Freight fluctuation risk quantitative hedging method and system, electronic equipment and storage medium

Abstract

The invention provides a freight fluctuation risk quantification hedging method and system, electronic equipment and a storage medium. The freight fluctuation risk quantification hedging method comprises the following steps: acquiring current first shipping data; first data information is determined according to the first shipping data and a quantitative model, the first data information comprises the number of positions of futures and the value of the futures, the futures comprise freight derivatives and carbon quotas, and the quantitative model is a quantitative model of the carbon quotas and freight fluctuation risks; and determining a hedging strategy according to the first data information and the hedging strategy model. According to the method, the quantitative model of the carbon quota and freight fluctuation risk and the hedging strategy model are introduced, so that the influence of the carbon quota on freight fluctuation can be accurately evaluated, and meanwhile, a scientific and effective hedging strategy can be provided for enterprises.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for quantitatively hedging freight rate fluctuation risk, an electronic device and a storage medium. Background Art

[0002] Traditional risk management methods often focus on the identification and response of a single risk, lacking comprehensiveness and systematicness. Therefore, in the complex environment where the shipping industry is facing the pressure of carbon quota policies and the risk of freight rate fluctuations, relevant technologies are difficult to accurately assess the impact of carbon quotas on freight rate fluctuations, and are unable to provide companies with scientific and effective hedging strategies. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art or related art.

[0004] To this end, the first aspect of the present invention proposes a quantitative hedging method for freight rate fluctuation risk.

[0005] The second aspect of the present invention proposes a quantitative hedging system for freight rate fluctuation risk.

[0006] A third aspect of the present invention provides an electronic device.

[0007] A fourth aspect of the present invention provides a storage medium.

[0008] In view of this, according to a first aspect of the present invention, a method for quantitative hedging of freight volatility risk is proposed, comprising: obtaining current first shipping data; determining first data information based on the first shipping data and a quantitative model, wherein the first data information comprises: the number of futures positions and the value of futures, the futures comprise freight derivatives and carbon quotas, and the quantitative model is a quantitative model of carbon quotas and freight volatility risks; determining a hedging strategy based on the first data information and a hedging strategy model.

[0009] The quantitative hedging method for freight fluctuation risk provided by the present invention mainly includes: firstly obtaining the current first shipping data, wherein the first shipping data may include but is not limited to the freight data, carbon emission data, carbon quota allocation data, shipping market supply and demand, international oil price change trend and policy and regulatory changes of the shipping company. The first shipping data can be obtained from multiple channels such as shipping companies, carbon trading markets, shipping industry associations and authoritative research institutions. After obtaining the first shipping data, since the sources and formats of the first shipping data are various, it is necessary to perform preprocessing operations on the first shipping data to clarify, organize and standardize the first shipping data to ensure the accuracy and consistency of the first shipping data, wherein the preprocessing operations include but are not limited to removing outliers, filling missing values ​​and data normalization. After preprocessing the first shipping data, the first shipping data is input into the quantitative model to determine the first data information, the first data information includes: the number of futures positions and the value of futures, the futures include: freight derivatives and carbon quotas, the quantitative model refers to the quantitative model of carbon quota and freight fluctuation risk, the quantitative model can fully consider the complex interactive relationship between the carbon quota market and the shipping freight market, accurately predict the trend of carbon quota price changes, and evaluate the potential risk of shipping freight fluctuations accordingly. The first data information includes the number of futures positions and the value of futures, so that the potential risk of shipping freight fluctuations can be reflected in the form of data. After obtaining the first data information, the first data information and the first shipping data are input into the hedging strategy model to determine the hedging strategy, wherein the hedging strategy can also be symmetrically combined, that is, the hedging combination is automatically generated according to the results of the quantitative evaluation model, and the hedging combination can be a hedging combination of carbon quotas and freight derivatives. Through the intelligent hedging strategy formulation method, not only the hedging efficiency is improved, but also the risk of human operation is reduced, providing a more stable and reliable risk management tool for shipping companies. In the present invention, by acquiring the current shipping data, and then inputting the current shipping data into the quantitative model of carbon quota and shipping freight fluctuation risk to obtain data information, and then inputting the data information into the hedging strategy model to obtain the hedging strategy, it is possible to accurately evaluate the impact of carbon quotas on freight fluctuations, and also provide enterprises with scientific and effective hedging strategies.

[0010] The above-mentioned freight rate fluctuation risk quantitative hedging method according to the present invention may also have the following technical features: In some technical solutions, optionally, before the step of determining the first data information based on the first shipping data and the quantitative model, the method for quantitative hedging of freight volatility risk includes: obtaining historical second shipping data and historical market information, wherein the historical market information includes the number of carbon quota positions and the number of freight derivative positions; training the first mathematical model based on the second shipping data and the historical market information to obtain a quantitative model.

[0011] In the technical solution, before the step of determining the first data information according to the first shipping data and the quantitative model, the method for quantitative hedging of freight fluctuation risk includes: firstly obtaining the historical second shipping data and the corresponding historical market information, wherein the second shipping information may include but is not limited to the historical freight data of the shipping enterprise, the historical carbon emission data, the carbon quota allocation data, the supply and demand of the shipping market, the trend of international oil price changes and the changes in policies and regulations, etc., and the second shipping information may be obtained from multiple channels such as shipping enterprises, carbon trading markets, shipping industry associations and authoritative research institutions. The historical market information refers to the number of positions of carbon quotas and the number of positions of freight derivatives corresponding to the second shipping data. After obtaining the second shipping data and the historical market information, since the sources and formats of the second shipping data and the historical market information are various, it is necessary to perform preprocessing operations on the second shipping data and the historical market information to clarify, organize and standardize the second shipping data and the historical market information, and ensure the accuracy and consistency of the second shipping data and the historical market information, wherein the preprocessing operations include but are not limited to removing outliers, filling missing values ​​and data normalization, etc. Then, the first mathematical model is trained using the second shipping data after the preprocessing operation and the historical market information, and the trained first mathematical model is used as a quantitative model. The parameters in the first mathematical model are estimated by using the historical second shipping data and the historical market information, and the performance of the first mathematical model is backtested by the historical second shipping data and the historical market information, and then the parameters of the first mathematical model are adjusted according to the performance, and finally the adjusted first mathematical model is used as a quantitative model, thereby improving the prediction accuracy and risk management ability of the quantitative model.

[0012] In some technical schemes, optionally, before the step of training the first mathematical model according to the second shipping data and historical market information, the method for quantitative hedging of freight volatility risk includes: determining the first equation according to ship power, cargo demand, carbon quota price, carbon quota quantity and freight; determining the second equation according to the freight and the intrinsic value of freight derivatives; determining the third equation according to the carbon quota price, digital factors and time value; determining the fourth equation according to the carbon quota quantity, carbon quota price, the execution price of carbon quota and the adjustment value; determining the fifth equation according to the freight, the intrinsic value, time value and adjustment value of freight derivatives and the value of freight derivatives; determining the sixth equation according to freight volatility, carbon quota volatility and the number of positions of freight derivatives; determining the seventh equation according to the number of positions of freight derivatives and the number of positions of carbon quotas; determining the first mathematical model according to the first equation, the second equation, the third equation, the fourth equation, the fifth equation, the sixth equation and the seventh equation.

