FinTech-based Joint Pricing of Options and Stocks: Data Quality, Mathematical Modeling, and Visualization Framework
Main Article Content
Keywords
FinTech, joint pricing, options, stocks, data cleaning
Abstract
With the rapid development of FinTech technologies such as big data analytics, cloud computing, and algorithmic modeling, joint pricing of options and stocks faces new opportunities and challenges. Traditional option pricing methods often underperform in high-dimensional, noisy, and asynchronous market data. This paper proposes a FinTech-based joint pricing framework, encompassing data quality control, robust modeling methods, and result visualization strategies. We analyze common data collection problems, such as missing values, inconsistent formats, time asynchrony, and high-frequency noise, and propose corresponding data cleaning, feature engineering, and modeling processes, including classic parametric models, machine learning models, and hybrid models. Finally, this paper discusses visualization methods, such as time series plots, implied volatility surfaces, and residual analysis, to support model validation and risk management. This framework provides academic researchers and investment practitioners with a systematic and reproducible joint pricing process.
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