Risk Analysis of Cumulative Intraday Return Curves

Piotr Kokoszka 1 , Hong Miao 2 , Stilian Stoev 3  and Ben Zheng 4
  • 1 Department of Statistics, Colorado State University, CO 80523, Fort Collins, USA
  • 2 Department of Finance and Real Estate, Colorado State University, CO 80523, Fort Collins, USA
  • 3 Department of Statistics, University of Michigan, MI 48109, Ann Arbor, USA
  • 4 Department of Statistics, Colorado State University, CO 80523, Fort Collins, USA
Piotr Kokoszka, Hong Miao
  • Department of Finance and Real Estate, Colorado State University, Fort Collins, CO 80523, USA
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, Stilian Stoev and Ben Zheng

Abstract

Motivated by the risk inherent in intraday investing, we propose several ways of quantifying extremal behavior of a time series of curves. A curve can be extreme if it has shape and/or magnitude much different than the bulk of observed curves. Our approach is at the nexus of functional data analysis and extreme value theory. The risk measures we propose allow us to assess probabilities of observing extreme curves not seen in a historical record. These measures complement risk measures based on point-to-point returns, but have different interpretation and information content. Using our approach, we study how the financial crisis of 2008 impacted the extreme behavior of intraday cumulative return curves. We discover different impacts on shares in important sectors of the US economy. The information our analysis provides is in some cases different from the conclusions based on the extreme value analysis of daily closing price returns.

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The Journal of Time Series Econometrics (JTSE) serves as an internationally recognized outlet for important new research in both theoretical and applied classical and Bayesian time series, spatial and panel data econometrics. The scope of the journal includes papers dealing with estimation, testing and other methodological aspects involved in the application of time series and spatial analytic techniques to economic, financial and related data.

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