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CN-121981824-A - Financial derivative pricing method and device, electronic equipment and storage medium

CN121981824ACN 121981824 ACN121981824 ACN 121981824ACN-121981824-A

Abstract

The invention discloses a financial derivative pricing method, a financial derivative pricing device, electronic equipment and a storage medium. The method comprises the steps of obtaining a market data stream, carrying out first quantum preprocessing on the basis of a plurality of target asset price paths in the market data stream, iteratively calculating expected benefit price of a target derivative on the basis of a first quantum preprocessing result by using a quantum amplitude estimation algorithm, carrying out second quantum preprocessing on the basis of risk factors of investment combinations in the market data stream, iteratively calculating a risk exposure expected value of the target derivative on the basis of a second quantum preprocessing result by using a quantum approximation optimization algorithm, and realizing pricing of the target derivative under the condition that the expected benefit price is matched with the risk exposure expected value. The technical scheme of the invention improves the wind control effect of pricing of the financial derivative.

Inventors

  • LI ZHAOJIA

Assignees

  • 中国工商银行股份有限公司

Dates

Publication Date
20260505
Application Date
20260116

Claims (10)

  1. 1. A method of pricing a financial derivative, comprising: Obtaining a market data stream; Performing first quantum preprocessing on the basis of a plurality of target asset price paths in the market data stream, and iteratively calculating expected revenue prices of the target derivative on the basis of first quantum preprocessing results by utilizing a quantum amplitude estimation algorithm; Performing second quantum preprocessing based on risk factors of investment combinations in the market data stream, and iteratively calculating a risk exposure expected value of the target derivative based on a second quantum preprocessing result by utilizing a quantum approximation optimization algorithm; The target derivative pricing is achieved if the expected revenue price and the risk exposure expected value match.
  2. 2. The method of claim 1, wherein performing a first quantum preprocessing based on a plurality of target asset price paths in the market data stream comprises: acquiring a plurality of target asset price paths and an expiration price of each path based on the market data stream; encoding an expiration price vector and a price path quantum superposition state for each path based on the plurality of target asset price paths and the expiration price for each path; Defining a profit judgment condition of the target derivative according to the expiration price vector of each path, and encoding a price estimation target quantum state of the target derivative according to the initial state of the price path quantum superposition state, the non-price internal probability of the target derivative and the profit judgment condition; and according to the income judgment conditions, encoding the non-valence internal subspace reflection quantum state of the target derivative, and encoding initial state reflection for the non-valence internal subspace reflection quantum state.
  3. 3. The method of claim 2, wherein iteratively calculating the expected revenue price for the target derivative based on the first quantum preprocessing result using the quantum amplitude estimation algorithm comprises: determining an amplitude amplification operator according to the price estimation target quantum state, the non-price internal subspace reflection quantum state and the initial state reflection; and carrying out quantum phase estimation based on the amplitude amplification operator to obtain a phase estimation value, and calculating the expected benefit price of the target derivative according to the phase estimation value.
  4. 4. A method according to claim 3, wherein quantum phase estimation based on the amplitude amplification operator yields a phase estimate and calculating the expected revenue price for the target derivative from the phase estimate comprises: performing iterative processing on the amplitude amplification operator to obtain an amplified non-valence internal state amplitude, and obtaining a first phase estimation value according to the amplified non-valence internal state amplitude; adding auxiliary bits to the amplitude amplification operator and performing quantum Fourier transform to obtain a second phase estimated value, wherein the second phase estimated value and the first phase estimated value meet a preset error condition; And calculating the non-price internal probability of the target derivative according to the second phase estimated value, and calculating the expected benefit price of the target derivative according to the non-price internal probability.
  5. 5. The method as recited in claim 2, further comprising: The price path quantum superposition state, the price estimation target quantum state and the non-price internal subspace reflection quantum state are spatially error-correction coded by a preset error correction code; The price path quantum superposition state, the price estimation target quantum state and the non-price internal subspace reflection quantum state are obtained through preset amplitude damping noise evolution.
  6. 6. The method of claim 1, wherein performing a second quantum preprocessing based on risk factors of portfolios in the market data stream comprises: Acquiring risk factors of the portfolio based on the market data stream; Encoding a risk exposure objective function of the target derivative into a problem hamiltonian volume based on the risk factor, wherein the problem hamiltonian volume constructs constraint conditions according to the risk value and the conditional risk value; the hybrid hamiltonian is constructed from the bery-X operator.
  7. 7. The method of claim 6, wherein iteratively calculating risk exposure expectations for the target derivative based on the first quantum pretreatment result using a quantum approximation optimization algorithm comprises: Generating a parameterized quantum state by alternately applying parameterized evolution of the problem hamiltonian and the mixed hamiltonian; Iteratively measuring an expected value of the problem hamiltonian under the parameterized quantum state; and correcting the expected value of the Hamiltonian amount of the problem through predefining error correction Hamiltonian amount and zero noise extrapolation, and taking the corrected result as the risk exposure expected value of the target derivative.
  8. 8. A financial derivative pricing apparatus, comprising: The data acquisition module is used for acquiring market data streams; the price calculation module is used for carrying out first quantum preprocessing on the basis of a plurality of target asset price paths in the market data stream, and iteratively calculating expected income price of the target derivative on the basis of a first quantum preprocessing result by utilizing a quantum amplitude estimation algorithm; the risk calculation module is used for carrying out second quantum preprocessing on the basis of risk factors of the investment portfolios in the market data stream, and iteratively calculating a risk exposure expected value of the target derivative on the basis of second quantum preprocessing results by utilizing a quantum approximation optimization algorithm; And the pricing module is used for realizing the pricing of the target derivative under the condition that the expected income price and the risk exposure expected value are matched.
  9. 9. An electronic device, the electronic device comprising: at least one processor, and A memory communicatively coupled to the at least one processor, wherein, The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a financial derivative pricing method according to any one of claims 1-7.
  10. 10. A computer readable storage medium storing computer instructions for causing a processor to perform a method of pricing a financial derivative according to any one of claims 1-7.

