Rough Volatility: Fact or Artefact?

Abstract

We investigate the statistical evidence for the use of ‘rough’ fractional processes with Hurst exponent $$H< 0.5$$ H < 0.5 for modeling the volatility of financial assets, using a model-free approach. We introduce a non-parametric method for estimating the roughness of a function based on discrete sample, using the concept of normalized p-th variation along a sequence of partitions. Detailed numerical experiments based on sample paths of fractional Brownian motion and other fractional processes reveal good finite sample performance of our estimator for measuring the roughness of sample paths of stochastic processes. We then apply this method to estimate the roughness of realized volatility signals based on high-frequency observations. Detailed numerical experiments based on stochastic volatility models show that, even when the instantaneous volatility has diffusive dynamics with the same roughness as Brownian motion, the realized volatility exhibits rough behaviour corresponding to a Hurst exponent significantly smaller than 0.5. Comparison of roughness estimates for realized and instantaneous volatility in fractional volatility models with different values of Hurst exponent shows that, irrespective of the roughness of the spot volatility process, realized volatility always exhibits ‘rough’ behaviour with an apparent Hurst index $$\widehat{H}<0.5$$ H ^ < 0.5 . These results suggest that the origin of the roughness observed in realized volatility time series lies in the estimation error rather than the volatility process itself.

A Noisy Fractional Brownian Motion Model for Multiscale Correlation Analysis of High-Frequency Prices

“…In the literature, there are different approaches to modeling this. In the recent work by [16], noise is implicitly modeled based on the discrepancy between stochastic volatility and realized volatility, and it has an effect on estimating the Hurst exponent of the volatility process.…”

“…As a consequence, the market microstructure noise can potentially bias estimation of the scaling parameters. For example, as shown by [16], the discrepancy between realized and instantaneous volatility can lead to bias in the estimation of the Hurst exponent ( [17]). A common idea in various approaches is to conduct measurement at multiple timescales, and then, assimilate the outputs to arrive at an estimate (see [11,[18][19][20][21]).…”