[0013] In the technical solution, before the step of training the first mathematical model according to the second shipping data and historical market information, the method for quantitative hedging of freight volatility risk includes: first, determining the first equation according to ship power, cargo demand, carbon quota price, carbon quota quantity and freight, wherein the first equation mainly reflects the mathematical relationship between freight and ship power, cargo demand, carbon quota price and carbon quota quantity, that is, the freight can be calculated through ship power, cargo demand, carbon quota price and carbon quota quantity. Then, determining the second equation according to the intrinsic value of freight and freight derivatives, wherein the second equation reflects the mathematical relationship between freight and the intrinsic value of freight derivatives, that is, the intrinsic value of freight derivatives can be calculated through freight, wherein the intrinsic value refers to the value that can be obtained if the futures contract of freight derivatives is executed immediately. Then, the third equation is determined according to the carbon quota price, mathematical factors and time value, wherein the third equation can reflect the mathematical relationship between the present value of time value and the carbon quota price and mathematical factors, that is, the time value can be calculated through the carbon quota price and mathematical factors, wherein the mathematical factor refers to the factor that discounts the future cash flow to the present. Then the fourth equation is determined according to the carbon quota quantity, carbon quota price, carbon quota execution price and adjustment value, wherein the carbon quota execution price refers to the execution price in the carbon quota futures contract, and the adjustment value refers to the value of the freight derivative in the futures contract adjusted after the carbon cost is used as a factor affecting the freight. The fourth equation mainly represents the mathematical relationship between the adjustment value and the carbon quota quantity, carbon quota price and carbon quota execution price, that is, the adjustment value can be calculated through the carbon quota quantity, carbon quota price and carbon quota execution price. Then, the fifth equation is determined according to the freight, the intrinsic value, time value and adjustment value of the derivatives and the value of the freight derivatives, wherein the fifth equation mainly reflects the mathematical relationship between the value of the freight derivatives and the freight, the intrinsic value, time value and adjustment value of the derivatives, that is, the value of the freight derivatives can be calculated through the freight, the intrinsic value, time value and adjustment value of the freight derivatives. Furthermore, since the first equation is related to the freight, the second equation is related to the value of the freight derivatives, the third equation is related to the time value, and the fourth equation is related to the adjustment value, the fifth equation can also be composed of the first equation, the second equation, the third equation and the fourth equation. Then, the sixth equation is determined according to the freight volatility, the carbon quota volatility and the number of positions of freight derivatives, that is, the sixth equation mainly represents the mathematical relationship between the number of positions of freight derivatives and the freight volatility and the carbon quota volatility, that is, the number of positions of freight derivatives can be obtained through the freight volatility and the carbon quota volatility, wherein the volatility represents the uncertainty or variability of the freight price or carbon quota.Then, the seventh equation is determined according to the number of freight derivatives held and the number of carbon quotas held, wherein the seventh equation mainly expresses the mathematical relationship between the number of freight derivatives held and the number of carbon quotas held. Finally, the first equation, the second equation, the third equation, the fourth equation, the fifth equation, the sixth equation and the seventh equation are combined and sorted to obtain the first mathematical model. The present invention introduces carbon quotas into the calculation process of freight, and then establishes a mathematical relationship between the value of freight derivatives and freight, as well as a mathematical relationship between the number of freight derivatives held and the number of carbon quotas held, so that the first mathematical model becomes a quantitative model of carbon quota and freight volatility risk.

[0014] In some technical solutions, optionally, before the step of determining the first equation based on ship power, cargo demand, carbon quota price, carbon quota quantity and freight, the freight volatility risk quantitative hedging method includes: constructing a carbon quota price prediction model, a carbon emissions prediction model and a carbon quota demand and supply model; determining the carbon quota quantity based on the carbon quota price prediction model, the carbon emissions prediction model and the carbon quota demand and supply model.

[0015] In this embodiment, before the step of determining the first equation according to ship power, cargo demand, carbon quota price, carbon quota quantity and freight, the freight volatility risk quantitative hedging method includes: since the data at the next moment needs to be predicted according to the current data in the first mathematical model, it is also necessary to construct a carbon quota price prediction model, a carbon emission prediction model and a carbon quota demand and supply model respectively, wherein the carbon quota price prediction model can predict the carbon quota price at the next moment according to the carbon quota price at this moment, the carbon emission prediction model can predict the carbon emission at the next moment according to the carbon emission at this moment, and the carbon quota demand and supply model can calculate the demand and supply of carbon quotas. Finally, the carbon quota quantity is determined according to the carbon quota price prediction model, the carbon emission prediction model and the carbon quota demand and supply model, that is, the carbon quota price prediction model, the carbon emission prediction model and the carbon quota demand and supply model are integrated to determine the carbon quota quantity.

[0016] In some technical schemes, optionally, before the step of determining the hedging strategy based on the first data information and the hedging strategy model, the quantitative hedging method for freight volatility risk includes: determining a constraint formula based on the expected return of futures, futures weight information, return variance and return covariance; and constructing a hedging strategy model based on the constraint formula and the Sharpe ratio.

[0017] In the technical scheme, before the step of determining the hedging strategy according to the first data information and the hedging strategy model, the quantitative hedging method for freight fluctuation risk includes: before determining the hedging strategy according to the first data information and the hedging strategy model, it is necessary to construct a hedging strategy model. Specifically, firstly, the constraint formula is determined according to the expected return of futures, the weight information of futures, the return variance and the return covariance. Since the constraints of the hedging strategy model are capital constraints, transaction volume restrictions and hedging ratio restrictions, the expected return of futures, the weight information of futures, the return variance and the return covariance can be determined according to the above constraints, and then the mathematical relationship between the expected return of futures, the weight information of futures, the return variance and the return covariance is used as the constraint formula. Then, the hedging strategy model is constructed according to the constraint formula and the Sharpe ratio. Specifically, since the hedging strategy model aims to maximize the risk-adjusted return of the enterprise, the Sharpe ratio can be used as a measure of the risk-adjusted return, and then the Sharpe ratio is combined with the constraint formula to obtain the final hedging strategy model.

[0018] In some technical solutions, optionally, after the step of determining the hedging strategy based on the first data information and the hedging strategy model, the method for quantitative hedging of freight fluctuation risk includes: monitoring the first shipping data and the implementation results of the hedging strategy; and adjusting the hedging strategy based on abnormal implementation results or changes in the first shipping data.

[0019] In this technical solution, after the step of determining the hedging strategy according to the first data information and the hedging strategy model, the quantitative hedging method for freight fluctuation risk includes: after obtaining the hedging strategy, guiding the shipping company to perform actual operations according to the hedging strategy, wherein the hedging strategy includes purchasing carbon financial products and signing transportation contracts. At the same time, it is also necessary to monitor the implementation results of the first shipping data and the hedging strategy, that is, it is necessary to continuously monitor the changes in freight transfer risks and carbon quota costs, as well as the implementation effect of the hedging strategy. If an abnormal situation or market change trend, that is, the first shipping data changes, the hedging strategy is adjusted in a timely manner.

[0020] In some technical schemes, optionally, after the step of determining the hedging strategy based on the first data information and the hedging strategy model, the freight fluctuation risk quantitative hedging method includes: evaluating the implementation results of the hedging strategy according to the evaluation indicators at preset time intervals to determine the evaluation results, and the evaluation indicators include: the degree of risk reduction and / or profit improvement; adjusting the quantitative model and the hedging strategy model according to the evaluation results.

[0021] In this technical solution, after the step of determining the hedging strategy according to the first data information and the hedging strategy model, the quantitative hedging method for freight fluctuation risk includes: evaluating the implementation results of the hedging strategy according to the evaluation indicators at every preset time period to determine the evaluation results, that is, regularly evaluating the implementation effect of the hedging strategy to obtain the evaluation results, wherein the evaluation indicators may include the degree of risk reduction and the improvement of returns, etc. Then, the quantitative model and the hedging strategy model are adjusted according to the evaluation results, that is, the quantitative model, the hedging strategy model and the implementation process are feedback-adjusted according to the evaluation results, wherein the adjustment method may be to optimize the model parameters, adjust the hedging strategy combination and improve the implementation process, etc. By adjusting the quantitative model and the hedging strategy model according to the implementation effect of the hedging strategy, the accuracy of the quantitative model and the hedging strategy model is provided.

