Julien Hambuckers, Li Sun, Luca Trapin
arXiv 3 Jan 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2301.01362 · PDF · DOI · OpenAlex · Extracted main text
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.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Lee, J. H., Shi, Z., and Gao, Z (2022) On lasso for predictive regression | 0.928 | 4 | 3 | 100% |
| 2 | Brogaard, J., Carrion, A., Moyaert, T., Riordan, R., Shkilko, A., an… (2018) High frequency trading and extreme price movements | 0.811 | 4 | 2 | 100% |
| 3 | Goyenko, R. Y., Holden, C. W., and Trzcinka, C. A (2009) Do liquidity measures measure liquidity? | 0.737 | 3 | 2 | 100% |
| 4 | Massacci, D (2017) Tail risk dynamics in stock returns: Links to the macroeconomy and global markets connectedness | 0.737 | 3 | 2 | 100% |
| 5 | Bee, M., Dupuis, D. J., and Trapin, L (2019) Realized peaks over threshold: A time-varying extreme value approach with high-frequency-based measures self | 0.644 | 2 | 2 | 100% |
| 6 | Schwaab, B., Lucas, A., and Zhang, X (2021) Modeling extreme events: time-varying extreme tail shape | 0.644 | 2 | 2 | 100% |
| 7 | Smith, R. L (1985) Maximum likelihood estimation in a class of nonregular cases | 0.585 | 3 | 1 | 100% |
| 8 | Chavez-Demoulin, V., Embrechts, P., and Hofert, M (2016) An extreme value approach for modeling operational risk losses depending on covariates | 0.511 | 2 | 1 | 100% |
| 9 | Coles, S (2001) An introduction to statistical modeling of extreme values | 0.511 | 2 | 1 | 100% |
| 10 | Lee, J. H (2016) Predictive quantile regression with persistent covariates: IVX-QR approach | 0.511 | 2 | 1 | 100% |
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