Azhmyakov, V.; Shirokov, I.; Guzman Trujillo, L.A. Advanced Statistical Analysis of the Predicted Volatility Levels in Crypto Markets. Preprints2024, 2024041838. https://doi.org/10.20944/preprints202404.1838.v1
APA Style
Azhmyakov, V., Shirokov, I., & Guzman Trujillo, L.A. (2024). Advanced Statistical Analysis of the Predicted Volatility Levels in Crypto Markets. Preprints. https://doi.org/10.20944/preprints202404.1838.v1
Chicago/Turabian Style
Azhmyakov, V., Ilya Shirokov and Luz Adriana Guzman Trujillo. 2024 "Advanced Statistical Analysis of the Predicted Volatility Levels in Crypto Markets" Preprints. https://doi.org/10.20944/preprints202404.1838.v1
Abstract
Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. Firstly, we consider the conventional GARCH involved volatility models. We next extend the corresponding GARCH based forecasting and calculate a specific probability associated with the predicted volatility levels. Since the probability evaluation is based on a stochastic model, we next develop an additional data-driven estimation of this probability. The proposed advanced statistical estimation uses real (historical) market data. The obtained theoretic results for the statistical probability of levels are also discussed in the framework of the integrated volatility concept. A possible application of the established probability estimation to the volatility clustering problem is also mentioned. Our paper includes a concrete practical implementation of the resulting (combined) volatility prediction tool and considers a novel module for trading and volatility estimation in crypto markets recently developed by 1ex Trading Board group in collaboration with the GoldenGate Venture. Moreover, we discuss shortly a possible using of the proposed model based and data-driven volatility prediction methodology in the financial risk management.
Keywords
technical analysis; formal volatility models; volatility prediction; statistical probability of levels; trading algorithms
Subject
Business, Economics and Management, Finance
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