[0022] According to a second aspect of the present invention, a quantitative hedging system for freight volatility risk is proposed, comprising: a first acquisition module, the first acquisition module is used to acquire current first shipping data; a first determination module, the first determination module is used to determine first data information based on the first shipping data and a quantitative model, wherein the first data information includes: the number of futures positions and the value of futures, the futures include freight derivatives and carbon quotas, and the quantitative model is a quantitative model of carbon quotas and freight volatility risks; a second determination module, the second determination module is used to determine the hedging strategy based on the first data information and the hedging strategy model.

[0023] The freight fluctuation risk quantitative hedging system provided by the present invention mainly includes: a first acquisition module, a first determination module and a second determination module. Among them, the first acquisition module can first acquire the current first shipping data, wherein the first shipping data may include but is not limited to the freight data, carbon emission data, carbon quota allocation data, shipping market supply and demand, international oil price change trends and policy and regulatory changes of shipping companies. The first shipping data can be obtained from multiple channels such as shipping companies, carbon trading markets, shipping industry associations and authoritative research institutions. After obtaining the first shipping data, since the sources and formats of the first shipping data are diverse, it is necessary to perform preprocessing operations on the first shipping data to clarify, organize and standardize the first shipping data to ensure the accuracy and consistency of the first shipping data, wherein the preprocessing operations include but are not limited to removing outliers, filling missing values ​​and data normalization. After preprocessing the first shipping data, the first determination module inputs the first shipping data into the quantitative model to determine the first data information, the first data information includes: the number of futures positions and the value of futures, the futures include: freight derivatives and carbon quotas, the quantitative model refers to the quantitative model of carbon quota and freight fluctuation risk, the quantitative model can fully consider the complex interactive relationship between the carbon quota market and the shipping freight market, accurately predict the trend of carbon quota price changes, and evaluate the potential risk of shipping freight fluctuations accordingly. The first data information includes the number of futures positions and the value of futures, so that the potential risk of shipping freight fluctuations can be reflected in the form of data. After obtaining the first data information, the second determination module inputs the first data information and the first shipping data into the hedging strategy model to determine the hedging strategy, wherein the hedging strategy can also be symmetrically combined, that is, the hedging combination is automatically generated according to the results of the quantitative evaluation model, and the hedging combination can be a hedging combination of carbon quotas and freight derivatives. Through the intelligent hedging strategy formulation method, not only the hedging efficiency is improved, but also the risk of human operation is reduced, providing shipping companies with a more stable and reliable risk management tool. In the present invention, by obtaining the current shipping data, and then inputting the current shipping data into the quantitative model of carbon quota and shipping freight fluctuation risk to obtain data information, and then inputting the data information into the hedging strategy model to obtain the hedging strategy, so as to accurately evaluate the impact of carbon quota on freight fluctuations, and also provide scientific and effective hedging strategies for enterprises.

[0024] According to a third aspect of the present invention, an electronic device is proposed, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for quantitative hedging of freight fluctuation risk as described above are implemented.

[0025] The electronic device provided by the present invention implements the steps of the above-mentioned method for quantitative hedging of freight fluctuation risk when the processor executes the computer program, which can achieve the technical effect of any of the above-mentioned technical solutions and will not be repeated here.

[0026] According to a fourth aspect of the present invention, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for quantitative hedging of freight fluctuation risk as described above are implemented.

[0027] The storage medium provided by the present invention implements the steps of the above-mentioned method for quantitative hedging of freight fluctuation risk when the computer program is executed by the processor, and can achieve the technical effects of any of the above-mentioned technical solutions, which will not be repeated here.

[0028] Additional aspects and advantages of the present invention will become apparent from the following description or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 A schematic diagram showing a flow chart of a method for quantitatively hedging freight rate fluctuation risk according to an embodiment of the present invention; Figure 2 A flow chart showing a method for quantitative hedging of freight fluctuation risk according to an embodiment of the present invention before the step of determining first data information according to first shipping data and a quantitative model; Figure 3 A flow chart showing a method for quantitative hedging of freight rate fluctuation risk according to an embodiment of the present invention before the step of training a first mathematical model according to second shipping data and historical market information; Figure 4 A flow chart showing a method for quantitatively hedging freight fluctuation risk according to an embodiment of the present invention before the step of determining the first equation according to ship power, cargo demand, carbon quota price, carbon quota quantity and freight; Figure 5 A flow chart showing a method for quantitative hedging of freight fluctuation risk according to an embodiment of the present invention before the step of determining a hedging strategy according to first data information and a hedging strategy model; Figure 6 One of the flowchart diagrams is shown after the step of determining the hedging strategy according to the first data information and the hedging strategy model in the method for quantitative hedging of freight fluctuation risk in one embodiment of the present invention; Figure 7A second flow chart diagram is shown after the step of determining the hedging strategy according to the first data information and the hedging strategy model in the method for quantitative hedging of freight fluctuation risk according to one embodiment of the present invention; Figure 8 A schematic block diagram showing a system for quantitative hedging of freight fluctuation risk according to an embodiment of the present invention; Fig. 9 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0030] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0032] Figure 1 A flow chart of a method for quantitatively hedging freight rate fluctuation risk according to an embodiment of the present invention is shown. The method comprises: Step 102: Obtain current first shipping data; Step 104: determining first data information according to the first shipping data and the quantitative model, wherein the first data information includes: the number of futures positions and the value of the futures, the futures include freight derivatives and carbon quotas, and the quantitative model is a quantitative model of carbon quotas and freight volatility risks; Step 106: Determine a hedging strategy based on the first data information and the hedging strategy model.

[0033] The quantitative hedging method for freight fluctuation risk provided by the present invention mainly includes: firstly obtaining the current first shipping data, wherein the first shipping data may include but is not limited to the freight data, carbon emission data, carbon quota allocation data, shipping market supply and demand, international oil price change trend and policy and regulatory changes of the shipping company. The first shipping data can be obtained from multiple channels such as shipping companies, carbon trading markets, shipping industry associations and authoritative research institutions. After obtaining the first shipping data, since the sources and formats of the first shipping data are various, it is necessary to perform preprocessing operations on the first shipping data to clarify, organize and standardize the first shipping data to ensure the accuracy and consistency of the first shipping data, wherein the preprocessing operations include but are not limited to removing outliers, filling missing values ​​and data normalization. After preprocessing the first shipping data, the first shipping data is input into the quantitative model to determine the first data information, the first data information includes: the number of futures positions and the value of futures, the futures include: freight derivatives and carbon quotas, the quantitative model refers to the quantitative model of carbon quota and freight fluctuation risk, the quantitative model can fully consider the complex interactive relationship between the carbon quota market and the shipping freight market, accurately predict the trend of carbon quota price changes, and evaluate the potential risk of shipping freight fluctuations accordingly. The first data information includes the number of futures positions and the value of futures, so that the potential risk of shipping freight fluctuations can be reflected in the form of data. After obtaining the first data information, the first data information and the first shipping data are input into the hedging strategy model to determine the hedging strategy, wherein the hedging strategy can also be symmetrically combined, that is, the hedging combination is automatically generated according to the results of the quantitative evaluation model, and the hedging combination can be a hedging combination of carbon quotas and freight derivatives. Through the intelligent hedging strategy formulation method, not only the hedging efficiency is improved, but also the risk of human operation is reduced, providing a more stable and reliable risk management tool for shipping companies. In the present invention, by acquiring the current shipping data, and then inputting the current shipping data into the quantitative model of carbon quota and shipping freight fluctuation risk to obtain data information, and then inputting the data information into the hedging strategy model to obtain the hedging strategy, it is possible to accurately evaluate the impact of carbon quotas on freight fluctuations, and also provide enterprises with scientific and effective hedging strategies.

