CN112381322A - Method and system for risk early warning based on option calculation fluctuation rate index - Google Patents

Method and system for risk early warning based on option calculation fluctuation rate index Download PDF

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CN112381322A
CN112381322A CN202011364170.XA CN202011364170A CN112381322A CN 112381322 A CN112381322 A CN 112381322A CN 202011364170 A CN202011364170 A CN 202011364170A CN 112381322 A CN112381322 A CN 112381322A
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fluctuation rate
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安润民
王宁
朱珺
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Shanghai Jiufangyun Intelligent Technology Co ltd
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Abstract

The invention provides a method and a system for carrying out risk early warning based on option calculation fluctuation rate index, which comprises the following steps: adopting all trading option data meeting preset requirements; preprocessing all collected option data meeting preset requirements and being traded, and storing the acquired option data to a server or a local hard disk; calculating the corresponding implicit fluctuation rate of each option by using a BS option pricing formula and a dichotomy according to the stored option data; calculating to obtain the current-day fluctuation rate index according to the implicit fluctuation rate, and storing the current-day fluctuation rate index to a server or a local hard disk; judging whether the fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index deviates from a preset value or not; pre-judging the increase or decrease of the transaction amount according to the fluctuation rate risk; and determining potential hot spot transactions of the transactions according to the increase or decrease of the transaction amount, and determining database hot spots of the transaction system according to the potential hot spot transactions. The invention reduces the error and subjective influence which may be brought by human intervention.

Description

Method and system for risk early warning based on option calculation fluctuation rate index
Technical Field
The invention relates to an option trading algorithm, in particular to a method and a system for carrying out risk early warning based on option calculation fluctuation rate indexes, and more particularly to a method and a system for realizing measurement and early warning of future financial market fluctuation rate risks based on fluctuation rate index calculation.
Background
A fluctuation rate index (VIX index, panic index) compiling method of a Chicago option exchange (CBOE) is a main algorithm for compiling the fluctuation rate index at present, the method calculates the fluctuation rate index by applying an academic top-difference interchange pricing principle, a certain deviation exists between the algorithm of the CBOE and the theory, mainly a truncation error and a dispersion error, and in reality, the truncation error and the dispersion error cannot be avoided, so that the deviation of a calculation result to a real value is overlarge. The core of the fluctuation rate index compiling method is to reduce calculation errors as much as possible.
The main disadvantages of the prior art are: in the conventional VIX calculation method of the CBOE, strict data screening standards are adopted when the fluctuation rate index is calculated, so that too few samples can be calculated, and the deviation of a calculation result is increased.
The method and the device avoid the defects in the prior art and improve the calculation accuracy by improving the fluctuation rate index calculation formula.
Patent document CN104809647A (application number: 201410042263.9) discloses a fluctuation rate index compilation method, which requires the steps of selecting option contract months, calculating the remaining duration of the selected months, calculating the risk-free interest rate of the current month and the next month, determining option contract prices, calculating the future price of the current month and the next month, screening contracts, calculating the fluctuation rate of two months, and calculating the fluctuation rate index in sequence.
Patent document CN105339973A (application No. 201380075864.3) discloses a computer system for calculating a government bond volatility index, comprising a memory configured to store at least one program; and at least one processor communicatively coupled to the memory, wherein the at least one program, when executed by the at least one processor, causes the at least one processor to: receiving data regarding options derived from government bonds; calculating a government bond volatility index using data on options derived from government bonds; and transmitting data regarding the government bond volatility index.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for carrying out risk early warning based on option calculation fluctuation rate index.
