A0167
Title: A nonparametric rough volatility test
Authors: Carsten Chong - HKUST (Hong Kong) [presenting]
Viktor Todorov - Northwestern University (United States)
Abstract: A nonparametric test is developed for deciding whether volatility follows a semimartingale process or a rough process with paths of infinite quadratic variation. Drawing on the fact that volatility is rough if and only if changes in volatility are negatively correlated, our test is based on the sample autocovariance of increments of spot volatility estimates computed from high-frequency return data on a fixed time interval. By showing a feasible CLT for this test statistic under the null hypothesis of semimartingale volatility paths, we construct a test with fixed asymptotic size and asymptotic power equal to one. The test is derived under very general conditions on the data-generating process. In particular, it is robust to jumps of arbitrary activity and market microstructure noise. In an application, we apply the test to SPY high-frequency data.
