Working Paper: NBER ID: w28570
Authors: Torben G. Andersen; Rasmus T. Varneskov
Abstract: 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.
Keywords: parameter instability; structural change; predictive regressions; volatility forecasting
JEL Codes: G12; G17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| implied volatility (iv) (C26) | realized volatility (rv) (G17) |
| time-series forecasts (C53) | predictive accuracy (C52) |
| realized volatility (rv) (G17) | volatility risk premium predictive power (G17) |
| global financial crisis of 2008-2009 (F65) | volatility risk premium predictive power (G17) |
| implied volatility (iv) (C26) | predictive accuracy (C52) |