Why is the volatility of single stocks so much rougher than that of the S&P500?

TL;DR

This paper introduces a nested factor model with rough and super-rough volatility modes to explain the difference in volatility roughness between individual stocks and the S&P500 index, supported by a new estimation procedure.

Contribution

It presents a novel nested factor model with rough volatility modes and a consistent statistical method to estimate Hurst exponents from stock data.

Findings

The model accounts for the higher Hurst exponents in stock indexes.

Estimated roughness exponents validate the model assumptions.

Application to S&P500 data confirms the model's effectiveness.

Abstract

The Nested factor model was introduced by Chicheportiche et al. to represent non-linear correlations between stocks. Stock returns are explained by a standard factor model, but the (log)-volatilities of factors and residuals are themselves decomposed into factor modes, with a common dominant volatility mode affecting both market and sector factors but also residuals. Here, we consider the case of a single factor where the only dominant log-volatility mode is rough, with a Hurst exponent $H \simeq 0.11$ and the log-volatility residuals are ''super-rough'' or ''multifractal'', with $H \simeq 0$. We demonstrate that such a construction naturally accounts for the somewhat surprising stylized fact reported by Wu et al. , where it has been observed that the Hurst exponents of stock indexes are large compared to those of individual stocks. We propose a statistical procedure to estimate the Hurst factor exponent from the stock returns dynamics together with theoretical guarantees of its consistency. We demonstrate the effectiveness of our approach through numerical experiments and apply it to daily stock data from the S&P500 index. The estimated roughness exponents for both the factor and idiosyncratic components validate the assumptions underlying our model.