样因子对应的数据进行评分，得到各抽样因子对应的评分；
根据各抽样因子对应的评分以及各抽样因子的预设占比，计算第一评分的加权平均分数，将所述加权评分分数作为成分券的评分。
其中，各抽样因子的预设占比可以根据各抽样因子的重要程度来设置，例如，在一实施例中，月度累计成交量因子的预设占比优选地为50％，债券久期因子和债券发行规模因子的预设占比分别为25％。针对每个债券分组，分别按照上述三个抽样因子对应的评分规则对分组内的成分券进行评分，根据评分结果以及各个抽样因子的占比，对每个债券的三个评分值进行加权求和，作为最终的评分。其中，评分规则可以由用户根据需要设置。以修正久期为例，对于修正久期为0-3年的债券分组来说，若债券的修正久期为0.5-1.1，则评分为10分，若债券的修正久期为1.1-1.7，则评分为8分，若债券的修正久期为1.7-2.3，则评分为6分，若债券的修正久期为2.3-3，则评分为4分。对于修正久期为3-6年的债券分组来说，若债券的修正久期为3-3.7，则评分为10分，若债券的修正久期为3.7-4.4，则评分为8分，若债券的修正久期为4.4-5.1，则评分为6分，若债券的修正久期为5.1-6，则评分为4分。对于修正久期为6-9年的债券分组来说，若债券的修正久期为6-7，则评分为10分， 若债券的修正久期为7-7.5，则评分为8分，若债券的修正久期为7.5-8.1，则评分为6分，若债券的修正久期为8.1-9，则评分为4分。关于发行规模和月度累计成交量的评分规则，可以参照上述修正久期的评分规则设置类似的评分规则，在此不再赘述，其中，发行规模越大，分数越高；月度累计成交量越大，分数越高。
对于每组债券来说，按照各个抽样因子的评分规则对成分券进行评分，可以分别得到三个评分，根据月度累计成交量因子的预设占比50％、债券久期因子的预设占比25％、债券发行规模因子的预设占比25％对三个评分进行加权求和，将求和结果作为最终评分。将各组债券中的成分券按照最终评分由高至低的顺序排序。
确定各组债券的抽样数量。
确定各组债券的抽样数量的步骤包括：确定单市场债券指数的成分券总数量、目标复制率以及各组债券的复制比例；根据所述成分券总数量和所述目标复制率计算总抽样数据；根据所述总抽样数据和所述各组债券的复制比例计算各组债券的抽样数量。
预先设置好单市场债券ETF的目标复制率以及各组债券的复制比例。假设目标复制率为20％，修正久期为0-3年的债券分组的复制比例为50.94％，修正久期为3-6年的债券分组的复制比例为40.57％，修正久期为6-9年的债券分组的复制比例为7.55％，修正久期为9年以上的债券分组的复制比例为0.94％。上述各组债券的复制比例为各个分组债券中要抽选出的样本券的数量占抽样债券总数的比例。根据目标复制率以及各组债券的复制比例可以计算得到每组债券中要抽选出的样本券的数量在该组债券的成分券总数中所占的比例。根据分组结果可以确定每组债券中成分券的总数量，再结合每组债券中要抽选出的样本券的数量在该组债券的成分券总数中所占的比例，可以计算出各组债券的抽样数量。
根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券。
根据各组债券中成分券的评分，选择每个分组中评分最高的数量等于抽样数量的成分券作为样本券，即用于构建单市场债券ETF的标的券。
获取样本债券在目标时间区间的交易收益数据，根据所述交易收益数据，采用收益偏差二次规划模型计算样本债券的权重。
按照计算得到的权重组合所述样本债券，构成与所述目标时间区间对应的单市场债券ETF。
具体地，采用收益偏差二次规划模型计算样本券的最优权重的过程用函数可以表示为下述表达式1：
Figure PCTCN2018102136-appb-000011
其中，Q i为月初建仓时债券i的建仓数量，也是这个二次规划问题的求解对象，通过计算找出每个月初建仓时最优的建仓数量，即债券i在债券组合中的最优权重。假设在2017年1月初建仓，则2016年12月为目标时间区间，根据这一个月的数据选择样本券并计算权重。上述表达式1中，R T+1为T日收盘到T+1日收盘的债券指数，P i T为债券i在T交易日的收盘价，P i T+1债券i在T+1交易日的收盘价。n为一个月度内交易日的总数量，m为筛选出的样本券的总数量。
其中，债券指数可以根据债券指数的计算规则计算得到。债券指数的计算规则可以为：根据债券指数的全部成分券在当前交易日的总市值、全部成分券在前一个交易日的总市值、前一个交易日的债券指数的数值以及预设的指数计算公式计算当前交易日的债券指数。
Figure PCTCN2018102136-appb-000012
其中，R T+1为当前交易日T+1的债券指数，R T为当前交易日的前一个交易日T的债券指数，S T+1为全部成分券在当前交易日的总市值，S T为全部成分券在前一个交易日的总市值。
进一步地，为了提高计算机处理数据的效率，同时避免求解绝对数量的参数解，求得更加准确的权重数值，对上述表达式1进行等价变换，变换过程如下：
Figure PCTCN2018102136-appb-000013
同理：
Figure PCTCN2018102136-appb-000014
