Decoupling the Short- and Long-Term Behavior of Stochastic Volatility

By Mikkel Bennedsen, Asger Lunde, Mikko S. Pakkanen. Journal of Financial Econometrics, 2016

155 citations recorded by the source corpus.

We introduce a new class of continuous-time models of the stochastic volatility of asset prices. The models can simultaneously incorporate roughness and slowly decaying autocorrelations, including proper long memory, which are two stylized facts often found in volatility data. Our prime model is based on the so-called Brownian semistationary process and we derive a number of theoretical properties of this process, relevant to volatility modeling. Applying the models to realized volatility measures covering a vast panel of assets, we find evidence consistent with the hypothesis that time series of realized measures of volatility are both rough and very persistent. Lastly, we illustrate the utility of the models in an extensive forecasting study; we find that the models proposed in this article outperform a wide array of benchmarks considerably, indicating that it pays off to exploit both roughness and persistence in volatility forecasting.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/5ecb0a1813683558db30f174c5940099f4bed9ea

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Citations in the corpus

Rough volatility of Bitcoin (2019, background): “Further empirical studies confirmed the roughness of the log-volatility for thousands of stocks(Bennedsen et al., 2016) and implied volatility(Livieri et al., 2018).”
Rough Volatility: Fact or Artefact? (2022, unknown): “Bennedsen et al. (2022) discuss several such approaches.”
Lifting the Heston model (2018, methodology): “In what follows, we provide a quantitative analysis of the previous observation by running two standard statistical experiments that have been used in Bennedsen et al. (2016) and Gatheral et al. (2018) to estimate the roughness of a realized volatility time series. More precisely, the empirical stud”
No‐arbitrage implies power‐law market impact and rough volatility (2018, unknown): “volatility, rough Heston model, hyper-rough Heston model, Hawkes processes. 1 Introduction It is now well-admitted that volatility is rough. This stylized fact rst established in [15] and conrmed in [4, 24] means that the (log-)volatility process of an asset essentially behaves as a fractional Brown”
A regularity structure for rough volatility (2017, background): “…be easily mixed, so that rough volatility governs the short time behavior, while trending volatility affects the long time behavior; we refer to Comte and Renault (1998), Comte, Coutin, and Renault (2012), Alòs and Yang (2017), and Bennedsen, Lunde, and Pakkanen (2016) for more information on this.”