Endogenous Markov Switching Regression Models for High-Frequency Data Under Microstructure Noise

40 Pages Posted: 29 May 2015 Last revised: 31 May 2015

See all articles by Markus Leippold

Markus Leippold

University of Zurich; Swiss Finance Institute

Felix Matthys


Date Written: May 30, 2015


We present a novel method in analyzing microstructure noise of high-frequency data as a measurement error problem within an endogenous Markov-switching regression model. In this model, the regression disturbance and the latent state variable controlling the regime are correlated. We show that under endogeneity the popular realized variance estimator is biased and no longer converges to the integrated regime dependent volatility. Exploring intraday return data on foreign exchange rates, we find significant endogeneity at high frequencies. Similar to the popular volatility signature plot suggested by Andersen, Bollerslev, Diebold, and Labys (2000b), we propose an endogeneity plot, which indicates as to which sampling frequency the assumption of exogeneity of the state variable controlling the regime remains valid.

Keywords: Endogeneous regime switching, microstructure noise, realized volatility, endogeneity plot.

JEL Classification: C13, C32

Suggested Citation

Leippold, Markus and Matthys, Felix, Endogenous Markov Switching Regression Models for High-Frequency Data Under Microstructure Noise (May 30, 2015). Available at SSRN: https://ssrn.com/abstract=2611154 or http://dx.doi.org/10.2139/ssrn.2611154

Markus Leippold

University of Zurich ( email )

Rämistrasse 71
Zürich, CH-8006

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4

Felix Matthys (Contact Author)

ITAM ( email )

Av. Camino a Sta. Teresa 930
Col. Héroes de Padierna
Mexico City, D.F. 01000, Federal District 01080
+52 155 1394 6562 (Phone)

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