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Paper Citation Record · LEDGER

Deep learning interpretability for rough volatility

As of 19 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2411.19317.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.19317 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

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measured 49 of 49 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:34:25.385170Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-30T08:24:26.449589Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e92244b9-3c52-4a88-afbb-c5043605430c · outbound

This paper cites A bi Jaber and O.

Deep learning interpretability for rough volatility A bi Jaber and O

Reference 1

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Observation eb1d8711-664b-48e3-aa8c-f4780bb1c5cb · outbound

This paper cites A bi Jaber, M.

Deep learning interpretability for rough volatility A bi Jaber, M

Reference 2

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Observation 399e5510-e989-4927-8962-eea8f116b014 · outbound

This paper cites Deep Calibration of Interest Rates Model.

Deep learning interpretability for rough volatility Deep Calibration of Interest Rates Model

Reference 3

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Observation 839107f0-8951-45b5-b386-476e702bef7b · outbound

This paper cites A l`os, J.

Deep learning interpretability for rough volatility A l`os, J

Reference 4

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Observation 43798469-4230-4fce-a238-ea1fbcf222e8 · outbound

This paper cites B ach, A.

Deep learning interpretability for rough volatility B ach, A

Reference 5

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This paper cites B aschetti, G.

Deep learning interpretability for rough volatility B aschetti, G

Reference 6

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This paper cites B ayer, C.

Deep learning interpretability for rough volatility B ayer, C

Reference 7

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Observation 3e5025ab-e5d7-4281-a4bb-975dcba1b00c · outbound

This paper cites B ayer, P.

Deep learning interpretability for rough volatility B ayer, P

Reference 8

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Source-reported events for the cited work

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Observation 6e4093f7-182a-4766-99d3-1d403cce9829 · outbound

This paper cites B ayer, M.

Deep learning interpretability for rough volatility B ayer, M

Reference 9

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Observation 91f4b0f6-7c0a-480a-b117-65fce7802f50 · outbound

This paper cites B ayer, E.

Deep learning interpretability for rough volatility B ayer, E

Reference 10

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Observation 8d2df6a9-78df-4a45-bd05-4a211ddb90ec · outbound

This paper cites On deep calibration of (rough) stochastic volatility models.

Deep learning interpretability for rough volatility On deep calibration of (rough) stochastic volatility models

Reference 11

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Observation ffbbf449-a4ea-4da1-9cb5-2aebb63fd3ba · outbound

This paper cites B ennedsen, A.

Deep learning interpretability for rough volatility B ennedsen, A

Reference 12

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Deep learning interpretability for rough volatility Unresolved cited work

Reference 13

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Deep learning interpretability for rough volatility Unresolved cited work

Reference 14

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Observation bc125e92-357d-4557-a46c-021d7486bf5c · outbound

This paper cites B onesini, G.

Deep learning interpretability for rough volatility B onesini, G

Reference 15

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Observation c4428a7d-8c57-4d8a-922c-e21c673adad4 · outbound

This paper cites Rough volatility, path-dependent PDEs and weak rates of convergence.

Deep learning interpretability for rough volatility Rough volatility, path-dependent PDEs and weak rates of convergence

Reference 16

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Observation 2e9c7e4c-255b-4c6f-b18e-886a4b9e7fe8 · outbound

This paper cites Interpretability in deep learning for finance: a case study for the Heston model.

Deep learning interpretability for rough volatility Interpretability in deep learning for finance: a case study for the Heston model

Reference 17

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Observation ba110c40-1a08-4055-bfb7-9b3165aa932e · outbound

This paper cites C arr and D.

Deep learning interpretability for rough volatility C arr and D

Reference 18

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Observation f4fb4006-8b40-49af-a795-642650d13cb6 · outbound

This paper cites D ecreusefond and A.

Deep learning interpretability for rough volatility D ecreusefond and A

Reference 19

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Observation c88679b0-d543-411c-aa79-3f1113d53765 · outbound

This paper cites E l Euch, J.

Deep learning interpretability for rough volatility E l Euch, J

Reference 20

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Observation 10860a20-1864-43b0-8d26-d278c8820460 · outbound

This paper cites E l Euch and M.

Deep learning interpretability for rough volatility E l Euch and M

Reference 21

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This paper cites an unresolved cited work.

Deep learning interpretability for rough volatility Unresolved cited work

Reference 22

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Observation 2ab07936-f5da-4180-b3d8-3f4bf8094815 · outbound

This paper cites F ukasawa, Short-time at-the-money skew and rough fractional volatility , Quantitative Finance, 17 (2017), pp.

