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Article overview
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Measuring tail risk at high-frequency: An $L_1$-regularized extreme value regression approach with unit-root predictors | Julien Hambuckers
; Li Sun
; Luca Trapin
; | Date: |
3 Jan 2023 | Abstract: | We study tail risk dynamics in high-frequency financial markets and their
connection with trading activity and market uncertainty. We introduce a dynamic
extreme value regression model accommodating both stationary and local
unit-root predictors to appropriately capture the time-varying behaviour of the
distribution of high-frequency extreme losses. To characterize trading activity
and market uncertainty, we consider several volatility and liquidity
predictors, and propose a two-step adaptive $L_1$-regularized maximum
likelihood estimator to select the most appropriate ones. We establish the
oracle property of the proposed estimator for selecting both stationary and
local unit-root predictors, and show its good finite sample properties in an
extensive simulation study. Studying the high-frequency extreme losses of nine
large liquid U.S. stocks using 42 liquidity and volatility predictors, we find
the severity of extreme losses to be well predicted by low levels of price
impact in period of high volatility of liquidity and volatility. | Source: | arXiv, 2301.01362 | Services: | Forum | Review | PDF | Favorites |
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