Abstract
Deviations of asset prices from the random walk dynamic imply the predictability of asset returns and thus have important implications for portfolio construction and risk management. This paper proposes a real-time monitoring device for such deviations using intraday high-frequency data. The proposed procedure is based on unit root tests with in-fill asymptotics but extended to take the empirical features of high-frequency financial data (particularly jumps) into consideration. We derive the limiting distribution of the test statistic under both the null hypothesis of a random walk with jumps and the alternative of mean reversion/explosiveness with jumps. The limiting results show that ignoring the presence of jumps could potentially lead to severe size distortions of both the left-sided (against mean reversion) and right-sided (against explosive) unit root tests. The simulation results reveal the satisfactory performance of the test even with data from a relatively short time span (i.e., one quarter). As an illustration, we apply the procedure to the Nasdaq composite index at the 10-minute frequency from January 2, 1996 to December 8, 2017. We find strong evidence of explosiveness dynamics in asset prices during the dot-com bubble and the subprime mortgage crisis periods and mean reversion in the late 2015 and early 2016.
Published Version
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