The invention provides a risk early warning method based on option calculation fluctuation rate index, which comprises the following steps:
step M1: acquiring all option data meeting preset requirements in trading from a data service provider and the trading by adopting an internet intelligent information acquisition technology and a data interface;
step M2: preprocessing all collected option data meeting preset requirements and being traded, and storing the preprocessed option data to a server or a local hard disk;
step M3: calculating corresponding implicit fluctuation rate for each option by using a BS option pricing formula and a dichotomy for option data stored in a server or a local hard disk;
step M4: calculating to obtain a current-day fluctuation rate index according to the corresponding implicit fluctuation rate of each option, and storing the calculated current-day fluctuation rate index to a server or a local hard disk;
step M5: judging whether a fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index stored in the server or the local hard disk deviates from a preset value or not, and transmitting the fluctuation rate risk to a result display end;
step M6: according to the predicted fluctuation rate risk, pre-judging fluctuation of the market conditions, and further predicting the data request pressure of the database;
step M7: according to the predicted data request pressure of the database, the calculation power and computer resources of the database are set in advance, the calculation power distribution of the database is improved, and the corresponding data acquisition time is shortened.
Preferably, the preprocessing in the step M2 includes missing value complementing, discarding and cleaning data.
Preferably, the step M4 includes:
Figure BDA0002804940350000021
Figure BDA0002804940350000022
Figure BDA0002804940350000023
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents the daily fluctuation rate index; volume (i) represents the transaction amount; i denotes the ith option.
Preferably, step M4 includes:
Figure BDA0002804940350000031
Figure BDA0002804940350000032
Figure BDA0002804940350000033
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents the daily fluctuation rate index; openinterest (i) represents the amount of unbinned bins on the day; i tableThe ith option is shown.
Preferably, the step M5 includes: judging whether the fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index deviates from a preset value or not; when the volatility index deviates from a preset value, predicting that the volatility risk exists in the future preset time; and when the volatility index does not deviate from the preset value, predicting that no volatility risk exists in the future preset time, and constructing a natural language readable output character string according to the volatility risk and displaying the result through a display end.
The invention provides a risk early warning system based on option calculation fluctuation rate index, which comprises:
module M1: acquiring all option data meeting preset requirements in trading from a data service provider and the trading by adopting an internet intelligent information acquisition technology and a data interface;
module M2: preprocessing all collected option data meeting preset requirements and being traded, and storing the preprocessed option data to a server or a local hard disk;
module M3: calculating corresponding implicit fluctuation rate for each option by using a BS option pricing formula and a dichotomy for option data stored in a server or a local hard disk;
module M4: calculating to obtain a current-day fluctuation rate index according to the corresponding implicit fluctuation rate of each option, and storing the calculated current-day fluctuation rate index to a server or a local hard disk;
module M5: judging whether a fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index stored in the server or the local hard disk deviates from a preset value or not, and transmitting the fluctuation rate risk to a result display end;
module M6: according to the predicted fluctuation rate risk, pre-judging fluctuation of the market conditions, and further predicting the data request pressure of the database;
module M7: according to the predicted data request pressure of the database, the calculation power and computer resources of the database are set in advance, the calculation power distribution of the database is improved, and the corresponding data acquisition time is shortened.
Preferably, the preprocessing in the module M2 includes missing value complementing, discarding and cleaning data.
Preferably, said module M4 comprises:
Figure BDA0002804940350000041
Figure BDA0002804940350000042
Figure BDA0002804940350000043
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents the daily fluctuation rate index; volume (i) represents the transaction amount; i denotes the ith option.
Preferably, the module M4 includes:
Figure BDA0002804940350000044
Figure BDA0002804940350000045
Figure BDA0002804940350000046
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents the daily fluctuation rate index; openinterest (i) indicates not present dayLeveling the bin; i denotes the ith option.
Preferably, the step M5 includes: judging whether the fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index deviates from a preset value or not; when the volatility index deviates from a preset value, predicting that the volatility risk exists in the future preset time; and when the volatility index does not deviate from the preset value, predicting that no volatility risk exists in the future preset time, and constructing a natural language readable output character string according to the volatility risk and displaying the result through a display end.
