Fusion feature-based option implicit volatility and price analysis system

Abstract

The invention discloses a fusion feature-based option implicit volatility and price analysis system, on one hand, various market business features, non-market features, intermediate stage analysis features of existing models and the like can be effectively fused by vectorization representation capability of deep learning, and continuous expansion and combination of more input feature information can be realized; on the other hand, various models can be used for modeling, and various parameters of the models are optimized, so that the quantitative analysis result is more accurate and more applicable. According to the technical scheme, the system comprises a cross-market quotation type feature processing module, a financial text semantic factor type feature processing module, an option theory analysis model quantitative feature module, a specific time scale feature data fusion module, a multi-time scale feature fusion module, a multi-field feature fusion and modeling analysis module. and an expert knowledge and risk parameter management module.

Description

technical field [0001] The invention relates to an option quantitative analysis technology, in particular to an option implied volatility and price analysis system based on fusion features. Background technique [0002] For futures derivatives on exchanges such as financial futures and commodity futures, the implied volatility and premium price of option products (hereinafter referred to as price, option price or option contract price) have always been the most concerned and researched in option trading. It is also the most important link in market risk management. The reason is that, on the surface, the price of an option product reflects the expectation of the participants in the derivatives market on the future price fluctuation of the option product, which is closely related to the investor's investment strategy; An important link, this price change trend also implicitly reflects the entire financial market's prediction of the market investor group's willingness for the...

AI Technical Summary

Problems solved by technology

[0006] (1) The input and output of the method are too fixed and cannot be better integrated into various characteristics in the market (such as price information) and outside the market (such as news public opinion);

[0007] (2) The modeling process of the method comes from theoretical derivation with too many assumptions, and it is impossible to continuously correct and adjust the model parameters in a data-driven way combined with real data, so the actual availability of the analysis results of the model is limited

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