Optimal inventory management and order book modeling

Baradel, Nicolas; Bouchard, Bruno; Evangelista, David; Mounjid, Othmane

doi:10.1051/proc/201965145

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…Although high-frequency trading (HFT) is a relatively new development in financial markets, it has become a primary force in market pricing. A large scientific literature has developed to understand the nature of these forces (Ait-Sahalia & Saglam, 2017;Avellaneda & Stoikov, 2008;Baradely et al, 2018;Cartea et al, 2014;Cont, 2011;Cont et al, 2009;Fodra & Labadie, 2012;Gueant, 2017;Huang et al, 2014). Since HFT by definition implies fast trading, this has generally prevented complex sophisticated algorithms from being applied to trading in real time.…”

Section: Developing Bid-ask Probabilities For High-frequency Tradingmentioning

confidence: 99%

Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19

Ingber¹

2020

Preprint

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Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are described as being useful to detail such systems. Method: ASA and PATHINT/PATHTREE have been demonstrated as being effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Not only can selected systems in these three disciplines be aptly modeled, but results of detailed calculations have led to new results and insights not previously obtained. Conclusion: While optimization and path-integral algorithms are now quite well-known (at least to many scientists), these applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

show abstract

Section: Developing Bid-ask Probabilities For High-frequency Tradingmentioning

confidence: 99%

Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19

Ingber¹

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…High-frequency trading (HFT) is a relatively new development in financial markets, where it has become a primary force in market pricing. A large scientific literature has developed to understand the nature of these forces (Ait-Sahalia & Saglam, 2017; Avellaneda & Stoikov, 2008;Baradely et al, 2018;Cartea et al, 2014;Cont, 2011;Cont et al, 2009;Fodra & Labadie, 2012;Gueant, 2017;Huang et al, 2014).…”

Section: Developing Bid-ask Probabilities For High-frequency Tradingmentioning

confidence: 99%

Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19

Ingber¹

2020

Preprint

View full text Add to dashboard Cite

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are offered to detail such systems. Method: ASA and PATHINT/PATHTREE have been effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Applications are described for COVID-19. Results: Results of detailed calculations have led to new results and insights not previously obtained. Conclusion: These 3 applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

show abstract

“…Although high-frequency trading (HFT) is a relatively new development in financial markets, it has become a primary force in market pricing. A large scientific literature has developed to understand the nature of these forces (Ait-Sahalia & Saglam, 2017; Avellaneda & Stoikov, 2008;Baradely et al, 2018;Cartea et al, 2014;Cont, 2011;Cont et al, 2009;Fodra & Labadie, 2012;Gueant, 2017;Huang et al, 2014). Since HFT by definition implies fast trading, this has generally prevented complex sophisticated algorithms from being applied to trading in real time.…”

Section: Developing Bid-ask Probabilities For High-frequency Tradingmentioning

confidence: 99%

Forecasting with Importance-Sampling and Path-Integrals

Ingber¹

2020

Preprint

View full text Add to dashboard Cite

Background: Forecasting nonlinear stochastic systems most often is quite difficult, without giving in to temptations to simply simplify models for the sake of permitting simple computations. Objective: Here, two basic algorithms, Adaptive Simulated Annealing (ASA) and path-integral codes PATHINT/PATHTREE (and their quantum generalizations qPATHINT/qPATHTREE) are described as being useful to detail such systems. Method: ASA and PATHINT/PATHTREE have been demonstrated as being effective to forecast properties in three disparate disciplines in neuroscience, financial markets, and combat analysis. Results: Not only can selected systems in these three disciplines be aptly modeled, but results of detailed calculations have led to new results and insights not previously obtained. Conclusion: While optimization and path-integral algorithms are now quite well-known (at least to many scientists), these applications give strong support to a quite generic application of these tools to stochastic nonlinear systems.

show abstract

Optimal inventory management and order book modeling

Cited by 11 publications

References 26 publications

Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19

Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19

Forecasting with Importance-Sampling and Path-Integrals: Applications to COVID-19

Forecasting with Importance-Sampling and Path-Integrals

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