Wild Bootstrap Tests for Autocorrelation in Vector Autoregressive Models

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Use this URL to link or cite this item: http://hdl.handle.net/10138/36634
Title: Wild Bootstrap Tests for Autocorrelation in Vector Autoregressive Models
Author: Ahlgren, Niklas; Catani, Paul
Belongs to series: Working Paper - 562
ISBN: 978-952-232-178-7
Abstract: Standard asymptotic and residual-based bootstrap tests for error autocorrela-
tion are unreliable in the presence of conditional heteroskedasticity. In this article
we propose wild bootstrap tests for autocorrelation in vector autoregressive mod-
els when the errors are conditionally heteroskedastic. In particular, we investigate
the properties of Lagrange multiplier tests. Monte Carlo simulations show that
the wild bootstrap tests have satisfactory size properties in models with con-
stant conditional correlation generalised autoregressive conditional heteroskedas-
tic (CCC-GARCH) errors, whereas the standard asymptotic and residual-based
bootstrap tests are oversized. The tests are applied to credit default swap prices
and Euribor interest rates.
URI: http://hdl.handle.net/10138/36634
Date: 2012-09-11
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