上述转换中，
Figure PCTCN2018102136-appb-000015
为每月初建(调)仓时，债券i的最优权重，即为要求解的对象，对于所有样本券来说，满足条件：
Figure PCTCN2018102136-appb-000016
经过上述转换，将原来的二次规划求解问题由求绝对数量的参数变成求解相对数量的参数，可以将上述表达式1转换为如下表达式2：
Figure PCTCN2018102136-appb-000017
其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
Figure PCTCN2018102136-appb-000018
其中，w i≥1，
Figure PCTCN2018102136-appb-000019
为样本券i在交易日T的收益率，
Figure PCTCN2018102136-appb-000020
为样本券i在交易日T+1的收益率，可以直接从数据库中提取。R T+1为交易日T+1的债券指数。对于上述表达式的求解，本方案采用穷举法计算出各个样本券的最优权重，其中，穷举的单位设置为0.01。
在2017年1月初建仓时，将2016年12月作为目标时间区间，根据这个月的数据筛选出样本券构成2017年1月的持仓篮子，按照上述模型计算篮子中各个标的券的最优权重，按照计算出的最优权重组合持仓篮子中的样本券构成2017年1月度的单市场债券ETF。
进一步地，由于在月度之间可能存在调仓的变动，因此，每个月度的月初可以根据上个月的交易情况进行一次调仓，建立新的持仓篮子，并重新计算篮子中各个样本券的权重，构建该月度新的单市场债券ETF。
本实施例提出的单市场债券ETF的构建装置，获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券；获取成分券在目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模；确定各组债券的抽样数量；根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；获取样本债券在目标时间区间的交易收益数据，根据交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；按照计算得到的权重组合样本债券，构成与目标时间区间对应的单市场债券ETF。本申请的方案一种新的债券复制逻辑，通过对成分券分组评分并抽取一定量的样本券用于 跟踪，并且根据交易收益数据，采用收益偏差二次规划模型为样本券分配合适的权重，通过计算的权重组合样本券作为单市场债券ETF，解决了现有技术中没有适用于信用债的债券ETF的构建方案的技术问题。
可选地，在其他的实施例中，ETF构建程序还可以被分割为一个或者多个模块，一个或者多个模块被存储于存储器11中，并由一个或多个处理器(本实施例为处理器12)所执行以完成本申请，本申请所称的模块是指能够完成特定功能的一系列计算机程序指令段，用于描述ETF构建程序在单市场债券ETF的构建装置中的执行过程。
例如，参照图3所示，为本申请单市场债券ETF的构建装置一实施例中的ETF构建程序的程序模块示意图，该实施例中，ETF构建程序可以被分割为数据获取模块10、数量确定模块20、样本抽取模块30、权重分配模块40和债券组合模块50，示例性地：
数据获取模块10用于：获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券。
以及，获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模。
数量确定模块20用于：确定各组债券的抽样数量。
样本抽取模块30用于：根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券。
权重分配模块40用于：获取样本债券在目标时间区间的交易收益数据，根据所述交易收益数据，采用收益偏差二次规划模型计算样本债券的权重。
债券组合模块50用于：按照计算得到的权重组合所述样本债券，构成与所述目标时间区间对应的单市场债券ETF。
上述数据获取模块10、数量确定模块20、样本抽取模块30、权重分配模块40和债券组合模块50等程序模块被执行时所实现的功能或操作步骤与上述实施例大体相同，在此不再赘述。
此外，本申请实施例还提出一种计算机可读存储介质，所述计算机可读存储介质上存储有ETF构建程序，所述ETF构建程序可被一个或多个处理器执行，以实现如下操作：
获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券；
获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模；
确定各组债券的抽样数量；
根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；
获取样本债券在目标时间区间的交易收益数据，根据所述交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；