[0034] Figure 2 A flow chart of a method for quantitatively hedging freight fluctuation risk according to an embodiment of the present invention is shown before the step of determining first data information according to first shipping data and a quantitative model; wherein, before the step of determining first data information according to first shipping data and a quantitative model, the method for quantitatively hedging freight fluctuation risk includes: Step 202: Acquire historical second shipping data and historical market information, wherein the historical market information includes the number of carbon quota positions and the number of freight derivative positions; Step 204: Train the first mathematical model according to the second shipping data and historical market information to obtain a quantitative model.

[0035] In this embodiment, before the step of determining the first data information according to the first shipping data and the quantitative model, the freight volatility risk quantitative hedging method includes: first obtaining the historical second shipping data and the corresponding historical market information, wherein the second shipping information may include but is not limited to the historical freight data of the shipping enterprise, the historical carbon emission data, the carbon quota allocation data, the supply and demand of the shipping market, the trend of international oil price changes and the changes in policies and regulations, etc., and the second shipping information can be obtained from multiple channels such as shipping enterprises, carbon trading markets, shipping industry associations and authoritative research institutions. The historical market information refers to the number of positions of carbon quotas and the number of positions of freight derivatives corresponding to the second shipping data. After obtaining the second shipping data and the historical market information, since the sources and formats of the second shipping data and the historical market information are various, it is necessary to perform preprocessing operations on the second shipping data and the historical market information to clarify, organize and standardize the second shipping data and the historical market information to ensure the accuracy and consistency of the second shipping data and the historical market information, wherein the preprocessing operations include but are not limited to removing outliers, filling missing values ​​and data normalization. Then, the first mathematical model is trained using the second shipping data after the preprocessing operation and the historical market information, and the trained first mathematical model is used as a quantitative model. The parameters in the first mathematical model are estimated by using the historical second shipping data and the historical market information, and the performance of the first mathematical model is backtested by the historical second shipping data and the historical market information, and then the parameters of the first mathematical model are adjusted according to the performance, and finally the adjusted first mathematical model is used as a quantitative model, thereby improving the prediction accuracy and risk management ability of the quantitative model.

[0036] Figure 3 A flow chart of a method for quantitatively hedging freight fluctuation risk according to an embodiment of the present invention is shown before the step of training the first mathematical model according to the second shipping data and the historical market information; wherein, before the step of training the first mathematical model according to the second shipping data and the historical market information, the method for quantitatively hedging freight fluctuation risk includes: Step 302: determining a first equation according to ship power, cargo demand, carbon quota price, carbon quota quantity and freight; Step 304: Determine the second equation based on the intrinsic value of freight and freight derivatives; determine the third equation based on the carbon quota price, the digital factor and the time value; Step 306: Determine a fourth equation according to the carbon quota quantity, the carbon quota price, the carbon quota execution price and the adjustment value; Step 308: determining a fifth equation based on the freight, the intrinsic value of the freight derivative, the time value, the adjustment value and the value of the freight derivative; Step 310: Determine the sixth equation according to the freight volatility, the carbon quota volatility and the number of positions of freight derivatives; Step 312: determining a seventh equation according to the number of freight derivative positions and the number of carbon quota positions; Step 314: Determine a first mathematical model according to the first equation, the second equation, the third equation, the fourth equation, the fifth equation, the sixth equation, and the seventh equation.

[0037] In this embodiment, before the step of training the first mathematical model according to the second shipping data and historical market information, the method for quantitative hedging of freight fluctuation risk includes: first, determining a first equation according to ship power, cargo demand, carbon quota price, carbon quota quantity and freight, wherein the first equation mainly reflects the mathematical relationship between freight and ship power, cargo demand, carbon quota price and carbon quota quantity, that is, the freight can be calculated by ship power, cargo demand, carbon quota price and carbon quota quantity. Then, determining a second equation according to the intrinsic value of freight and freight derivatives, wherein the second equation reflects the mathematical relationship between freight and the intrinsic value of freight derivatives, that is, the intrinsic value of freight derivatives can be calculated by freight, wherein the intrinsic value refers to the value that can be obtained if the futures contract of freight derivatives is executed immediately. Then, the third equation is determined according to the carbon quota price, mathematical factors and time value, wherein the third equation can reflect the mathematical relationship between the present value of time value and the carbon quota price and mathematical factors, that is, the time value can be calculated through the carbon quota price and mathematical factors, wherein the mathematical factor refers to the factor that discounts the future cash flow to the present. Then the fourth equation is determined according to the carbon quota quantity, carbon quota price, carbon quota execution price and adjustment value, wherein the carbon quota execution price refers to the execution price in the carbon quota futures contract, and the adjustment value refers to the value of the freight derivative in the futures contract adjusted after the carbon cost is used as a factor affecting the freight. The fourth equation mainly represents the mathematical relationship between the adjustment value and the carbon quota quantity, carbon quota price and carbon quota execution price, that is, the adjustment value can be calculated through the carbon quota quantity, carbon quota price and carbon quota execution price. Then, the fifth equation is determined according to the freight, the intrinsic value, time value and adjustment value of the derivatives and the value of the freight derivatives, wherein the fifth equation mainly reflects the mathematical relationship between the value of the freight derivatives and the freight, the intrinsic value, time value and adjustment value of the derivatives, that is, the value of the freight derivatives can be calculated through the freight, the intrinsic value, time value and adjustment value of the freight derivatives. Furthermore, since the first equation is related to the freight, the second equation is related to the value of the freight derivatives, the third equation is related to the time value, and the fourth equation is related to the adjustment value, the fifth equation can also be composed of the first equation, the second equation, the third equation and the fourth equation. Then, the sixth equation is determined according to the freight volatility, the carbon quota volatility and the number of positions of freight derivatives, that is, the sixth equation mainly represents the mathematical relationship between the number of positions of freight derivatives and the freight volatility and the carbon quota volatility, that is, the number of positions of freight derivatives can be obtained through the freight volatility and the carbon quota volatility, wherein the volatility represents the uncertainty or variability of the freight price or carbon quota.Then, the seventh equation is determined according to the number of freight derivatives held and the number of carbon quotas held, wherein the seventh equation mainly expresses the mathematical relationship between the number of freight derivatives held and the number of carbon quotas held. Finally, the first equation, the second equation, the third equation, the fourth equation, the fifth equation, the sixth equation and the seventh equation are combined and sorted to obtain the first mathematical model. The present invention introduces carbon quotas into the calculation process of freight, and then establishes a mathematical relationship between the value of freight derivatives and freight, as well as a mathematical relationship between the number of freight derivatives held and the number of carbon quotas held, so that the first mathematical model becomes a quantitative model of carbon quota and freight volatility risk.

[0038] Figure 4 A flow chart of a method for quantitatively hedging freight fluctuation risk according to an embodiment of the present invention is shown before the step of determining the first equation according to ship power, cargo demand, carbon quota price, carbon quota quantity and freight; wherein, before the step of determining the first equation according to ship power, cargo demand, carbon quota price, carbon quota quantity and freight, the method for quantitatively hedging freight fluctuation risk includes: Step 402: constructing a carbon quota price prediction model, a carbon emission prediction model, and a carbon quota demand and supply model; Step 404: Determine the carbon quota quantity according to the carbon quota price prediction model, the carbon emission prediction model, and the carbon quota demand and supply model.

[0039] In this embodiment, before the step of determining the first equation according to ship power, cargo demand, carbon quota price, carbon quota quantity and freight, the freight volatility risk quantitative hedging method includes: since the data at the next moment needs to be predicted according to the current data in the first mathematical model, it is also necessary to construct a carbon quota price prediction model, a carbon emission prediction model and a carbon quota demand and supply model respectively, wherein the carbon quota price prediction model can predict the carbon quota price at the next moment according to the carbon quota price at this moment, the carbon emission prediction model can predict the carbon emission at the next moment according to the carbon emission at this moment, and the carbon quota demand and supply model can calculate the demand and supply of carbon quotas. Finally, the carbon quota quantity is determined according to the carbon quota price prediction model, the carbon emission prediction model and the carbon quota demand and supply model, that is, the carbon quota price prediction model, the carbon emission prediction model and the carbon quota demand and supply model are integrated to determine the carbon quota quantity.

