Vidal Llauradó, Joan (2026): A Rough Theory of Markets.
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Abstract
This paper develops a structural market theory in which roughness, systemic synchronization, crash-like episodes, and incomplete visible pricing and hedging arise from one common mechanism. The setting is an N-asset Gaussian Volterra market with directed latent contagion kernels, asymmetric information flow, and an aggregate market-volatility functional. The admissibility condition is internalized at market scale: active contagion channels must remain source-screened visible and admit a genuinely shrinking projected normalization, and the admissible class is characterized rather than stipulated. Within that class, smooth directional contagion is locally degenerate at high frequency, so roughness becomes the only stable observable contagion phase. The same dominant rough edges define a screened contagion operator, and above its Perron threshold latent stress synchronizes along a common mode and becomes macroscopically amplifying. Under threshold-triggered activation, a coarse observer can see a stochastic jump limit even though the primitive market remains continuous. The paper then shows that visible prices and visible hedges are compressions of latent market-variance risk: compression to visible pricing summaries leaves a lower-bounded convexity gap, and visible hedging of variance-linked claims leaves an explicit residual-risk lower bound whenever the synchronized latent mode retains conditional variance. Together, these results yield a unified market theory in which asymmetric latent contagion forces roughness, roughness organizes synchronization, synchronized activation can generate crash-like episodes, and visible pricing and hedging remain phase-dependent compressions of latent market-variance risk.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | A Rough Theory of Markets |
| Language: | English |
| Keywords: | rough theory of markets; latent contagion; market phases; market-variance risk; rough volatility; systemic synchronization; crash-like episodes; visible pricing summaries; hedging incompleteness; systemic risk |
| Subjects: | C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods G - Financial Economics > G0 - General > G01 - Financial Crises G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates G - Financial Economics > G1 - General Financial Markets > G13 - Contingent Pricing ; Futures Pricing G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation |
| Item ID: | 129124 |
| Depositing User: | Joan Vidal Llauradó |
| Date Deposited: | 05 Jun 2026 15:01 |
| Last Modified: | 05 Jun 2026 15:01 |
| References: | E. Abi Jaber, M. Larsson, and S. Pulido, Affine Volterra processes, The Annals of Applied Probability 29(5):3155–3200, 2019. T. Adrian and M. K. Brunnermeier, CoVaR, American Economic Review 106(7):1705–1741, 2016. C. Bayer, P. Friz, and J. Gatheral, Pricing under rough volatility, Quantitative Finance 16(6):887–904, 2016. C. Brownlees and R. F. Engle, SRISK: A conditional capital shortfall measure of systemic risk, The Review of Financial Studies 30(1):48–79, 2017. R. Cont and P. Das, Rough volatility: Fact or artefact?, Sankhyā B 86(1):191–223, 2024. F. X. Diebold and K. Yılmaz, Measuring financial asset return and volatility spillovers, with application to global equity markets, The Economic Journal 119(534):158–171, 2009. F. X. Diebold and K. Yılmaz, On the network topology of variance decompositions: Measuring the connectedness of financial firms, Journal of Econometrics 182(1):119–134, 2014. O. El Euch and M. Rosenbaum, The characteristic function of rough Heston models, Mathematical Finance 29(1):3–38, 2019. K. J. Forbes and R. Rigobon, No contagion, only interdependence: Measuring stock market comovements, The Journal of Finance 57(5):2223–2261, 2002. J. Gatheral, T. Jaisson, and M. Rosenbaum, Volatility is rough, Quantitative Finance 18(6):933–949, 2018. L. Le Cam and G. L. Yang, Asymptotics in Statistics: Some Basic Concepts, 2nd ed., Springer, New York, 2000. G. Livieri, S. Mouti, A. Pallavicini, and M. Rosenbaum, Rough volatility: Evidence from option prices, IISE Transactions 50(9):767–776, 2018. A. W. van der Vaart, Asymptotic Statistics, Cambridge University Press, 1998. J. Vidal Llauradó, Dynamic Observability of Latent Contagion in Rough Volatility Models: Sequential Revelation and Observable Screening, Zenodo, 2026. DOI: 10.5281/zenodo.19473921. J. Vidal Llauradó, Detecting Latent Volatility Contagion: A Projected Score Estimator for Rough Volatility Models, Zenodo, 2026. DOI: 10.5281/zenodo.19545994. J. Vidal Llauradó, Latent Volatility Contagion in Rough Volatility Models: Spectral Thresholds and Detectability, Zenodo, 2026. DOI: 10.5281/zenodo.19473771. |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/129124 |
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A Rough Theory of Markets. (deposited 15 May 2026 14:49)
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A Rough Theory of Markets. (deposited 15 May 2026 15:22)
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A Rough Theory of Markets. (deposited 15 May 2026 15:22)

