G2TT
来源类型Working Paper
规范类型报告
DOI10.3386/w28570
来源IDWorking Paper 28570
Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions
Torben G. Andersen; Rasmus T. Varneskov
发表日期2021-03-22
出版年2021
语种英语
摘要This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and multiple structural changes with unknown break dates, and the number of breaks being known or unknown. We establish asymptotic limit theory for the tests, showing that it coincides with standard testing procedures. As a consequence, existing critical values for tied-down Bessel processes may be applied, without modification. We implement the new structural change tests to explore the stability of the fractionally cointegrating relation between implied- and realized volatility (IV and RV). Moreover, we assess the relative efficiency of IV forecasts against a challenging time-series benchmark constructed from high-frequency data. Unlike existing studies, we find evidence that the IV-RV cointegrating relation is unstable, and that carefully constructed time-series forecasts are more efficient than IV in capturing low-frequency movements in RV.
主题Financial Economics ; Portfolio Selection and Asset Pricing ; Financial Markets
URLhttps://www.nber.org/papers/w28570
来源智库National Bureau of Economic Research (United States)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/586244
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GB/T 7714
Torben G. Andersen,Rasmus T. Varneskov. Testing for Parameter Instability and Structural Change in Persistent Predictive Regressions. 2021.
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