Uncertainty Shocks of Trump Election in an Interval Model of Stock Market

This paper proposes a new class of nonlinear interval models for interval-valued time series (ITS). By matching the interval model with interval observations, we develop a nonlinearminimum-distance estimation method for the proposed models, and establish the asymptotictheory for the proposed estimators. Superior to traditional point-based methods, the proposed interval modelling approach can assess the change in both the trend and volatilitysimultaneously. Within the proposed interval framework, this paper examines the impactof the 2016 US presidential election (henceforth Trump election) on US stock market as acase study. Considering the validity of daily high-low range as a proxy of market efficiency,we employ an interval-valued return to jointly measure the fundamental value movementand market efficiency simultaneously. Empirical results suggest a strong evidence that theTrump election has increased the level/trend and lowered the volatility of S&P 500 index inboth ex ante and ex post analysis. Furthermore, a longer half-life period for the impact onfundamental value (62.4 days) than high-low range (15.9 days) has shown that the impact ofTrump's victory on fundamental value is more persistent than its impact on market efficiency