System and method for real-time spot price volatility surface prediction

Patent metadata

Field	Value
Publication number	US-2025238866-A1
Application number	US-202418417757-A
Country	US
Kind code	A1
Filing date	Jan 19, 2024
Priority date	Jan 19, 2024
Publication date	Jul 24, 2025
Grant date	—

Abstract

Various methods and processes, apparatuses/systems, and media for data processing are disclosed. A processor accesses a database that stores a plurality of historical data and input data corresponding to a derivative instrument; implements an artificial intelligence deep learning model; trains the artificial intelligence deep learning model with the historical data and the input data corresponding to the derivative instrument for time-series data prediction; learns, in response to training, volatility surface deformation data over time corresponding to the derivative instrument; calculates spot sensitivity data of the derivative instrument based on the volatility surface deformation data output from the artificial intelligence deep learning model; displays the spot sensitivity data onto a user interface; and receives user input via the user interface to conduct a transaction with respect to the derivative instrument.

First claim

What is claimed is:

1 . A method for data processing by utilizing one or more processors along with allocated memory, the method comprising:
accessing a database that stores a plurality of historical data and input data corresponding to a derivative instrument; implementing an artificial intelligence deep learning model; training the artificial intelligence deep learning model with the historical data and the input data corresponding to the derivative instrument for time-series data prediction; learning, in response to training, volatility surface deformation data over time corresponding to the derivative instrument; calculating spot sensitivity data of the derivative instrument based on the volatility surface deformation data output from the artificial intelligence deep learning model; displaying the spot sensitivity data onto a user interface; and receiving user input via the user interface to conduct a transaction with respect to the derivative instrument.

2 . The method according to claim 1 , in calculating spot sensitivity data, the method further comprising:
implementing an algorithm to capture volatility surface dynamics data corresponding to the derivative instrument.

3 . The method according to claim 1 , wherein the artificial intelligence deep learning model is a recurrent neural network model.

4 . The method according to claim 3 , further comprising:
implementing feedback loop to allow strike-wise dynamic corresponding to the derivative instrument; and inputting variable length sequences as the input data.

5 . The method according to claim 1 , wherein the database is a position service that stores position service data corresponding to the derivative instrument.

6 . The method according to claim 1 , wherein the database is a market data service that stores market data corresponding to the derivative instrument.

7 . The method according to claim 1 , further comprising:
applying bidirectional gate recurrent unit neural network algorithm; and outputting, in response to applying the bidirectional gate recurrent unit neural network algorithm, implied volatility dynamics data corresponding to the derivative instrument.

8 . A system for data processing, the system comprising:
a processor; and a memory operatively connected to the processor via a communication interface, the memory storing computer readable instructions, when executed, causes the processor to: access a database that stores a plurality of historical data and input data corresponding to a derivative instrument; implement an artificial intelligence deep learning model; train the artificial intelligence deep learning model with the historical data and the input data corresponding to the derivative instrument for time-series data prediction; learn, in response to training, volatility surface deformation data over time corresponding to the derivative instrument; calculate spot sensitivity data of the derivative instrument based on the volatility surface deformation data output from the artificial intelligence deep learning model; display the spot sensitivity data onto a user interface; and receive user input via the user interface to conduct a transaction with respect to the derivative instrument.

9 . The system according to claim 8 , in calculating spot sensitivity data, the processor is further configured to:
implement an algorithm to capture volatility surface dynamics data corresponding to the derivative instrument.

10 . The system according to claim 8 , wherein the artificial intelligence deep learning model is a recurrent neural network model.

11 . The system according to claim 10 , wherein the processor is further configured to:
implement feedback loop to allow strike-wise dynamic corresponding to the derivative instrument; and input variable length sequences as the input data.

12 . The system according to claim 8 , wherein the database is a position service that stores position service data corresponding to the derivative instrument.

13 . The system according to claim 8 , wherein the database is a market data service that stores market data corresponding to the derivative instrument.

14 . The system according to claim 8 , wherein the processor is further configured to:
apply bidirectional gate recurrent unit neural network algorithm; and output, in response to applying the bidirectional gate recurrent unit neural network algorithm, implied volatility dynamics data corresponding to the derivative instrument.

15 . A non-transitory computer readable medium configured to store instructions for data processing, the instructions, when executed, cause a processor to perform the following:
accessing a database that stores a plurality of historical data and input data corresponding to a derivative instrument; implementing an artificial intelligence deep learning model; training the artificial intelligence deep learning model with the historical data and the input data corresponding to the derivative instrument for time-series data prediction; learning, in response to training, volatility surface deformation data over time corresponding to the derivative instrument; calculating spot sensitivity data of the derivative instrument based on the volatility surface deformation data output from the artificial intelligence deep learning model; displaying the spot sensitivity data onto a user interface; and receiving user input via the user interface to conduct a transaction with respect to the derivative instrument.

16 . The non-transitory computer readable medium according to claim 15 , in calculating spot sensitivity data, the instructions, when executed, cause the processor to further perform the following:
implementing an algorithm to capture volatility surface dynamics data corresponding to the derivative instrument.

17 . The non-transitory computer readable medium according to claim 15 , wherein the artificial intelligence deep learning model is a recurrent neural network model.

18 . The non-transitory computer readable medium according to claim 17 , wherein the instructions, when executed, cause the processor to further perform the following:
implementing feedback loop to allow strike-wise dynamic corresponding to the derivative instrument; and inputting variable length sequences as the input data.

19 . The non-transitory computer readable medium according to claim 15 , wherein the database includes a position service that stores position service data corresponding to the derivative instrument, and a market data service that stores market data corresponding to the derivative instrument.

20 . The non-transitory computer readable medium according to claim 15 , wherein the instructions, when executed, cause the processor to further perform the following:
applying bidirectional gate recurrent unit neural network algorithm; and outputting, in response to applying the bidirectional gate recurrent unit neural network algorithm, implied volatility dynamics data corresponding to the derivative instrument.

Assignees

• Jpmorgan Chase Bank Na

Inventors

• Ni Kai
• Che Charlie
• Lin Hanxuan
• Hu Guofan

Classifications

• G06Q40/04

  Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange · CPC title

• G06F16/2474

  Sequence data queries, e.g. querying versioned data · CPC title

• G06Q40/06 Primary

  Asset management; Financial planning or analysis · CPC title