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Monitoring for Disruptions in Financial Markets

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  • Elena Andreou
  • Eric Ghysels

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Abstract

Historical and sequential CUSUM change-point tests for strongly dependent nonlinear processes are studied. These tests are used to monitor the conditional variance of asset returns and to provide early information regarding instabilities or disruptions in financial risk. Data-driven monitoring schemes are investigated. Since the processes are strongly dependent several novel issues require special attention. One such issue is the sampling frequency. We study the power of detection as sampling frequencies vary. Analytical local power results are obtained for historical CUSUM tests and simulation evidence is presented for sequential tests. Finally, a prediction-based statistic is introduced that reduces the detection delay considerably. The prediction based formula is based on a local Brownian bridge approximation argument and provides an assessment of the likelihood of change-points. Nous étudions les tests CUSUM historiques et séquentiels pour des séries dépendantes avec des applications en finance. Pour les processus temporels, une nouvelle dimension se présente : l'effet du choix de la fréquence des observations. Un nouveau test est également proposé. Ce test est basé sur une formule de prévision locale d'un pont brownien.

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Bibliographic Info

Paper provided by CIRANO in its series CIRANO Working Papers with number 2004s-26.

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Date of creation: 01 May 2004
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Handle: RePEc:cir:cirwor:2004s-26

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Keywords: structural change; CUSUM; GARCH; quadratic variation; power variation; high frequency data; Brownian bridge; boundary crossing; sequential tests; local power; changement structurel; CUSUM; GARCH; variation quadratique; 'power variation'; données de haute fréquence; pont Brownien; puissance locale; tests séquentiels;

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Citations

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Cited by:
  1. Elena Andreou & Eric Ghysels, 2007. "Quality Control for Structural Credit Risk Models," University of Cyprus Working Papers in Economics 3-2007, University of Cyprus Department of Economics.
  2. Anatolyev, Stanislav, 2009. "Nonparametric Retrospection and Monitoring of Predictability of Financial Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 149-160.
  3. Cizek, P. & Haerdle, W. & Spokoiny, V., 2007. "Adaptive Pointwise Estimation in Time-Inhomogeneous Time-Series Models," Discussion Paper 2007-35, Tilburg University, Center for Economic Research.

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