Measuring Extreme Price Risks by Different Statistical Methods: An In-Depth Case Study in the Crude Oil Market

21 Pages Posted: 3 Aug 2020

See all articles by Quynh Trang Nguyen

Quynh Trang Nguyen

Norwegian University of Science and Technology (NTNU)

Date Written: December 22, 18

Abstract

Crude oil prices are particularly volatile. Managing such price risks is vital for participants in financial markets, in particular the oil market. In the perspective of a long position, we conduct an in-depth study of popular existing statistical approaches as well as a recently developed method to estimate Value at Risk of the next day's oil price — a measurement of potential extreme price risks. We then validate the estimations via tests of accuracy, independence, and a combination of both criteria. The approaches that capture heteroscedasticity in the data, namely conditional Extreme Value Theory and Filtered Historical Simulation, perform considerably better than the pure bootstrapping method — Historical Simulation — and the (sub)asymptotic-target approach — Average Conditional Exceedance Rate.

Keywords: Oil price volatility, Value at Risk, Extreme Value Theory, Heteroscedasticity, Bootstrapping, ACER, Backtesting

JEL Classification: C15, C22, C52, C65, G17

Suggested Citation

Nguyen, Quynh Trang, Measuring Extreme Price Risks by Different Statistical Methods: An In-Depth Case Study in the Crude Oil Market (December 22, 18). Available at SSRN: https://ssrn.com/abstract=3640738 or http://dx.doi.org/10.2139/ssrn.3640738

Quynh Trang Nguyen (Contact Author)

Norwegian University of Science and Technology (NTNU) ( email )

Høgskoleringen
Trondheim NO-7491, 7491
Norway

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