How Informative Is High-Frequency Data for Tail Risk Estimation and Forecasting? An Intrinsic Time Perspective

GSDS Working Paper No. 2018-04

36 Pages Posted: 11 Oct 2018

See all articles by Timo Dimitriadis

Timo Dimitriadis

University of Konstanz - Department of Economics

Roxana Halbleib

University of Konstanz

Date Written: April 27, 2018

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

Suggested Citation

Dimitriadis, Timo and Halbleib, Roxana, How Informative Is High-Frequency Data for Tail Risk Estimation and Forecasting? An Intrinsic Time Perspective (April 27, 2018). GSDS Working Paper No. 2018-04. Available at SSRN: https://ssrn.com/abstract=3251870

Timo Dimitriadis

University of Konstanz - Department of Economics ( email )

Konstanz, 78457
Germany

Roxana Halbleib (Contact Author)

University of Konstanz ( email )

Universitaetsstr. 10
Box: D 124
78457 Konstanz
Germany

HOME PAGE: http://econometrics.wiwi.uni-konstanz.de/staff/halbleib.htm

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