Cont's research focuses on probability theory, stochastic analysis and mathematical modelling in finance.[12]
His mathematical work focuses on pathwise methods in stochastic analysis [13] and the Functional Ito calculus.[14]
In quantitative finance he is known in particular for his work on models based on jump processes,[15] the stochastic modelling of limit order books as queueing systems
[16]
,[17]machine learning methods in finance [18]
and the mathematical modelling of systemic risk.[19][20]
He was editor in chief of the Encyclopedia of Quantitative Finance.[21]
Cont is known in mathematics for his the "Causal functional calculus", a calculus for non-anticipative, or "causal", functionals on the space of paths.[29]
Cont and collaborators built on the seminal work of German mathematician Hans Föllmer[30] and Bruno Dupire to construct a calculus for non-anticipative functionals,[31] which includes as a special case the so-called Ito-Föllmer calculus, a pathwise counterpart of Ito's stochastic calculus.
[32]
Subsequent work by Cont and Nicolas Perkowski [33]
extended
the Ito-Föllmer calculus to functions and functionals of more general irregular paths with non-zero p-th order variation.
Systemic risk modeling
Work by Cont and his collaborators on mathematical modeling of systemic risk and financial stability, in particular on network models of financial contagion and the modeling of indirect contagion via 'fire sales', has influenced academic research and policy in this area.[23][34]
Central clearing
Cont's research on central clearing in over-the-counter (OTC) markets has influenced risk management practices of central counterparties and regulatory thinking on central clearing.[35] Cont has argued that central clearing does not eliminate counterparty risk but transforms it into liquidity risk, therefore risk management and stress testing of central counterparties should focus on liquidity risk and liquidity resources, not capital.[36]
Risk measurement and Model risk
Cont introduced a rigorous approach for the assessment of model risk[37] which has been influential in the design of model risk management frameworks in financial institutions.
[38][39]
Cont, Deguest and Scandolo[40] introduced the concept of 'risk measurement procedure', an empirical counterpart of the notion of risk measure, and defined a robust class of risk measurement procedures known as 'Range Value-at-risk' (RVaR), a robust alternative to Expected shortfall.[41]
Cont, Kotlicki and Valderrama define the concept of Liquidity at risk,[42] as the amount of liquid assets needed by a financial institution to face liquidity outflows in this scenario.
Cont, Rama; De Larrard, Adrien (2013). "Price Dynamics in a Markovian Limit Order Market". SIAM Journal on Financial Mathematics. 4 (1): 1–25. arXiv:1104.4596. doi:10.1137/110856605. S2CID1238587.
^Cont, Rama; De Larrard, Adrien (2013). "Price Dynamics in a Markovian Limit Order Market". SIAM Journal on Financial Mathematics. 4 (1): 1–25. arXiv:1104.4596. doi:10.1137/110856605. S2CID1238587.
^Cont, Rama; Stoikov, Sasha; Talreja, Rishi (2008). "A Stochastic Model for Order Book Dynamics". Operations Research. 58 (7176): 340–344. doi:10.1287/opre.1090.0780.
^Ananova, Anna; Cont, Rama (2017), "Pathwise integration with respect to paths of finite quadratic variation", Journal de Mathématiques Pures et Appliquées, 107 (6): 737–757, arXiv:1603.03305, doi:10.1016/j.matpur.2016.10.004, S2CID16318176
^Cont, Rama; Perkowski, Nicolas (2020), "Pathwise integration and change of variable formulas for continuous paths with arbitrary regularity", Transactions of the American Mathematical Society, 6 (5): 161-186, arXiv:1803.09269, doi:10.1090/btran/34
^Morini, Massimo (2012). Understanding and Managing Model Risk: A Practical Guide for Quants, Traders and Validators. Wiley. doi:10.1002/9781118467312. ISBN9781118467312.
