Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets

Dean Fantazzini

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigates the estimation of the Value-at-Risk (VaR) across various probability levels for the log-returns of a comprehensive dataset comprising four thousand crypto-assets. Employing four recently introduced Adaptive Conformal Inference (ACI) algorithms, we aim to provide robust uncertainty estimates crucial for effective risk management in financial markets. We contrast the performance of these ACI algorithms with that of traditional benchmark models, including GARCH models and daily range models. Despite the substantial volatility observed in the majority of crypto-assets, our findings indicate that ACI algorithms exhibit notable efficacy. In contrast, daily range models, and to a lesser extent, GARCH models, encounter challenges related to numerical convergence issues and structural breaks. Among the ACI algorithms, the Fully Adaptive Conformal Inference (FACI) and the Scale-Free Online Gradient Descent (SF-OGD) stand out for their ability to provide precise VaR estimates across all quantiles examined. Conversely, the Aggregated Adaptive Conformal Inference (AgACI) and the Strongly Adaptive Online Conformal Prediction (SAOCP) demonstrate proficiency in estimating VaR for extreme quantiles but tend to be overly conservative for higher probability levels. These conclusions withstand robustness checks encompassing the market capitalization of crypto-assets, time series size, and different forecasting methods for asset log-returns. This study underscores the promise of ACI algorithms in enhancing risk assessment practices in the context of volatile and dynamic crypto-asset markets.

Keywords: Value at Risk (VaR); Adaptive Conformal Inference (ACI); Aggregated Adaptive Conformal Inference (AgACI); Fully Adaptive Conformal Inference (FACI); Scale-Free Online Gradient Descent (SF-OGD); Strongly Adaptive Online Conformal Prediction (SAOCP), GARCH; Daily Range; Risk Management (search for similar items in EconPapers)
JEL-codes: C14 C51 C53 C58 G17 G32 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-ecm, nep-pay and nep-rmg
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Journal Article: Adaptive Conformal Inference for Computing Market Risk Measures: An Analysis with Four Thousand Crypto-Assets (2024)
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