Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)

<Previous Article In Volume
Next Article In Volume>

Implied Volatility Prediction of Financial Options Products Based on the CL-TCN Model

Authors
Yuemeng Li1, Chenyu Wang1, Zhongchen Miao1, Jian Gao1, Jidong Lu1, *
1Innovation Lab, Shanghai Financial Futures Information Technology Co., Ltd, Shanghai, China
*Corresponding author. Email: lujd@cffex.com.cn
Corresponding Author
Jidong Lu
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-198-2_59How to use a DOI?
Keywords
Implied Volatility; Contrastive Learning; TCN; Clustering; Feature Extraction
Abstract

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.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
10 August 2023
ISBN
978-94-6463-198-2
ISSN
2589-4900
DOI
10.2991/978-94-6463-198-2_59How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yuemeng Li
AU  - Chenyu Wang
AU  - Zhongchen Miao
AU  - Jian Gao
AU  - Jidong Lu
PY  - 2023
DA  - 2023/08/10
TI  - Implied Volatility Prediction of Financial Options Products Based on the CL-TCN Model
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 572
EP  - 587
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_59
DO  - 10.2991/978-94-6463-198-2_59
ID  - Li2023
ER  -