按照计算得到的权重组合所述样本债券，构成与所述目标时间区间对应的单市场债券ETF。
本申请计算机可读存储介质具体实施方式与上述单市场债券ETF的构建装置和方法各实施例基本相同，在此不作累述。
需要说明的是，上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。并且本文中的术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、装置、物品或者方法不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、装置、物品或者方法所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、装置、物品或者方法中还存在另外的相同要素。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种单市场债券ETF的构建方法，其特征在于，所述方法包括：
    获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券；
    获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模；
    确定各组债券的抽样数量；
    根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；
    获取样本债券在目标时间区间的交易收益数据，根据所述交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；
    按照计算得到的权重组合所述样本债券，构成与所述目标时间区间对应的单市场债券ETF。
  2. 如权利要求1所述的单市场债券ETF的构建方法，其特征在于，所述获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券的步骤包括：
    获取单市场债券指数的成分券在目标时间区间内每个交易日的日度久期数据，根据获取的每个交易日的日度久期数据计算平均日度久期数据，将所述平均日度久期数据作为成分券在目标时间区间的久期数据；
    按照所述成分券在目标时间区间的久期数据和预设的久期区间将成分券分为多组债券。
  3. 如权利要求1所述的单市场债券ETF的构建方法，其特征在于，所述确定各组债券的抽样数量的步骤包括：
    确定单市场债券指数的成分券总数量、目标复制率以及各组债券的复制比例；
    根据所述成分券总数量和所述目标复制率计算总抽样数据；
    根据所述总抽样数据和所述各组债券的复制比例计算各组债券的抽样数量。
  4. 如权利要求1所述的单市场债券ETF的构建方法，其特征在于，所述获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样 因子对应的数据和预设的评分规则对各组债券中的成分券进行评分的步骤包括：
    获取成分券在所述目标时间区间内抽样因子对应的数据；
    分别按照各抽样因子对应的预设评分规则对各抽样因子对应的数据进行评分，得到各抽样因子对应的评分；
    根据各抽样因子对应的评分以及各抽样因子的预设占比，计算第一评分的加权平均分数，将所述加权评分分数作为成分券的评分。
  5. 如权利要求1所述的单市场债券ETF的构建方法，其特征在于，所述收益偏差二次规划模型的表达式为：
    Figure PCTCN2018102136-appb-100001
    其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
    Figure PCTCN2018102136-appb-100002
    其中，
    Figure PCTCN2018102136-appb-100003
    为样本券i在交易日T的收益率，R T+1为交易日T+1的债券指数。
  6. 如权利要求2所述的单市场债券ETF的构建方法，其特征在于，所述收益偏差二次规划模型的表达式为：
    Figure PCTCN2018102136-appb-100004
    其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
    Figure PCTCN2018102136-appb-100005
    其中，
    Figure PCTCN2018102136-appb-100006
    为样本券i在交易日T的收益率，R T+1为交易日T+1的债券指数。
  7. 如权利要求3所述的单市场债券ETF的构建方法，其特征在于，所述收益偏差二次规划模型的表达式为：
    Figure PCTCN2018102136-appb-100007
    其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
    Figure PCTCN2018102136-appb-100008
    其中，