Description

Financial derivative pricing method and device, electronic equipment and storage medium Technical Field The present invention relates to the field of data processing technologies, and in particular, to a method and apparatus for pricing a financial derivative, an electronic device, and a storage medium. Background Due to the coupling effect of product complexity, market dynamics and risk conduction, how to effectively manage the financial derivative in advance is always a serious difficult field of various financial institutions. On one hand, the endogenous lever effect of the financial derivative enables the profit and the loss to be amplified in a nonlinear way, once the financial market severely fluctuates and is easy to cause the chain explosion bin, on the other hand, the financial derivative has high nonlinear risks and is difficult to precisely quantify, parameters such as options, exchange long-term period and the like are in a higher derivative relation along with the change of the target price, a traditional linear model (such as Delta hedging strategy and the like) fails, in addition, the financial derivative also has the cross-market risk infection characteristic and is high in concealment, the derivative and the basic asset (strand, debt and sink) form an entanglement network, and the risk is conducted across the market through the fluctuation rate curved surface, the basic difference and other channels. The conventional financial derivative wind control technical scheme is often limited to a pricing model, such as a option pricing method based on a Black-Scholes model and a Monte Carlo simulation, and a bond combination optimization method based on a classical enumeration method. The inventors have found that the following drawbacks exist in the prior art in the process of implementing the present invention: the method is difficult to identify high-dimensional nonlinear risks, insufficient in real-time performance and weak in long-tail risk capturing capability, and the wind control effect of derivative pricing is poor. Disclosure of Invention The invention provides a method, a device, electronic equipment and a storage medium for pricing financial derivatives, so as to improve the wind control effect of pricing the financial derivatives. According to an aspect of the present invention, there is provided a method of pricing a financial derivative, the method comprising: Obtaining a market data stream; Performing first quantum preprocessing on the basis of a plurality of target asset price paths in the market data stream, and iteratively calculating expected revenue prices of the target derivative on the basis of first quantum preprocessing results by utilizing a quantum amplitude estimation algorithm; Performing second quantum preprocessing based on risk factors of investment combinations in the market data stream, and iteratively calculating a risk exposure expected value of the target derivative based on a second quantum preprocessing result by utilizing a quantum approximation optimization algorithm; The target derivative pricing is achieved if the expected revenue price and the risk exposure expected value match. According to another aspect of the present invention, there is provided a financial derivative pricing apparatus, the apparatus comprising: The data acquisition module is used for acquiring market data streams; the price calculation module is used for carrying out first quantum preprocessing on the basis of a plurality of target asset price paths in the market data stream, and iteratively calculating expected income price of the target derivative on the basis of a first quantum preprocessing result by utilizing a quantum amplitude estimation algorithm; the risk calculation module is used for carrying out second quantum preprocessing on the basis of risk factors of the investment portfolios in the market data stream, and iteratively calculating a risk exposure expected value of the target derivative on the basis of second quantum preprocessing results by utilizing a quantum approximation optimization algorithm; And the pricing module is used for realizing the pricing of the target derivative under the condition that the expected income price and the risk exposure expected value are matched. According to another aspect of the present invention, there is provided an electronic apparatus including: at least one processor, and A memory communicatively coupled to the at least one processor, wherein, The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the financial derivative pricing method of any of the embodiments of the invention. According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the financial derivative pricing method according to any of the embodiments of the present invention. According to