This Quantcast episode is dedicated to rough volatility models, which originated from attempts to model volatility without assuming its surface is smooth, as implied by, for example, the Black-Scholes model.
Our guest, Mathieu Rosenbaum, head of analytics and models for regulation at the department of applied mathematics of the École Polytechnique, is one of the authors who introduced the concept of rough volatility to finance in 2014, together with Jim Gatheral and Thibault Jaisson (Gatheral et al, 2014), and has contributed to develop models in this area since.
According to Rosenbaum, these models are set to become more popular because of their ability to describe markets’ volatility accurately. “It is very hard to reject that volatility is rough on historical data and it is very hard to reject that volatility is rough on implied volatility data,” says Rosenbaum.
In addition, he points out their specification is very parsimonious. There are only three parameters: volatility level, volatility of volatility and spot/vol correlation.
His latest work, Roughening Heston, is co-authored with Omar El Euch and Jim Gatheral. It extends the original rough volatility model combining it with the classical Heston model.
Rosenbaum explains the issues related to applicability and implementation of the rough Heston model. Also, he offers his answers to some of the criticisms the model has received, such as the limited applicability of a non-Markovian model and the precision of the fitting of a model with only three parameters.
01:15 What is rough volatility?
02:25 Development of the model
04:15 Advantages of rough volatility
06:32 Rough Heston paper
11:25 Rough volatility with other volatility models
12:50 What products can rough volatility be applied to
13:35 Limited applicability of non-Markovian models?
16:25 Poor man’s rough Heston model
18:24 Fitting precision
21:20 Are banks using it?
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