Compared with the prior art, the invention has the following beneficial effects:
1. comparing the threshold value according to the current daily fluctuation rate index to obtain the fluctuation rate risk, outputting the fluctuation rate risk to a terminal to be displayed to a user, and giving early warning to the fluctuation rate and the risk of the current market;
2. the fluctuation rate risk is shared with the user in real time through mail reminding, WeChat message reminding and APP popup reminding, so that the user can conveniently grasp the dynamic state in real time, and the risk is avoided;
3. the StrikeWeight part realizes the function of automatic attenuation between the row weight price distance and the weight, and reduces errors and subjective influences possibly caused by human intervention; the MaturityWeiight part of the invention realizes the attenuation function between the due date and the weight, and eliminates the huge error caused by the expiration of the option on the fluctuation rate index; therefore, the time for calculating the fluctuation rate is greatly reduced, and the calculation accuracy is improved;
4. predicting fluctuation rate risks, obtaining a market situation with large rise/fall after a large probability, further prejudging data request pressure of the database, setting configured calculation power and computer resources in advance, improving calculation power distribution of the database, preventing the influence of the calculation power distribution on normal operation of a project due to long data request time, and improving stability and continuity of operation at a business peak; meanwhile, the time for the client to request data acquisition can be shortened, and the use experience of the client is optimized;
5. finance assumes that the stock price profitability is brownian motion with a certain fluctuation rate, and the fluctuation rate is generally considered as standard deviation sigma, as shown in fig. 2, IPvix has obvious advance (or sigma has obvious lag property compared with IPvix) compared with standard deviation sigma, so that IPvix reacts faster than sigma as a method for describing 50ETF fluctuation rate;
6. IPvix is used as an index for measuring the fluctuation rate, and the fluctuation rate is used as a fixed parameter of the stock price and theoretically should fluctuate above and below a fixed value. As shown in fig. 3, when the fluctuation ratio index is between 15 and 25, the price is obviously more stable, and large fluctuation can not occur; while the index reaches over 30 at several peaks, the price will rise and fall in a short period thereafter. Therefore, IPvix can be used as a risk indicator with the promptness for warning that the price may have big rise and fall after early warning, and assisting the secondary market trader to control the position and the risk.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic diagram of a system for risk forewarning based on option calculation volatility index;
FIG. 2 is a graph of the upper 50ETF fluctuation index and the upper 50ETF standard deviation trend;
FIG. 3 is a graph of the price of the upper evidence 50ETF and the fluctuation rate index trend of the upper evidence 50 ETF;
fig. 4 is a flow chart of fluctuation ratio index calculation.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Measuring and early warning of future financial market fluctuation rate risks are achieved based on fluctuation rate index calculation, firstly, all option data which are being traded in the market are obtained, and the implicit fluctuation rate of each option is calculated according to a related algorithm and a formula; then, calculating the fluctuation rate index of the current day according to the information such as the implicit fluctuation rate and the transaction amount and the related formulas derived and modified by the user; and finally, judging whether the fluctuation rate risk exists in the future short term or not according to whether the fluctuation rate index deviates from the theoretical normal value (15-25).
Example 1
The invention provides a risk early warning method based on option calculation fluctuation rate index, which comprises the following steps: as shown in figure 1 of the drawings, in which,
step M1: acquiring all option data meeting preset requirements in trading from a data service provider and the trading by adopting an internet intelligent information acquisition technology and a data interface;
step M2: preprocessing all collected option data meeting preset requirements and being traded, and storing the preprocessed option data to a server or a local hard disk;
step M3: calculating corresponding implicit fluctuation rate for each option by using a BS option pricing formula and a dichotomy for option data stored in a server or a local hard disk;
step M4: calculating to obtain a current-day fluctuation rate index according to the corresponding implicit fluctuation rate of each option, and storing the calculated current-day fluctuation rate index to a server or a local hard disk;
step M5: judging whether a fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index stored in the server or the local hard disk deviates from a preset value or not, and transmitting the fluctuation rate risk to a result display end;
step M6: according to the predicted fluctuation rate risk, pre-judging fluctuation of the market conditions, and further predicting the data request pressure of the database;
step M7: according to the predicted data request pressure of the database, the calculation power and computer resources of the database are set in advance, the calculation power distribution of the database is improved, and the corresponding data acquisition time is shortened.
Specifically, the preprocessing in the step M2 includes missing value complementing, discarding and cleaning data.
Specifically, the prior art CBOE volatility index formula includes:
Figure BDA0002804940350000061
Figure BDA0002804940350000071
the parameter a is artificially specified so that the StrikeWeight term is entered into IPvix as a hyper-parameter.