[0040] Figure 5 A flow chart of a method for quantitatively hedging freight fluctuation risk according to an embodiment of the present invention is shown before the step of determining a hedging strategy according to first data information and a hedging strategy model; wherein, before the step of determining a hedging strategy according to first data information and a hedging strategy model, the method for quantitatively hedging freight fluctuation risk includes: Step 502: determining a constraint formula according to the expected return of futures, weight information of futures, return variance and return covariance; Step 504: Construct a hedging strategy model according to the constraint formula and the Sharpe ratio.

[0041] In this embodiment, before the step of determining the hedging strategy according to the first data information and the hedging strategy model, the quantitative hedging method for freight fluctuation risk includes: before determining the hedging strategy according to the first data information and the hedging strategy model, it is necessary to construct a hedging strategy model. Specifically, firstly, the constraint formula is determined according to the expected return of the futures, the weight information of the futures, the return variance and the return covariance. Since the constraints of the hedging strategy model are capital constraints, transaction volume restrictions and hedging ratio restrictions, the expected return of the futures, the weight information of the futures, the return variance and the return covariance can be determined according to the above constraints, and then the mathematical relationship between the expected return of the futures, the weight information of the futures, the return variance and the return covariance is used as the constraint formula. Then, the hedging strategy model is constructed according to the constraint formula and the Sharpe ratio. Specifically, since the hedging strategy model aims to maximize the risk-adjusted return of the enterprise, the Sharpe ratio can be used as a measure of the risk-adjusted return, and then the Sharpe ratio is combined with the constraint formula to obtain the final hedging strategy model.

[0042] Exemplarily, the specific steps of constructing the quantitative model and hedging strategy model of the present invention are: first, collect relevant data from multiple channels such as shipping companies, carbon trading markets, shipping industry associations and authoritative research institutions. The relevant data include but are not limited to historical freight data of shipping companies, historical carbon emission data, carbon quota allocation data, shipping market supply and demand, international oil price trends, and policy and regulatory changes. Among them, the indicator names and sources of the relationship data are shown in Table 1: Table 1

[0043] After obtaining the relevant data, preprocessing operations are performed on the relevant data, wherein the preprocessing operations include but are not limited to removing outliers, filling missing values, and data normalization, etc. By performing preprocessing operations on the relevant data, the accuracy of the relevant data is ensured.

[0044] Furthermore, based on the results of the above processing, each indicator is abstracted into letters, as follows: F t : freight at time t; C t : Carbon quota price at time t; Q t : The amount of carbon quota at time t; S t : Total shipping capacity; R t : Real-time ship capacity; Dt : Cargo demand forecast; P t : Freight index; E hist，macro : Historical macro carbon emission data, reflecting the emissions of the entire shipping industry; E hist，micro : Historical micro carbon emission data, reflecting the carbon emission of specific ships / companies; A t : Carbon quota allocation data; T t : Carbon tax price; : Port congestion; Pol t : Policy changes; M t : Market sentiment.

[0045] Furthermore, based on the above parameters, a model is constructed. First, a freight forecasting model is constructed. The freight index P can be predicted using time series analysis. t ; Therefore, the freight forecast model can be: P t+1 =α0+α1×P t +ε t+1 ; Among them, α0, α1 are model parameters, ε t+1 is the error term, P t+1 It represents the freight at time t+1, P t It represents the freight at time t.

[0046] Then, a carbon quota price prediction model is constructed, in which the ARIMA (Auto Regressive Integrated Moving Average Model) model can be used to predict the carbon quota price C t ; The carbon quota price prediction model can be: C t+1 =ɸ0+ɸ1×C t +θ1×C t-1 + t+1 ; Among them, ɸ0, ɸ1 and θ1 are model parameters, t+1 is the error term, C t+1 represents the carbon quota price at time t+1, C t-1 represents the carbon quota price at time t-1, C t It represents the carbon quota price at time t.

[0047] Then, a carbon emission prediction model is constructed to predict carbon emissions by combining macro and micro historical carbon emission data; the carbon emission prediction model can be: E t+1 =β0+β1×E hist，macro +β2×Ehist，micro +υ t+1 ; Among them, β0, β1 and β2 are model parameters, υ t+1 is the error term, E hist，macro is the historical macro carbon emission data, E hist，micro is the historical micro carbon emission data, E t+1 is the carbon emission at time t+1.

[0048] Then, a carbon quota demand and supply model is constructed. When constructing a carbon quota demand and supply balance model, the model is as follows: ; Q supply，t+1 =g(A t , E t ,Pol t ); Among them, Q demand，t+1 It represents the demand for carbon quotas. In the demand function f: D t It represents the forecast of cargo demand, which reflects the market demand for transportation services, and thus affects the demand for carbon quotas. t It represents the total shipping capacity, which indicates the number of ships available in the market and is positively correlated with the demand for carbon quotas. t It represents the real-time capacity of ships, indicating the capacity of ships currently in use, which may be directly related to the demand for carbon quotas; It indicates the port congestion, which may increase the waiting time of ships, thereby increasing carbon emissions and carbon quota demand. supply，t+1 It represents the supply of carbon quotas. In the supply function g: A t It represents the carbon quota allocation data, which may be set by the government or relevant agencies based on policies and environmental goals. t It represents the current carbon emissions, which may affect the supply of carbon quotas because the supply needs to meet the actual or expected emissions. t It means that policy changes, such as changes in carbon tax policies or emissions trading systems, will directly affect the supply of carbon quotas.

[0049] Then, a quantitative model of shipping freight volatility risk considering carbon quotas is constructed. First, the impact of carbon quota price C on freight costs needs to be considered. If shipping companies need to purchase carbon quotas to cover their carbon emissions, this will increase operating costs, which may in turn affect freight rates. Incorporating carbon costs into the freight model, it can be imagined that freight F depends not only on the state of the transportation market, but also on carbon costs, so the first equation can be obtained: F=F(S t , D t , C t , Q t); Among them, S t is the ship capacity, D t is the demand for goods, C t is the carbon quota price, Q t is the amount of carbon quota, and F represents the freight.

[0050] Then for the futures contract, its value V can be calculated based on the expected freight and carbon costs. For futures contracts, the Black-Scholes model can be used to price them, taking carbon costs as a factor affecting freight rates, so we get V=V base (F, t) + V carbon (C t , Q t , t); Among them, V base is the value of a derivative based on traditional factors (freight and time), V carbon is the adjustment value based on carbon cost and carbon quota quantity, F represents freight, t represents time, C t is the carbon quota price, Q t is the number of carbon quotas.

[0051] Taking into account the purchase or sale of carbon quotas, the formula for the adjustment value can be obtained, namely the fourth equation: V carbon =Q t ×(C t -C strike ) + ; Among them, C strike is the execution price of the carbon quota in the futures contract, (x) + represents max(x, 0), which means that for the party holding the call option of carbon quota, if the carbon quota price in the market is C t Higher than the strike price C strike , can be C strike Buy carbon quotas at a price of C t The price is sold, thus making a profit, which is (C t -C strike ) + .

[0052] Taking all the above factors into consideration, the value of freight derivatives, i.e. the fifth equation, can be expressed as: V=F×N(d1)-C×e -r(T-t) ×N(d2)+Q t ×max(C t -C strike , 0); Among them, N(d1) and N(d2) are the cumulative normal distribution values ​​calculated based on the Black-Scholes model, and the other symbols have the same meanings as before. Specifically, F×N(d1) is the second equation, which represents the intrinsic value of the derivative, that is, the value that can be obtained if the contract is executed immediately. C×e -r(T-t) ×N(d2) is the third equation, which represents the present value of time value, e -r(T-t) is the factor that discounts future cash flows to the present. d1 and d2 are intermediate variables of the model. The calculation formulas for d1 and d2 are as follows: ; ; Among them, σ F is the volatility of freight, indicating the uncertainty or volatility of freight prices, r is the risk-free interest rate, T is the expiration time, t is the current time, F is the freight, and C is the carbon quota price.