    Figure PCTCN2018102136-appb-100009
    为样本券i在交易日T的收益率，R T+1为交易日T+1的债券指数。
  8. 一种单市场债券ETF的构建装置，其特征在于，所述装置包括存储器和处理器，所述存储器上存储有可在所述处理器上运行的ETF构建程序，所述ETF构建程序被所述处理器执行时实现如下步骤：
    获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券；
    获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模；
    确定各组债券的抽样数量；
    根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；
    获取样本债券在目标时间区间的交易收益数据，根据所述交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；
    按照计算得到的权重组合所述样本债券，构成与所述目标时间区间对应的单市场债券ETF。
  9. 如权利要求8所述的单市场债券ETF的构建装置，其特征在于，所述获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券的步骤包括：
    获取单市场债券指数的成分券在目标时间区间内每个交易日的日度久期数据，根据获取的每个交易日的日度久期数据计算平均日度久期数据，将所述平均日度久期数据作为成分券在目标时间区间的久期数据；
    按照所述成分券在目标时间区间的久期数据和预设的久期区间将成分券分为多组债券。
  10. 如权利要求8所述的单市场债券ETF的构建装置，其特征在于，所述确定各组债券的抽样数量的步骤包括：
    确定单市场债券指数的成分券总数量、目标复制率以及各组债券的复制比例；
    根据所述成分券总数量和所述目标复制率计算总抽样数据；
    根据所述总抽样数据和所述各组债券的复制比例计算各组债券的抽样数量。
  11. 如权利要求8所述的单市场债券ETF的构建装置，其特征在于，所述获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分的步骤包括：
    获取成分券在所述目标时间区间内抽样因子对应的数据；
    分别按照各抽样因子对应的预设评分规则对各抽样因子对应的数据进行评分，得到各抽样因子对应的评分；
    根据各抽样因子对应的评分以及各抽样因子的预设占比，计算第一评分的加权平均分数，将所述加权评分分数作为成分券的评分。
  12. 如权利要求8所述的单市场债券ETF的构建装置，其特征在于，所述收益偏差二次规划模型的表达式为：
    Figure PCTCN2018102136-appb-100010
    其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
    Figure PCTCN2018102136-appb-100011
    其中，
    Figure PCTCN2018102136-appb-100012
    为样本券i在交易日T的收益率，R T+1为交易日T+1的债券指数。
  13. 如权利要求9所述的单市场债券ETF的构建装置，其特征在于，所述收益偏差二次规划模型的表达式为：
    Figure PCTCN2018102136-appb-100013
    其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
    Figure PCTCN2018102136-appb-100014
    其中，
    Figure PCTCN2018102136-appb-100015
    为样本券i在交易日T的收益率，R T+1为交易日T+1的债券指数。
  14. 如权利要求10所述的单市场债券ETF的构建装置，其特征在于，所述收益偏差二次规划模型的表达式为：
    Figure PCTCN2018102136-appb-100016
    其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
    Figure PCTCN2018102136-appb-100017
    其中，
    Figure PCTCN2018102136-appb-100018
    为样本券i在交易日T的收益率，R T+1为交易日T+1的债券指数。
  15. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有ETF构建程序，所述ETF构建程序可被一个或者多个处理器执行，以下步骤：
    获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券；
    获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分，其中，抽样因子包括久期、累计成交量和发行规模；