IPvix calculation for a day: assuming that there are N tradeable options for the day, the numerator portion is calculated as: the trading volume of the option is the row weight StrikeWeight is the implicit fluctuation rate of the option, and the denominator part is the trading volume of the option is the row weight;
the existing CBOE volatility index formula has two obvious problems: first, the parameter a of StrikeWeight, as the hyperparameter to be input, has a certain prior error. Further, in use, certain objectivity is lost; secondly, when the term is close to the price of the row weight, the implicit fluctuation rate is significantly increased, thereby affecting the final value of IPvix and distorting the value.
Specifically, the step M4 includes: as shown in figure 4 of the drawings,
Figure BDA0002804940350000072
Figure BDA0002804940350000073
Figure BDA0002804940350000074
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter for preventing the implicit fluctuation rate of a single option from exponentially distorting the fluctuation rate of the option by distance from the day of the optionA value; impVol (i) denotes the implicit volatility of the ith option; IPvix represents a volatility index; volume (i) represents the transaction amount; i denotes the ith option.
The new formula firstly improves the StrikeWeiight, so that the parameter a to be determined is not required to be input, and the prior error is not existed any more; secondly, a MaturityWeiight item is added, so that the influence of options adjacent to the right-of-way day on the parameters of the IPvix is 0, and the condition of fluctuation rate index distortion caused by the adjacent right-of-way day is eliminated.
Specifically, step M4 includes:
in view of the strong "mastery" feature of the trading of options, like the "mastery contract" of futures trading, the market has a significant preference for some options, and the trading volume and position holding volume of these options are significantly larger than those of other options. We can introduce the position holding ratio parameter OpenInterest, and improve the final IPvix formula by using the position holding amount of the option.
Figure BDA0002804940350000075
Figure BDA0002804940350000076
Figure BDA0002804940350000081
Wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents a volatility index; openinterest (i) represents the amount of unbinned bins on the day; i denotes the ith option.
In the traditional CBOE fluctuation rate index calculation formula, the option data meeting the conditions needs to be traversed firstly, then the option data (buying and selling price difference, forward theoretical price same as the right-to-go price, right-to-go price difference waiting) needs to be substituted into the formula, the sigma ^2 of each stock is calculated, and then the stock is issued as the sigma of each stock. In the middle, two times of data (acquiring option basic price data, acquiring corresponding purchase price difference, forward price and right price difference data) are acquired, and corresponding attribute values are calculated for multiple times. Then, it is necessary to obtain Vix ^2 values by combining the time (minute-precise) from the row weight corresponding to the option, and then obtain Vix. This process is likely to be negative, and Vix ^2<0, Vix cannot be square in the real number domain. The traditional calculation method has the advantages that unnecessary attribute values (forward price, right price difference and buying and selling price difference waiting) are calculated for multiple times, 2 times of data are obtained, and results cannot be obtained in a real number domain.
The invention only needs to acquire option data (option price data) once, and firstly saves some time in data acquisition. And secondly, due to the existence of the existing formula (and the data of buying and selling price difference, right-of-way price difference, forward price and the like required by the traditional formula do not need to be calculated in the formula), compared with the traditional formula for calculating the sigma ^2, the calculation method is simpler and further has quicker operation. Finally, due to the assumed condition of the BS formula, when the implicit fluctuation rate is calculated, a solution in the real number domain must exist, so that the problem that the final result cannot be solved in the real number domain does not occur.
Specifically, the step M5 includes: judging whether the fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index deviates from a preset value or not; when the volatility index deviates from a preset value, predicting that the volatility risk exists in the future preset time; when the volatility index does not deviate from the preset value, predicting that no volatility risk exists in the future preset time, constructing a natural language readable output character string according to the volatility risk, displaying the result through a display end, and displaying the result to a trader/fund manager/client through the display end (e-mail, WeChat, app popup window and the like);
specifically, the step M7 includes:
specifically, the module M7 includes: when the risk of fluctuation rate is predicted, a large rising/falling market appears after a large probability, and further the data request pressure of the database is increased (all people request data, so the time for a certain person to acquire the data is inevitably increased); in the aspect of service self business, for the models and projects needing to acquire data in real time, the configured calculation power and computer resources can be set in advance, the calculation power distribution of the database to the models and the projects is improved, and the influence on the normal operation of the projects caused by the long time of requesting the data is avoided. In the aspect of serving clients, when a large market occurs, the number of times that clients request data through the APP tends to increase, and further the pressure of the server will increase. And similarly, the calculation power distribution of the database can be improved in advance, the time for the client to request data acquisition is shortened, and the use experience of the client is optimized.