[0053] Finally, the combined optimization method is used to determine the optimal carbon quota and freight derivative positions, that is, the sixth and seventh equations: ; W C =1-W F ; Among them, W F and W C are the number of positions of freight derivatives and carbon quotas, σ C is the volatility of carbon quota, σ F is the volatility of freight rates.

[0054] Finally, the first equation, the second equation, the third equation, the fourth equation, the fifth equation, the sixth equation and the seventh equation are combined to obtain a mathematical model. At this point, the model has been built, and then the parameters of the above model are estimated using historical data and market information. By backtesting the performance of the model with historical data, the parameters are adjusted to improve the model's prediction accuracy and risk management capabilities.

[0055] Furthermore, a hedging strategy model is constructed. The hedging strategy model aims to maximize the risk-adjusted return of the enterprise, so the Sharpe ratio can be used as a measure of risk-adjusted return. The formula is: ; Among them, E[R P ] is the expected return of the portfolio. R f is the risk-free rate. Pis the standard deviation of the portfolio's return, and Sharpe Ratio represents a measure of risk-adjusted return.

[0056] Then the expected return formula for the portfolio is as follows: ; Among them, W i is the weight of asset i in the portfolio, E[R i ] is the expected return of asset i.

[0057] And the constraints of this model are capital constraints, transaction volume restrictions and hedging ratio restrictions, that is, ; l i ≤W i ≤u i ; h min ≤h i ≤h max ; Among them, l i and u i are the lower and upper limits of the weight of asset i, respectively, and h i is the hedging ratio of asset i, h min and h max are the minimum and maximum hedge ratios of asset i respectively.

[0058] In the present invention, the hedging strategy design is mainly based on the quantitative results of carbon quota and freight volatility risk to design the corresponding hedging strategy. The hedging strategy can include purchasing financial products such as carbon futures and options to hedge the carbon quota cost risk, and hedging the freight volatility risk by signing long-term transportation contracts and diversifying transportation routes.

[0059] Assume a simplified portfolio consisting of carbon futures and freight futures. Our goal is to maximize the Sharpe ratio, subject to the following constraints: ; Among them, W i and W j are the weights of carbon futures and freight futures, E[R i ] represents the expected returns of carbon futures and freight futures, σ ij represents the variance and covariance of the returns of each asset, R f is the risk-free rate.

[0060] The constraints are then combined with a measure of risk-adjusted returns to model the hedging strategy.

[0061] Figure 6One of the flow charts of a method for quantitative hedging of freight fluctuation risk according to an embodiment of the present invention is shown, after the step of determining a hedging strategy according to first data information and a hedging strategy model; wherein, after the step of determining a hedging strategy according to first data information and a hedging strategy model, the method for quantitative hedging of freight fluctuation risk includes: Step 602: monitoring the first shipping data and the implementation results of the hedging strategy; Step 604: Adjust the hedging strategy based on abnormal implementation results or changes in the first shipping data.

[0062] In this embodiment, after the step of determining the hedging strategy according to the first data information and the hedging strategy model, the method for quantitative hedging of freight fluctuation risk includes: after obtaining the hedging strategy, guiding the shipping company to perform actual operations according to the hedging strategy, wherein the hedging strategy includes purchasing carbon financial products and signing a transportation contract. At the same time, it is also necessary to monitor the implementation results of the first shipping data and the hedging strategy, that is, it is necessary to continuously monitor the changes in the freight transfer risk and the carbon quota cost, as well as the implementation effect of the hedging strategy. If an abnormal situation or a market change trend, that is, a change in the first shipping data, is found, the hedging strategy is adjusted in a timely manner.

[0063] Figure 7 A second flow chart of a method for quantitatively hedging freight fluctuation risk according to an embodiment of the present invention is shown after the step of determining a hedging strategy according to first data information and a hedging strategy model; wherein, after the step of determining a hedging strategy according to first data information and a hedging strategy model, the method for quantitatively hedging freight fluctuation risk includes: Step 702: Evaluate the implementation results of the hedging strategy according to the evaluation indicators at every preset time period to determine the evaluation results, where the evaluation indicators include: risk reduction degree and / or profit improvement; Step 704: Adjust the quantitative model and hedging strategy model according to the evaluation results.

[0064] In this embodiment, after the step of determining the hedging strategy according to the first data information and the hedging strategy model, the quantitative hedging method for freight fluctuation risk includes: evaluating the implementation results of the hedging strategy according to the evaluation indicators at every preset time period to determine the evaluation results, that is, regularly evaluating the implementation effect of the hedging strategy to obtain the evaluation results, wherein the evaluation indicators may include the degree of risk reduction and the improvement of returns, etc. Then, the quantitative model and the hedging strategy model are adjusted according to the evaluation results, that is, the quantitative model, the hedging strategy model and the implementation process thereof are feedback-adjusted according to the evaluation results, wherein the adjustment method may be to optimize the model parameters, adjust the hedging strategy combination and improve the implementation process, etc. By adjusting the quantitative model and the hedging strategy model according to the implementation effect of the hedging strategy, the accuracy of the quantitative model and the hedging strategy model is provided.

[0065] Figure 8 A schematic block diagram of a freight rate fluctuation risk quantitative hedging system according to an embodiment of the present invention is shown; wherein the freight rate fluctuation risk quantitative hedging system 80 comprises: A first acquisition module 802, used to acquire current first shipping data; A first determination module 804 is used to determine first data information according to the first shipping data and the quantitative model, wherein the first data information includes: the number of futures positions and the value of the futures, the futures include freight derivatives and carbon quotas, and the quantitative model is a quantitative model of carbon quotas and freight volatility risks; The second determination module 806 is used to determine the hedging strategy according to the first data information and the hedging strategy model.

[0066] The freight fluctuation risk quantitative hedging system 80 provided by the present invention mainly includes: a first acquisition module 802, a first determination module 804 and a second determination module 806. Among them, the first acquisition module 802 can first acquire the current first shipping data, wherein the first shipping data may include but is not limited to the shipping company's freight data, carbon emission data, carbon quota allocation data, shipping market supply and demand, international oil price change trends and policy and regulatory changes. The first shipping data can be obtained from multiple channels such as shipping companies, carbon trading markets, shipping industry associations and authoritative research institutions. After obtaining the first shipping data, since the sources and formats of the first shipping data are diverse, it is necessary to perform preprocessing operations on the first shipping data to clarify, organize and standardize the first shipping data to ensure the accuracy and consistency of the first shipping data, wherein the preprocessing operations include but are not limited to removing outliers, filling missing values ​​and data normalization. After preprocessing the first shipping data, the first determination module 804 inputs the first shipping data into the quantitative model to determine the first data information, the first data information includes: the number of futures positions and the value of futures, the futures include: freight derivatives and carbon quotas, the quantitative model refers to the quantitative model of carbon quota and freight fluctuation risk, the quantitative model can fully consider the complex interactive relationship between the carbon quota market and the shipping freight market, accurately predict the trend of carbon quota price changes, and evaluate the potential risk of shipping freight fluctuations accordingly. The first data information includes the number of futures positions and the value of futures, so that the potential risk of shipping freight fluctuations can be reflected in the form of data. After obtaining the first data information, the second determination module 806 inputs the first data information and the first shipping data into the hedging strategy model to determine the hedging strategy, wherein the hedging strategy can also be symmetrically combined, that is, the hedging combination is automatically generated according to the results of the quantitative evaluation model, and the hedging combination can be a hedging combination of carbon quotas and freight derivatives. Through the intelligent hedging strategy formulation method, not only the hedging efficiency is improved, but also the risk of human operation is reduced, providing shipping companies with a more stable and reliable risk management tool. In the present invention, by obtaining the current shipping data, and then inputting the current shipping data into the quantitative model of carbon quota and shipping freight fluctuation risk to obtain data information, and then inputting the data information into the hedging strategy model to obtain the hedging strategy, so as to accurately evaluate the impact of carbon quota on freight fluctuations, and also provide scientific and effective hedging strategies for enterprises.

