Guidelines for Building a Realistic Algorithmic Trading Market Simulator for Backtesting While Incorporating Market Impact
Babak Mahdavi-Damghani and Stephen Roberts, Algorithmic Finance, Volume 11, Issue 1, pages 1–25, 2023.
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Validating the PNL in NLP
Babak Mahdavi-Damghani, 2023.
Cryptocurrency Sectorisation Through Clustering and Web-Scraping: Application to Systematic Trading
Babak Mahdavi-Damghani, R. Fraser, J. Howell and J. S. Halldorsson, The Journal of Financial Data Science, 2021.
Doctoral Thesis: Data-Driven Models & Mathematical Finance: Apposition or Opposition?
Babak Mahdavi-Damghani, University of Oxford, 2020.
Addendum on How Many Times Cointelated Pairs Cross Paths
Babak Mahdavi-Damghani and Stephen Roberts, SSRN Working Paper 3550931, 2020.
A Bottom-Up Approach to the Financial Markets: Agent-Based Quantitative Algorithmic Strategies, Ecosystem, Dynamics & Detection
Babak Mahdavi-Damghani, University of Oxford, 2019.
Presentation: A Bottom-Up Approach to the Financial Markets: Agent-Based Quantitative Algorithmic Strategies, Ecosystem, Dynamics & Detection
downloadPortfolio Optimization for Cointelated Pairs: SDEs versus Machine Learning
Babak Mahdavi-Damghani, Konul Mustafayeva, Stephen Roberts and Cristin Buescu, arXiv preprint arXiv:1812.10183, 2018.
Machine Learning Techniques for Deciphering Implied Volatility Surface Data in a Hostile Environment: Scenario-Based Particle Filtering, Risk-Factor Decomposition & Arbitrage-Constraint Sampling
Babak Mahdavi-Damghani and Stephen Roberts, University of Oxford, Oxford-Man Institute of Quantitative Finance, 2018.
A Proposed Risk-Modelling Shift from the Approach of Stochastic Differential Equations towards Machine-Learning Clustering: Illustration with the Concepts of Anticipative & Responsible VaR
Babak Mahdavi-Damghani and Stephen Roberts, SSRN Working Paper 3039179, 2017.
Convergence of Heston to SVI Proposed Extensions: Rational & Conjecture for the Convergence of Extended Heston to the Implied Volatility Surface Parametrization
Babak Mahdavi-Damghani, Konul Mustafayeva and Stephen Roberts, SSRN Working Paper 3039185, 2017.
Deciphering Price Formation in the High-Frequency Domain: Systems & Evolutionary Dynamics as Keys for the Construction of the High-Frequency Trading Ecosystem
Babak Mahdavi-Damghani, 2017.
Introducing the HFTE Model: A Multi-Species Predator-Prey Ecosystem for High-Frequency Quantitative Financial Strategies
Babak Mahdavi-Damghani, Wilmott, Issue 89, 2017.
Presentation: Introducing the HFTE Model: A Multi-Species Predator-Prey Ecosystem for High-Frequency Quantitative Financial Strategies
downloadVideo presentation:
Introducing the Implied Volatility Surface Parametrization, IVP: Application to the FX Market
Babak Mahdavi-Damghani, Wilmott, Issue 77, pages 68–81, 2015.
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CQF Presentation: Introducing the Implied Volatility Surface Parametrization, IVP: Application to Options Statistical Arbitrage & Risk Management
downloadThe Non-Misleading Value of Inferred Correlation: An Introduction to the Cointelation Model
Babak Mahdavi-Damghani, Wilmott, Issue 67, pages 50–61, 2013.
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De-Arbitraging with a Weak Smile: Application to Skew Risk
Babak Mahdavi-Damghani and A. Kos, Wilmott, Issue 64, pages 40–49, 2013.
The Misleading Value of Measured Correlation
Babak Mahdavi-Damghani, D. Welch, C. O’Malley and S. Knights, Wilmott, Issue 62, pages 64–73, 2012.
The Unfortunate cosT Of Pattern rEcognition, UTOPE-ia: The Genetic Disorder of the Financial Industry
Babak Mahdavi-Damghani, Wilmott, Issue 60, pages 28–37, 2012.
Presentation: The Unfortunate cosT Of Pattern rEcognition, UTOPE-ia
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Sequential Monte Carlo Methods Applied to Multi-Target Tracking
Babak Mahdavi-Damghani, University of Cambridge, 2009.
Machine Learning Methods for Financial Forecasting: Application to the S&P 500
Babak Mahdavi-Damghani, University of Oxford Computing Laboratory, 2006.
Design of Agent-Injury Modeling
B. Silverman, G. K. Bharathy, Babak Mahdavi-Damghani, E. Kim and L. Lambert, University of Pennsylvania, Philadelphia, 2003.