The invention provides a risk early warning system based on option calculation fluctuation rate index, which comprises:
module M1: acquiring all option data meeting preset requirements in trading from a data service provider and the trading by adopting an internet intelligent information acquisition technology and a data interface;
module M2: preprocessing all collected option data meeting preset requirements and being traded, and storing the preprocessed option data to a server or a local hard disk;
module M3: calculating corresponding implicit fluctuation rate for each option by using a BS option pricing formula and a dichotomy for option data stored in a server or a local hard disk;
module M4: calculating to obtain a current-day fluctuation rate index according to the corresponding implicit fluctuation rate of each option, and storing the calculated current-day fluctuation rate index to a server or a local hard disk;
module M5: judging whether a fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index stored in the server or the local hard disk deviates from a preset value or not, and transmitting the fluctuation rate risk to a result display end;
module M6: according to the predicted fluctuation rate risk, pre-judging fluctuation of the market conditions, and further predicting the data request pressure of the database;
module M7: according to the predicted data request pressure of the database, the calculation power and computer resources of the database are set in advance, the calculation power distribution of the database is improved, and the corresponding data acquisition time is shortened.
In particular, the preprocessing in the module M2 includes missing value complementing, discarding and cleaning data.
Specifically, the prior art CBOE volatility index formula includes:
Figure BDA0002804940350000091
Figure BDA0002804940350000092
the parameter a is artificially specified so that the StrikeWeight term is entered into IPvix as a hyper-parameter.
IPvix calculation for a day: assuming that there are N tradeable options for the day, the numerator portion is calculated as: the trading volume of the option is the row weight StrikeWeight is the implicit fluctuation rate of the option, and the denominator part is the trading volume of the option is the row weight;
the existing CBOE volatility index formula has two obvious problems: first, the parameter a of StrikeWeight, as the hyperparameter to be input, has a certain prior error. Further, in use, certain objectivity is lost; secondly, when the term is close to the price of the row weight, the implicit fluctuation rate is significantly increased, thereby affecting the final value of IPvix and distorting the value.
Specifically, the module M4 includes:
Figure BDA0002804940350000101
Figure BDA0002804940350000102
Figure BDA0002804940350000103
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents a volatility index; volume (i) represents the transaction amount; i denotes the ith option.
The new formula firstly improves the StrikeWeiight, so that the parameter a to be determined is not required to be input, and the prior error is not existed any more; secondly, a MaturityWeiight item is added, so that the influence of options adjacent to the right-of-way day on the parameters of the IPvix is 0, and the condition of fluctuation rate index distortion caused by the adjacent right-of-way day is eliminated.
Specifically, the module M4 includes:
in view of the strong "mastery" feature of the trading of options, like the "mastery contract" of futures trading, the market has a significant preference for some options, and the trading volume and position holding volume of these options are significantly larger than those of other options. We can introduce the position holding ratio parameter OpenInterest, and improve the final IPvix formula by using the position holding amount of the option.
Figure BDA0002804940350000104
Figure BDA0002804940350000105
Figure BDA0002804940350000106
Wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents a volatility index; openinterest (i) represents the amount of unbinned bins on the day; i denotes the ith option.