[0067] In some embodiments, optionally, the freight volatility risk quantitative hedging system 80 also includes: a second acquisition module, used to acquire historical second shipping data and historical market information, wherein the historical market information includes the number of carbon quota positions and the number of freight derivative positions; a training module, used to train the first mathematical model according to the second shipping data and historical market information to obtain a quantitative model.

[0068] In this embodiment, the freight fluctuation risk quantitative hedging system 80 also includes: a second acquisition module and a training module, wherein the second acquisition module can acquire historical second shipping data and corresponding historical market information, wherein the second shipping information may include but is not limited to historical freight data of shipping companies, historical carbon emission data, carbon quota allocation data, shipping market supply and demand, international oil price change trends and policy and regulatory changes, etc., and the second shipping information can be obtained from multiple channels such as shipping companies, carbon trading markets, shipping industry associations and authoritative research institutions. Historical market information refers to the number of positions of carbon quotas and freight derivatives corresponding to the second shipping data. Then the training module uses the second shipping data and historical market information after the preprocessing operation to train the first mathematical model, and uses the trained first mathematical model as a quantitative model. By using the historical second shipping data and historical market information to estimate the parameters in the first mathematical model, and backtesting the performance of the first mathematical model through the historical second shipping data and historical market information, and then adjusting the parameters of the first mathematical model according to the performance, and finally using the adjusted first mathematical model as a quantitative model, the prediction accuracy and risk management ability of the quantitative model are improved.

[0069] In some embodiments, optionally, the freight volatility risk quantitative hedging system 80 also includes: a third determination module, which is used to determine the first equation based on ship power, cargo demand, carbon quota price, carbon quota quantity and freight; a fourth determination module, which is used to determine the second equation based on the freight and the intrinsic value of freight derivatives; a fifth determination module, which is used to determine the third equation based on the carbon quota price, digital factors and time value; a sixth determination module, which is used to determine the fourth equation based on the carbon quota quantity, carbon quota price, carbon quota execution price and adjustment value; a seventh determination module, which is used to determine the fifth equation based on the freight, the intrinsic value of freight derivatives, time value and adjustment value and the value of freight derivatives; an eighth determination module, which is used to determine the sixth equation based on freight volatility, carbon quota volatility and the number of positions of freight derivatives; a ninth determination module, which is used to determine the seventh equation based on the number of positions of freight derivatives and the number of positions of carbon quotas; and a tenth determination module, which is used to determine the first mathematical model based on the first equation, the second equation, the third equation, the fourth equation, the fifth equation, the sixth equation and the seventh equation.

[0070] In this embodiment, the freight volatility risk quantitative hedging system 80 also includes: a third determination module, a fourth determination module, a fifth determination module, a sixth determination module, a seventh determination module, an eighth determination module, a ninth determination module and a tenth determination module. Among them, the third determination module can determine the first equation according to the ship power, cargo demand, carbon quota price, carbon quota quantity and freight, wherein the first equation mainly reflects the mathematical relationship between freight and ship power, cargo demand, carbon quota price and carbon quota quantity. Then the fourth determination module can determine the second equation according to the intrinsic value of freight and freight derivatives, wherein the second equation reflects the mathematical relationship between freight and the intrinsic value of freight derivatives, wherein the intrinsic value refers to the value that can be obtained if the futures contract of freight derivatives is executed immediately. Then, the fifth determination module can determine the third equation according to the carbon quota price, mathematical factors and time value, wherein the third equation can reflect the mathematical relationship between the present value of time value and the carbon quota price and mathematical factors, wherein the mathematical factor refers to the factor that discounts future cash flows to the present. The sixth determination module then determines the fourth equation based on the carbon quota quantity, the carbon quota price, the carbon quota execution price and the adjustment value, wherein the carbon quota execution price refers to the execution price in the carbon quota futures contract, and the adjustment value refers to the value of the freight derivative in the futures contract adjusted after the carbon cost is used as a factor affecting the freight. The fourth equation mainly represents the mathematical relationship between the adjustment value and the carbon quota quantity, the carbon quota price and the carbon quota execution price. Then the seventh determination module can determine the fifth equation based on the freight, the intrinsic value of the derivative, the time value, the adjustment value and the value of the freight derivative, wherein the fifth equation mainly reflects the mathematical relationship between the value of the freight derivative and the freight, the intrinsic value of the derivative, the time value and the adjustment value. Further, since the first equation is related to the freight, the second equation is related to the value of the freight derivative, the third equation is related to the time value, and the fourth equation is related to the adjustment value, the fifth equation can also be composed of the first equation, the second equation, the third equation and the fourth equation. Then the eighth determination module can determine the sixth equation based on the freight volatility, carbon quota volatility and the number of freight derivative positions, that is, the sixth equation mainly represents the mathematical relationship between the number of freight derivative positions and the freight volatility and carbon quota volatility, wherein the volatility represents the uncertainty or variability of the freight price or carbon quota. Then the ninth determination module can determine the seventh equation based on the number of freight derivative positions and the number of carbon quota positions, wherein the seventh equation mainly represents the mathematical relationship between the number of freight derivative positions and the number of carbon quota positions. Finally, the tenth determination module can combine and organize the first equation, the second equation, the third equation, the fourth equation, the fifth equation, the sixth equation and the seventh equation to obtain the first mathematical model.The present invention introduces carbon quotas into the freight calculation process, and then establishes a mathematical relationship between the value of freight derivatives and freight, as well as a mathematical relationship between the number of freight derivative positions and the number of carbon quota positions, thereby making the first mathematical model a quantitative model of carbon quota and freight volatility risk.

[0071] In some embodiments, optionally, the freight fluctuation risk quantitative hedging system 80 also includes: a first construction module, used to construct a carbon quota price prediction model, a carbon emission prediction model, and a carbon quota demand and supply model; an eleventh determination module, used to determine the carbon quota quantity based on the carbon quota price prediction model, the carbon emission prediction model, and the carbon quota demand and supply model.

[0072] In this embodiment, the freight volatility risk quantitative hedging system 80 includes: a first construction module and an eleventh determination module. Among them, the first construction module can construct a carbon quota price prediction model, a carbon emission prediction model, and a carbon quota demand and supply model, wherein the carbon quota price prediction model can predict the carbon quota price at the next moment based on the carbon quota price at this moment, the carbon emission prediction model can predict the carbon emissions at the next moment based on the carbon emissions at this moment, and the carbon quota demand and supply model can calculate the demand and supply of carbon quotas. Finally, the eleventh determination module can determine the carbon quota quantity based on the carbon quota price prediction model, the carbon emission prediction model, and the carbon quota demand and supply model, that is, the carbon quota price prediction model, the carbon emission prediction model, and the carbon quota demand and supply model are integrated to determine the carbon quota quantity.

[0073] In some embodiments, optionally, the freight volatility risk quantitative hedging system 80 also includes: a twelfth determination module, used to determine the constraint formula based on the expected return of futures, futures weight information, return variance and return covariance; a second construction module, used to construct a hedging strategy model based on the constraint formula and the Sharpe ratio.