Deep learning interpretability for rough volatility F ukasawa, Short-time at-the-money skew and rough fractional volatility , Quantitative Finance, 17 (2017), pp

Reference 23

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Observation f14ce30e-deb0-4866-b64b-bc523174d97d · outbound

This paper cites F ukasawa andA.

Deep learning interpretability for rough volatility F ukasawa andA

Reference 24

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This paper cites G atheral, T.

Deep learning interpretability for rough volatility G atheral, T

Reference 25

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This paper cites G uennoun, A.

Deep learning interpretability for rough volatility G uennoun, A

Reference 26

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Observation a01f6b71-ada0-4f84-adde-8aa703aef63c · outbound

This paper cites H orvath, A.

Deep learning interpretability for rough volatility H orvath, A

Reference 27

Resolution
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Source-reported events for the cited work

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Observation c019d1fb-dbfe-4636-86af-6bab9d3860ed · outbound

This paper cites Deep learning calibration of option pricing models: some pitfalls and solutions.

Deep learning interpretability for rough volatility Deep learning calibration of option pricing models: some pitfalls and solutions

Reference 28

Resolution
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Observation 0b97a4e5-234a-4cc7-af31-2dd9d495cd2a · outbound

This paper cites J acquier, C.

Deep learning interpretability for rough volatility J acquier, C

Reference 29

Resolution
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Observation e4149739-f34b-4a9d-9083-0906d776f3da · outbound

This paper cites J acquier and Z.

Deep learning interpretability for rough volatility J acquier and Z

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9e3877af-1f06-4066-ae7d-be229b2591dd · outbound

This paper cites K eller-Ressel, M.

Deep learning interpretability for rough volatility K eller-Ressel, M

Reference 31

Resolution
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Source-reported events for the cited work

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Observation 8f537d4a-f416-45f2-8b33-66b943bee679 · outbound

This paper cites K eller-Ressel and A.

Deep learning interpretability for rough volatility K eller-Ressel and A

Reference 32

Resolution
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This paper cites an unresolved cited work.

Deep learning interpretability for rough volatility Unresolved cited work

Reference 33

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Observation 6446a2e8-bda4-4b48-9d7d-5080967086a2 · outbound

This paper cites L ewis, Option Valuation under Stochastic Volatility, Finance Press, Newport Beach, 2001.

Deep learning interpretability for rough volatility L ewis, Option Valuation under Stochastic Volatility, Finance Press, Newport Beach, 2001

Reference 34

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Observation f93e37be-90b6-4220-868e-d50379cf95eb · outbound

This paper cites L ipton, The vol smile problem, Risk, (2002).

Deep learning interpretability for rough volatility L ipton, The vol smile problem, Risk, (2002)

Reference 35

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation bcb33f54-67a5-473e-aa3e-cdf30b64d13a · outbound

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Deep learning interpretability for rough volatility Unresolved cited work

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Deep learning interpretability for rough volatility Unresolved cited work

Reference 37

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 261b402d-8d2c-41f2-ac78-0591d5bdc298 · outbound

This paper cites an unresolved cited work.

Deep learning interpretability for rough volatility Unresolved cited work

Reference 38

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This paper cites M iller, Explanation in Artificial Intelligence: Insights from the social sciences, Artificial intelligence, 267 (2019), pp.

Deep learning interpretability for rough volatility M iller, Explanation in Artificial Intelligence: Insights from the social sciences, Artificial intelligence, 267 (2019), pp

Reference 39

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This paper cites M olnar, Interpretable Machine Learning, 2022.

Deep learning interpretability for rough volatility M olnar, Interpretable Machine Learning, 2022

Reference 40

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Deep learning interpretability for rough volatility Unresolved cited work

Reference 41

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Deep learning interpretability for rough volatility P annier and C

Reference 42

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Deep learning interpretability for rough volatility Unresolved cited work

Reference 43

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Deep learning interpretability for rough volatility Volatility model calibration with neural networks a comparison between direct and indirect methods

Reference 44

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Reference 45

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Deep learning interpretability for rough volatility R osenbaum and J

Reference 46

Resolution
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Deep learning interpretability for rough volatility S hrikumar, P

Reference 47

Resolution
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Pith citing papers

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Integrating the implied regularity into implied volatility models: A study on free arbitrage model cites this paper.

Integrating the implied regularity into implied volatility models: A study on free arbitrage model Deep learning interpretability for rough volatility

Reference 30

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Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models cites this paper.

Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models Deep learning interpretability for rough volatility

Reference 195

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