In the traditional CBOE fluctuation rate index calculation formula, the option data meeting the conditions needs to be traversed firstly, then the option data (buying and selling price difference, forward theoretical price same as the right-to-go price, right-to-go price difference waiting) needs to be substituted into the formula, the sigma ^2 of each stock is calculated, and then the stock is issued as the sigma of each stock. In the middle, two times of data (acquiring option basic price data, acquiring corresponding purchase price difference, forward price and right price difference data) are acquired, and corresponding attribute values are calculated for multiple times. Then, it is necessary to obtain Vix ^2 values by combining the time (minute-precise) from the row weight corresponding to the option, and then obtain Vix. This process is likely to be negative, and Vix ^2<0, Vix cannot be square in the real number domain. The traditional calculation method has the advantages that unnecessary attribute values (forward price, right price difference and buying and selling price difference waiting) are calculated for multiple times, 2 times of data are obtained, and results cannot be obtained in a real number domain.
The invention only needs to acquire option data (option price data) once, and firstly saves some time in data acquisition. And secondly, due to the existence of the existing formula (and the data of buying and selling price difference, right-of-way price difference, forward price and the like required by the traditional formula do not need to be calculated in the formula), compared with the traditional formula for calculating the sigma ^2, the calculation method is simpler and further has quicker operation. Finally, due to the assumed condition of the BS formula, when the implicit fluctuation rate is calculated, a solution in the real number domain must exist, so that the problem that the final result cannot be solved in the real number domain does not occur.
Specifically, the module M5 includes: judging whether the fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index deviates from a preset value or not; when the volatility index deviates from a preset value, predicting that the volatility risk exists in the future preset time; and when the volatility index does not deviate from the preset value, predicting that no volatility risk exists in the future preset time, constructing a natural language readable output character string according to the volatility risk, displaying the result through a display end, and displaying the result to the trader/fund manager/client through the display end (e-mail, WeChat, app popup window and the like).
Specifically, the module M7 includes: when the risk of fluctuation rate is predicted, a large rising/falling market appears after a large probability, and further the data request pressure of the database is increased (all people request data, so the time for a certain person to acquire the data is inevitably increased); in the aspect of service self business, for the models and projects needing to acquire data in real time, the configured calculation power and computer resources can be set in advance, the calculation power distribution of the database to the models and the projects is improved, and the influence on the normal operation of the projects caused by the long time of requesting the data is avoided. In the aspect of serving clients, when a large market occurs, the number of times that clients request data through the APP tends to increase, and further the pressure of the server will increase. And similarly, the calculation power distribution of the database can be improved in advance, the time for the client to request data acquisition is shortened, and the use experience of the client is optimized.
Example 2
Example 2 is a modification of example 1
For option 10002690, whose closing price is 0.5598, trading volume is 87, 50ETF price is 3.211 at 2020, right of day 7 and 27, the right of way price is 3.7, the implicit volatility calculated by dichotomy is 0.3255, and the distance of the right from the right of way day is 0.4 (year), so the contribution of the right to IPvix is as follows:
molecular part:
Volume*StrikeWeight*ImpVol*MaturityWeight＝87*0.859*0.3255*1＝24.325
a denominator part:
Volume*StrikeWeight**MaturityWeight＝87*0.859*1＝74.733
all options which can be traded on the day are traversed, and the fluctuation rate index is known as follows: 28.09. while IPvix has some mean regression properties, its probability should be between 15 and 25. Therefore, the fluctuation rate index of the day is high, and the sudden rise and fall risk should be prevented.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (10)

1. A method for risk early warning based on option calculation fluctuation rate index is characterized by comprising the following steps:
step M1: acquiring all option data meeting preset requirements in trading from a data service provider and the trading by adopting an internet intelligent information acquisition technology and a data interface;
step M2: preprocessing all collected option data meeting preset requirements and being traded, and storing the preprocessed option data to a server or a local hard disk;
step M3: calculating corresponding implicit fluctuation rate for each option by using a BS option pricing formula and a dichotomy for option data stored in a server or a local hard disk;
step M4: calculating to obtain a current-day fluctuation rate index according to the corresponding implicit fluctuation rate of each option, and storing the calculated current-day fluctuation rate index to a server or a local hard disk;
step M5: judging whether a fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index stored in the server or the local hard disk deviates from a preset value or not, and transmitting the fluctuation rate risk to a result display end;
step M6: according to the predicted fluctuation rate risk, pre-judging fluctuation of the market conditions, and further predicting the data request pressure of the database;
step M7: according to the predicted data request pressure of the database, the calculation power and computer resources of the database are set in advance, the calculation power distribution of the database is improved, and the corresponding data acquisition time is shortened.
