SMU SOE Online Seminar (Nov 26, 2021, 4.00pm-5.30pm): Consistent Local Spectrum (LCM) Inference for Predictive Return Regressions

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TOPIC:

CONSISTENT LOCAL SPECTRUM (LCM) INFERENCE FOR PREDICTIVE RETURN REGRESSIONS

ABSTRACT This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a latent persistent conditional mean, whose memory is difficult to estimate consistently by standard techniques in finite samples. Moreover, the predictors may be endogenous and ``imperfect". In this setting, we show that the Local speCtruM (LCM) procedure is consistent and delivers asymptotically Gaussian inference. Furthermore, we provide a new LCM-based estimator of the conditional mean persistence that leverages biased regression slopes as well as new LCM-based tests for significance of (a subset of) the predictors, which are valid even without estimating the return persistence. Simulations illustrate the theoretical arguments. Finally, in an empirical application to monthly S\&P 500 return predictions, we provide evidence for a fractionally integrated conditional mean component. Moreover, using our new LCM procedure and tools, we document significant predictive power for key state variables such as the discount rates and the default spread. Click here to view the speaker's profile.		This seminar will be held virtually via Zoom. A confirmation email with the Zoom details will be sent to the registered email by 25 November 2021.
		PRESENTER Rasmus Tangsgaard Varneskov Copenhagen Business School
		RESEARCH FIELDS Econometrics Statistics Asset Pricing Financial Economics
		DATE: 26 November 2021 (Friday)
		TIME: 4.00pm - 5.30pm

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