[0074] In this embodiment, the freight volatility risk quantitative hedging system 80 also includes: a twelfth determination module and a second construction module. Among them, the twelfth module can determine the constraint formula according to the expected return of futures, the weight information of futures, the return variance and the return covariance. Since the constraints of the hedging strategy model are capital constraints, transaction volume restrictions and hedging ratio restrictions, the expected return of futures, the weight information of futures, the return variance and the return covariance can be determined according to the above constraints, and then the mathematical relationship between the expected return of futures, the weight information of futures, the return variance and the return covariance is used as the constraint formula. Then the second construction module constructs the hedging strategy model according to the constraint formula and the Sharpe ratio. Specifically, since the hedging strategy model is to maximize the risk-adjusted return of the enterprise, the Sharpe ratio can be used as a measure of the risk-adjusted return, and then the Sharpe ratio is combined with the constraint formula to obtain the final hedging strategy model.

[0075] In some embodiments, optionally, the freight fluctuation risk quantitative hedging system 80 also includes: a monitoring module for monitoring the first shipping data and the implementation results of the hedging strategy; and a first adjustment module for adjusting the hedging strategy based on abnormal implementation results or changes in the first shipping data.

[0076] In this embodiment, the freight fluctuation risk quantitative hedging system 80 also includes: a monitoring module and a first adjustment module. Among them, the monitoring module can monitor the first shipping data and the implementation results of the hedging strategy, that is, it is necessary to continuously monitor the changes in freight fluctuation risks and carbon quota costs, as well as the implementation effect of the hedging strategy. If an abnormal situation or market change trend, that is, the first shipping data changes, is found, the first adjustment module will adjust the hedging strategy in a timely manner.

[0077] In some embodiments, optionally, the freight fluctuation risk quantitative hedging system 80 also includes: an evaluation module, which is used to evaluate the implementation results of the hedging strategy according to the evaluation indicators at preset time intervals to determine the evaluation results, and the evaluation indicators include: the degree of risk reduction and / or the improvement of profits; a second adjustment module, which is used to adjust the quantitative model and the hedging strategy model according to the evaluation results.

[0078] In this embodiment, the freight fluctuation risk quantitative hedging system 80 also includes: an evaluation module and a second adjustment module. The evaluation module can evaluate the implementation results of the hedging strategy according to the evaluation indicators at every preset time period to determine the evaluation results, that is, regularly evaluate the implementation effect of the hedging strategy to obtain the evaluation results, wherein the evaluation indicators may include the degree of risk reduction and the improvement of returns, etc. Then the second adjustment module can adjust the quantitative model and the hedging strategy model according to the evaluation results, that is, feedback and adjust the quantitative model, the hedging strategy model and its implementation process according to the evaluation results, wherein the adjusted system can be to optimize model parameters, adjust the hedging strategy combination and improve the implementation process, etc.

[0079] Fig. 9 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown; wherein the electronic device 90 includes a memory 902, a processor 904, and a computer program stored in the memory 902 and executable on the processor 904, and when the processor 904 executes the computer program, the steps of the method for quantitative hedging of freight fluctuation risk as described above are implemented.

[0080] The electronic device 90 provided by the present invention implements the steps of the above-mentioned method for quantitative hedging of freight fluctuation risk when the processor 904 executes the computer program, which can achieve the technical effect of any of the above-mentioned embodiments and will not be repeated here.

[0081] An embodiment of the present invention provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods for quantitative hedging of freight fluctuation risk are implemented.

[0082] The storage medium provided by the present invention implements the steps of the above-mentioned method for quantitative hedging of freight fluctuation risk when the computer program is executed by the processor, and can achieve the technical effect of any of the above-mentioned embodiments, which will not be repeated here.

[0083] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance unless otherwise clearly specified and limited; the terms "connection", "installation", "fixation" and the like should be understood in a broad sense, for example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected or indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0084] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "specific embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A quantitative hedging method for freight rate fluctuation risk, characterized in that: include: Get current First Shipping data; Determine first data information according to the first shipping data and the quantitative model, wherein the first data information includes: the number of futures positions and the value of the futures, the futures include freight derivatives and carbon quotas, and the quantitative model is a quantitative model of the carbon quota and freight volatility risk; A hedging strategy is determined based on the first data information and the hedging strategy model.

2. The method for quantitative hedging of freight rate fluctuation risk according to claim 1, characterized in that: Before the step of determining the first data information according to the first shipping data and the quantitative model, the method for quantitative hedging of freight rate fluctuation risk includes: Acquiring historical second shipping data and historical market information, wherein the historical market information includes the number of carbon quota positions and the number of freight derivative positions; The first mathematical model is trained according to the second shipping data and the historical market information to obtain the quantitative model.

3. The method for quantitative hedging of freight rate fluctuation risk according to claim 2, characterized in that: Before the step of training the first mathematical model according to the second shipping data and the historical market information, the method for quantitative hedging of freight rate fluctuation risk includes: Determine the first equation based on ship power, cargo demand, carbon quota price, carbon quota quantity and freight; determining a second equation based on the intrinsic value of the freight rate and the freight rate derivative; Determine a third equation based on the carbon quota price, mathematical factors and time value; Determine a fourth equation according to the carbon quota quantity, the carbon quota price, the carbon quota execution price and the adjustment value; determining a fifth equation based on the freight rate, the intrinsic value of the freight derivative, the time value, and the adjustment value and the value of the freight derivative; Determine a sixth equation according to the freight volatility, the carbon quota volatility and the number of positions of the freight derivatives; Determine a seventh equation according to the number of positions of the freight derivatives and the number of positions of the carbon quotas; The first mathematical model is determined according to the first equation, the second equation, the third equation, the fourth equation, the fifth equation, the sixth equation, and the seventh equation.

4. The method for quantitative hedging of freight rate fluctuation risk according to claim 3, characterized in that: Before the step of determining the first equation according to the ship power, cargo demand, carbon quota price, carbon quota quantity and freight, the freight volatility risk quantitative hedging method includes: Construct carbon quota price prediction model, carbon emission prediction model and carbon quota demand and supply model; The carbon quota quantity is determined according to the carbon quota price prediction model, the carbon emission prediction model and the carbon quota demand and supply model.

5. The method for quantitative hedging of freight rate fluctuation risk according to claim 1, characterized in that: Before the step of determining the hedging strategy according to the first data information and the hedging strategy model, the quantitative hedging method for freight rate fluctuation risk includes: Determine the constraint formula based on the expected return of futures, futures weight information, return variance and return covariance; The hedging strategy model is constructed according to the constraint formula and the Sharpe ratio.

6. The method for quantitative hedging of freight rate fluctuation risk according to any one of claims 1 to 5, characterized in that: After the step of determining the hedging strategy according to the first data information and the hedging strategy model, the quantitative hedging method for freight rate fluctuation risk includes: Monitoring the first shipping data and the implementation results of the hedging strategy; The hedging strategy is adjusted based on the abnormal implementation result or the change of the first shipping data.

7. The method for quantitative hedging of freight rate fluctuation risk according to any one of claims 1 to 5, characterized in that: After the step of determining the hedging strategy according to the first data information and the hedging strategy model, the quantitative hedging method for freight rate fluctuation risk includes: Evaluate the implementation results of the hedging strategy according to the evaluation indicators at every preset time period to determine the evaluation results, wherein the evaluation indicators include: the degree of risk reduction and / or the improvement of returns; The quantitative model and the hedging strategy model are adjusted according to the evaluation results.

8. A quantitative hedging system for freight rate fluctuation risk, characterized in that: include: A first acquisition module, the first acquisition module is used to acquire current first shipping data; A first determination module, the first determination module is used to determine first data information according to the first shipping data and the quantitative model, wherein the first data information includes: the number of futures positions and the value of the futures, the futures include freight derivatives and carbon quotas, and the quantitative model is a quantitative model of the carbon quota and freight volatility risk; The second determination module is used to determine the hedging strategy according to the first data information and the hedging strategy model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for quantitative hedging of freight rate fluctuation risk according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for quantitative hedging of freight rate fluctuation risk according to any one of claims 1 to 7 are implemented.

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