Implied Volatility Prediction of Financial Options Products Based on the CL-TCN Model
Yuemeng Li, Chenyu Wang, Zhongchen Miao, Jian Shu Gao, Jidong Lu · Atlantis Highlights in Computer Sciences/Atlantis highlights in computer sciences · 2023
The implied volatility of options is a key factor in judging the price trend of options and analyzing their trade, so it is very important to use a reasonable method to predict it accurately.Since the traditional B-S-M formula calculation method cannot reflect the actual changes of Tick-level granularity implied volatility in the market, we found a model suitable for processing option quotation data with significant high-frequency and fine-grained timing features, which named CL-TCN model.It combines the contrastive learning framework and TCN model, and its special time series encoding method to predict the downstream implied volatility task is of great help, which can not only solve the problem of order discontinuity caused by the difference in option liquidity, improve the parallel processing efficiency of high-frequency time series data, but also improve the accuracy and generalization of forecasting.At the same time, this paper also mines the features of option-related business, verifies the endogeneity of the model by using clustering algorithm, and extracts a more representative volatility analysis indicators than the traditional calculated variables.