    确定各组债券的抽样数量；
    根据各组债券中成分券的评分和抽样数量从各组债券中抽取样本债券；
    获取样本债券在目标时间区间的交易收益数据，根据所述交易收益数据，采用收益偏差二次规划模型计算样本债券的权重；
    按照计算得到的权重组合所述样本债券，构成与所述目标时间区间对应的单市场债券ETF。
  16. 如权利要求15所述的计算机可读存储介质，其特征在于，所述获取单市场债券指数的成分券在目标时间区间内的久期数据，根据获取的久期数据和预设的久期区间将成分券分为多组债券的步骤包括：
    获取单市场债券指数的成分券在目标时间区间内每个交易日的日度久期数据，根据获取的每个交易日的日度久期数据计算平均日度久期数据，将所述平均日度久期数据作为成分券在目标时间区间的久期数据；
    按照所述成分券在目标时间区间的久期数据和预设的久期区间将成分券分为多组债券。
  17. 如权利要求15所述的计算机可读存储介质，其特征在于，所述确定各组债券的抽样数量的步骤包括：
    确定单市场债券指数的成分券总数量、目标复制率以及各组债券的复制比例；
    根据所述成分券总数量和所述目标复制率计算总抽样数据；
    根据所述总抽样数据和所述各组债券的复制比例计算各组债券的抽样数量。
  18. 如权利要求15所述的计算机可读存储介质，其特征在于，所述获取成分券在所述目标时间区间内抽样因子对应的数据，并根据获取的抽样因子对应的数据和预设的评分规则对各组债券中的成分券进行评分的步骤包括：
    获取成分券在所述目标时间区间内抽样因子对应的数据；
    分别按照各抽样因子对应的预设评分规则对各抽样因子对应的数据进行评分，得到各抽样因子对应的评分；
    根据各抽样因子对应的评分以及各抽样因子的预设占比，计算第一评分的加权平均分数，将所述加权评分分数作为成分券的评分。
  19. 如权利要求15所述的计算机可读存储介质，其特征在于，所述收益偏差二次规划模型的表达式为：
    Figure PCTCN2018102136-appb-100019
    其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
    Figure PCTCN2018102136-appb-100020
    其中，
    Figure PCTCN2018102136-appb-100021
    为样本券i在交易日T的收益率，R T+1为交易日T+1的债券指数。
  20. 如权利要求16所述的计算机可读存储介质，其特征在于，所述收益偏差二次规划模型的表达式为：
    Figure PCTCN2018102136-appb-100022
    其中，w i为样本券i的最优权重，为所述表达式的求解对象，所有样本券的w i满足条件：
    Figure PCTCN2018102136-appb-100023
    其中，
    Figure PCTCN2018102136-appb-100024
    为样本券i在交易日T的收益率，R T+1为交易日T+1的债券指数。
PCT/CN2018/102136 2018-05-16 2018-08-24 债券etf的构建方法、装置及计算机可读存储介质 WO2019218518A1 (zh)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090248561A1 (en) * 2005-09-28 2009-10-01 Extabs Pty Ltd Exchange traded asset based security
CN105339973A (zh) * 2013-03-15 2016-02-17 芝加哥期权交易所 创建政府债券波动率指数和基于政府债券波动率指数的交易衍生的方法和系统
CN105931119A (zh) * 2016-05-17 2016-09-07 中国建设银行股份有限公司 一种债券报价处理方法及装置
CN107103429A (zh) * 2017-05-03 2017-08-29 辽宁科技大学 债券代持交易数据的管理方法及系统

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090248561A1 (en) * 2005-09-28 2009-10-01 Extabs Pty Ltd Exchange traded asset based security
CN105339973A (zh) * 2013-03-15 2016-02-17 芝加哥期权交易所 创建政府债券波动率指数和基于政府债券波动率指数的交易衍生的方法和系统
CN105931119A (zh) * 2016-05-17 2016-09-07 中国建设银行股份有限公司 一种债券报价处理方法及装置
CN107103429A (zh) * 2017-05-03 2017-08-29 辽宁科技大学 债券代持交易数据的管理方法及系统

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