How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice

Roxana Halbleib () and Timo Dimitriadis

VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy from Verein für Socialpolitik / German Economic Association

Abstract: This paper proposes a novel and simple approach to compute daily Value at Risk (VaR) and Expected Shortfall (ES) directly from high-frequency data. It assumes that financial logarithm prices are subordinated unifractal processes in the intrinsic time, which stochastically transforms the clock time in accordance with the markets activity. This is a very general assumption that allows for a simple computation of daily VaR and ES by scaling up their intraday counterparts computed from data sampled in intrinsic time. In the empirical exercise, we discuss the statistical and dynamic properties of the resulting daily VaR and ES estimates and show that our method outperforms standard ones in accurately estimating and forecasting VaR and ES.

Keywords: Value at Risk; Expected Shortfall; Intrinsic Time; Subordinated Process; High-Frequency Data; Scaling Law (search for similar items in EconPapers)
JEL-codes: C1 C4 C5 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mst and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:vfsc19:203669

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