Babak Mahdavi-Damghani
TitleCited byYear
The Non‐Misleading Value of Inferred Correlation: An Introduction to the Cointelation Model
B Mahdavi-Damghani
Wilmott 2013 (67), 50-61, 2013
282013
The Misleading Value of Measured Correlation
B Mahdavi-Damghani, D Welch, C O'Malley, S Knights
Wilmott 2012 (62), 64-73, 2012
282012
De‐arbitraging With a Weak Smile: Application to Skew Risk
B Mahdavi-Damghani, A Kos
Wilmott 2013 (64), 40-49, 2013
242013
Introducing the Implied Volatility Surface Parametrization (IVP): Application to the FX Market
B Mahdavi-Damghani
Wilmott 2015 (77), 68-81, 2015
92015
Introducing the HFTE Model: A Multi-Species Predator Prey Ecosystem for High Frequency Quantitative Financial Strategies
B Mahdavi-Damghani
Wilmott 2017 (89), 2017
72017
A proposed risk modeling shift from the approach of stochastic differential equation towards machine learning clustering: Illustration with the concepts of anticipative …
B Mahdavi-Damghani, S Roberts
Available at SSRN 3039179, 2017
62017
The Unfortunate cosT Of Pattern rEcognition (UTOPE-ia): the Genetic Disorder of the Financial Industry
B Mahdavi-Damghani
Wilmott 2012 (60), 28-37, 2012
6*2012
Deciphering price formation in the high frequency domain: Systems & evolutionary dynamics as keys for construction of the high frequency trading ecosystem
B Mahdavi-Damghani, S Roberts
University of Oxford-Oxford-Man Institute of Quantitative Finance, 2017
52017
Portfolio optimization in the context of cointelated pairs: Stochastic differential equation vs. machine learning approach
B Mahdavi-Damghani, K Mustafayeva, S Roberts, C Buescu
Machine Learning Approach (March 4, 2018), 2018
42018
Machine Learning Techniques for Deciphering Implied Volatility Surface Data in a Hostile Environment: Scenario Based Particle Filter, Risk Factor Decomposition & Arbitrage …
B Mahdavi-Damghani, S Roberts
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3133862, 2018
32018
Convergence of Heston to SVI Proposed Extensions: Rational & Conjecture for the Convergence of Extended Heston to the Implied Volatility Surface Parametrization
B Mahdavi-Damghani, K Mustafayeva, S Roberts
Available at SSRN 3039185, 2017
32017
Data-Driven Models & Mathematical Finance: Opposition or Apposition?
B Mahdavi-Damghani
Available at SSRN 3347739, 2019
2019
A Bottom-up Approach to the Financial Markets
B Mahdavi-Damghani, S Roberts
Available at SSRN 3331423, 2019
2019
Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning
B Mahdavi-Damghani, K Mustafayeva, S Roberts, C Buescu
arXiv preprint arXiv:1812.10183, 2018
2018
Machine Learning vs. Mathematical Finance: Opposition or Apposition? Thoughts Around Reconstructing Quantitative Finance a Decade after the Subprime Derivatives & Electronic …
B Mahdavi-Damghani
University of Oxford, 2018
2018
Agent-Based Quantitative Financial Strategies & Price Formation: Systems & Evolutionary Dynamics as Deciphering Keys of the High Frequency Trading Ecosystem
B Mahdavi-Damghani, S Roberts
2018
Machine Learning Methods for Financial Forecasting: Application to the S&P 500.
B Mahdavi-Damghani
University of Oxford, 2006
2006
Financial Mathematics or Machine Learning in the context of Portfolio Optimization for Cointelated Pairs?
B Mahdavi-Damghani, K Mustafayeva, S Roberts, C Buescu
Data-Driven Models & Mathematical Finance: Opposition or Apposition?(14th Draft)
B MAHDAVI-DAMGHANI
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Articles 1–19