2. The method for risk pre-warning based on option calculation fluctuation rate index according to claim 1, wherein the pre-processing in the step M2 comprises missing value complementing, discarding and cleaning data.
3. The method for risk pre-warning based on option calculation fluctuation rate index according to claim 1, wherein the step M4 comprises:
Figure FDA0002804940340000011
Figure FDA0002804940340000012
Figure FDA0002804940340000013
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; im pVol (i) represents the implicit fluctuation rate of the ith option; IPvix represents the daily fluctuation rate index; volume (i) represents the transaction amount; i denotes the ith option.
4. The method for risk pre-warning based on option calculation fluctuation rate index according to claim 1, wherein the step M4 comprises:
Figure FDA0002804940340000021
Figure FDA0002804940340000022
Figure FDA0002804940340000023
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents the daily fluctuation rate index; open int erest (i) represents the amount of ununbinned bins on the day; i denotes the ith option.
5. The method for risk pre-warning based on option calculation fluctuation rate index according to claim 1, wherein the step M5 comprises: judging whether the fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index deviates from a preset value or not; when the volatility index deviates from a preset value, predicting that the volatility risk exists in the future preset time; and when the volatility index does not deviate from the preset value, predicting that no volatility risk exists in the future preset time, and constructing a natural language readable output character string according to the volatility risk and displaying the result through a display end.
6. A system for risk forewarning based on option calculation volatility index, comprising:
module M1: acquiring all option data meeting preset requirements in trading from a data service provider and the trading by adopting an internet intelligent information acquisition technology and a data interface;
module M2: preprocessing all collected option data meeting preset requirements and being traded, and storing the preprocessed option data to a server or a local hard disk;
module M3: calculating corresponding implicit fluctuation rate for each option by using a BS option pricing formula and a dichotomy for option data stored in a server or a local hard disk;
module M4: calculating to obtain a current-day fluctuation rate index according to the corresponding implicit fluctuation rate of each option, and storing the calculated current-day fluctuation rate index to a server or a local hard disk;
module M5: judging whether a fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index stored in the server or the local hard disk deviates from a preset value or not, and transmitting the fluctuation rate risk to a result display end;
module M6: according to the predicted fluctuation rate risk, pre-judging fluctuation of the market conditions, and further predicting the data request pressure of the database;
module M7: according to the predicted data request pressure of the database, the calculation power and computer resources of the database are set in advance, the calculation power distribution of the database is improved, and the corresponding data acquisition time is shortened.
7. The system for risk forewarning based on option calculation volatility index of claim 6, wherein the preprocessing in module M2 comprises missing value complementing, discarding and cleaning data.
8. The system for risk forewarning based on option calculation volatility index according to claim 6, wherein said module M4 comprises:
Figure FDA0002804940340000031
Figure FDA0002804940340000032
Figure FDA0002804940340000033
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents the daily fluctuation rate index; volume (i) represents the transaction amount; i denotes the ith option.
9. The system for risk forewarning based on option calculation volatility index of claim 6, wherein module M4 comprises:
Figure FDA0002804940340000034
Figure FDA0002804940340000035
Figure FDA0002804940340000036
wherein K represents a row right price; stRepresents the price of the ETF; a represents a parameter value which ensures that the implicit fluctuation rate of a single option is not distorted by the phase fluctuation rate index due to the distance from the right day; impVol (i) denotes the implicit volatility of the ith option; IPvix represents the daily fluctuation rate index; open int erest (i) represents the amount of ununbinned bins on the day; i denotes the ith option.
10. The method for risk pre-warning based on option calculation fluctuation rate index according to claim 6, wherein the step M5 comprises: judging whether the fluctuation rate risk exists in the future preset time or not according to whether the current-day fluctuation rate index deviates from a preset value or not; when the volatility index deviates from a preset value, predicting that the volatility risk exists in the future preset time; and when the volatility index does not deviate from the preset value, predicting that no volatility risk exists in the future preset time, and constructing a natural language readable output character string according to the volatility risk and displaying the result through a display end.
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