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Volatility models in practice: Rough, Path-dependent or Markovian?

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arxiv 2401.03345 v2 pith:LZWWXCY4 submitted 2024-01-07 q-fin.MF q-fin.CPq-fin.PR

classification q-fin.MFq-fin.CPq-fin.PR
keywords modelsroughvolatilitymarkovianmaturitieslongermodelone-factor
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abstract

We present an empirical study examining several claims related to option prices in rough volatility literature using SPX options data. Our results show that rough volatility models with the parameter $H \in (0,1/2)$ are inconsistent with the global shape of SPX smiles. In particular, the at-the-money SPX skew is incompatible with the power-law shape generated by these models, which increases too fast for short maturities and decays too slowly for longer maturities. For maturities between one week and three months, rough volatility models underperform one-factor Markovian models with the same number of parameters. When extended to longer maturities, rough volatility models do not consistently outperform one-factor Markovian models. Our study identifies a non-rough path-dependent model and a two-factor Markovian model that outperform their rough counterparts in capturing SPX smiles between one week and three years, with only 3 to 4 parameters.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Correct implied volatility shapes and reliable pricing in the rough Heston model

    q-fin.MF 2024-12 conditional novelty 7.0 of 10

    The paper shows that the rough Heston calibration in El Euch and Rosenbaum (2019) is likely a numerical artifact, and provides faster, more accurate pricing methods.

  2. Simulating integrated Volterra square-root processes and Volterra Heston models via Inverse Gaussian

    q-fin.MF 2025-04 conditional novelty 6.0 of 10

    An inverse-Gaussian implicit scheme for integrated Volterra square-root processes is proved weakly convergent and shown numerically accurate with very few time steps, including for hyper-rough fractional kernels.

  3. Pricing and Calibration of VIX Derivatives in Mixed Bergomi Models via Quantisation

    q-fin.PR 2025-06 conditional novelty 4.0 of 10

    Vector quantisation enables fast daily calibration of mixed Bergomi models to VIX futures and options, and a one-factor version may suffice.

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