Advanced Statistical Analysis of the Predicted Volatility Levels in Crypto Markets

Vadim Azhmyakov (), Ilya Shirokov and Luz Adriana Guzman Trujillo
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Vadim Azhmyakov: 1ex Corporation, Dubai P.O. Box 9305, United Arab Emirates
Ilya Shirokov: 1ex Corporation, Dubai P.O. Box 9305, United Arab Emirates
Luz Adriana Guzman Trujillo: LARIS, Université d’Angers, 49000 Angers, France

JRFM, 2024, vol. 17, issue 7, 1-15

Abstract: Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. First, we consider the conventional GARCH-based volatility models. Next, we extend the corresponding GARCH-based forecasting and calculate a specific probability associated with the predicted volatility levels. As the probability evaluation is based on a stochastic model, we develop an advanced data-driven estimation of this probability. The novel statistical estimation we propose uses real market data. The obtained analytical results for the statistical probability of the levels are also discussed in the framework of the integrated volatility concept. The possible application of the established probability estimation approach to the volatility clustering problem is also mentioned. Our paper includes a concrete implementation of the proposed volatility prediction tool and considers a novel trading and volatility estimation module for crypto markets recently developed by the 1ex Trading Board group in collaboration with GoldenGate Venture. We also briefly discuss the possible application of a model combined with the data-driven volatility prediction methodology to financial risk management.

Keywords: technical analysis; formal volatility models; volatility prediction; statistical probability of levels; trading algorithms (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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