Nonparametric Test for Rough Volatility
提出一种非参数检验方法，判断资产波动率是标准半鞅过程还是粗糙过程，利用高频数据中波动率增量的负自相关特性，并证明检验在一般条件下有效，应用于高频金融数据发现粗糙波动率证据。
We develop a nonparametric test for deciding whether volatility of an asset follows a standard semimartingale process, with paths of finite quadratic variation, or a rough process with paths of infinite quadratic variation. The test uses the fact that volatility is rough if and only if volatility increments are negatively autocorrelated at high frequencies. It is based on the sample autocovariance of increments of spot volatility estimates computed from high-frequency asset return data. By showing a feasible CLT for this statistic under the null hypothesis of semimartingale volatility paths, we construct a test with fixed asymptotic size and an asymptotic power equal to one. The test is derived under very general conditions for the data-generating process. In particular, it is robust to jumps with arbitrary activity and to the presence of market microstructure noise. In an application of the test to high-frequency financial data, we find evidence for rough volatility. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.