Market Implied Conformal Volatility Intervals

Alejandro Canete
Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 204:89-99, 2023.

Abstract

Volatility is a fundamental input for pricing and risk management of nancial instruments. In the following work we propose an algorithm to estimate the market implied uncertainty of future realized volatility. Our method interprets the market implied volatility as a point prediction of future realized volatility and applies online conformal prediction to estimate the uncertainty of this prediction. We analyze rolling coverage and width of several nonconformity scores over 15 years of daily data. The results suggest that conformal prediction can be used to infer market implied prediction intervals for realized volatility.

Cite this Paper

BibTeX
@InProceedings{pmlr-v204-canete23a, title = {Market Implied Conformal Volatility Intervals}, author = {Canete, Alejandro}, booktitle = {Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {89--99}, year = {2023}, editor = {Papadopoulos, Harris and Nguyen, Khuong An and Boström, Henrik and Carlsson, Lars}, volume = {204}, series = {Proceedings of Machine Learning Research}, month = {13--15 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v204/canete23a/canete23a.pdf}, url = {https://proceedings.mlr.press/v204/canete23a.html}, abstract = {Volatility is a fundamental input for pricing and risk management of nancial instruments. In the following work we propose an algorithm to estimate the market implied uncertainty of future realized volatility. Our method interprets the market implied volatility as a point prediction of future realized volatility and applies online conformal prediction to estimate the uncertainty of this prediction. We analyze rolling coverage and width of several nonconformity scores over 15 years of daily data. The results suggest that conformal prediction can be used to infer market implied prediction intervals for realized volatility. } }
Endnote
%0 Conference Paper %T Market Implied Conformal Volatility Intervals %A Alejandro Canete %B Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2023 %E Harris Papadopoulos %E Khuong An Nguyen %E Henrik Boström %E Lars Carlsson %F pmlr-v204-canete23a %I PMLR %P 89--99 %U https://proceedings.mlr.press/v204/canete23a.html %V 204 %X Volatility is a fundamental input for pricing and risk management of nancial instruments. In the following work we propose an algorithm to estimate the market implied uncertainty of future realized volatility. Our method interprets the market implied volatility as a point prediction of future realized volatility and applies online conformal prediction to estimate the uncertainty of this prediction. We analyze rolling coverage and width of several nonconformity scores over 15 years of daily data. The results suggest that conformal prediction can be used to infer market implied prediction intervals for realized volatility.
APA
Canete, A.. (2023). Market Implied Conformal Volatility Intervals. Proceedings of the Twelfth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 204:89-99 Available from https://proceedings.mlr.press/v204/canete23a.html.

Related Material