Ankush Agarwal

Associate Professor
PhD, Tata Institute of Fundamental Research, 2015
Office: WSC 227
Phone: x89731
Email: aagarw93@uwo.ca


 

Research areas

  • Mathematical finance
  • Data science in finance
  • Financial statistics
  • Monte Carlo methods

 

Teaching

  • FM9593B: Monte Carlo Methods and Financial Applications
  • FM4521B/9521B: Advanced Financial Modelling

 

Graduate Student Supervision

  • Ying Liao (University of Glasgow, UK)
  • Buchun Wang (University of Glasgow, UK)
  • Shuya Zhang (University of Glasgow, UK)

 

Publications

Journals 

  • Penalized estimation of sparse Markov regime-switching vector auto-regressive models with  Chavez Martinez, A. Khalili and S. Ejaz Ahmed. Technometrics. 2023, Vol. 65, No. 4, pp. 553-563.
  • Hedging longevity risk in defined contribution pension schemes(2023) with C-O. Ewald and Y. Wang*. Computational Management Science. 2023, Vol. 20.
  • A Fourier-based Picard-iteration approach for a class of McKean-Vlasov SDEs with Lévy jumps with  Pagliarani. Stochastics. 2021, Vol. 93, No. 4, pp. 592-624.
  • The implied Sharpe ratio with  Lorig. Quantitative Finance. 2020,Vol. 20, No. 6, pp. 1009-1026.
  • Branching diffusion representation of semi-linear elliptic PDEs and estimation using Monte Carlo method with  Claisse.Stochastic Processes and Their Applications. 2020, Vol. 130, No. 8, pp. 5006-5036.
  • Numerical approximation of McKean Anticipative BSDEs arising in initial margin requirements with  De Marco, E. Gobet, J.G. Lopéz-Salas, F. Noubiagain and A. Zhou. ESAIM: Proceedings and Surveys. 2019, Vol. 65, No. 1, pp. 1- 26.
  • Portfolio benchmarking under drawdown constraint and stochastic Sharpe ratio with  Sircar.SIAM Journal on Financial Mathematics. 2018, Vol. 9, No. 2, pp. 435- 464.
  • Study of new rare event simulation schemes and their application to scenario generation with  De Marco, E. Gobet and G. Liu.  Mathematics and Computers in Simulation. 2018, Vol. 143, Supp. C, pp. 89- 98.
  • American options under stochastic volatility: Control variates, randomization and multiscale asymptotics with Juneja and R. Sircar. Quantitative Finance. 2016, Vol. 16, No. 01, pp. 17-30.
  • Nearest neighbor based estimation technique for pricing Bermudan options with  Juneja. International Game Theory Review.2015, Vol. 17, No. 01, pp. 154002.
  • Efficient simulation of large deviations events for sums of random vectors using saddle point representations with  Dey and S. Juneja.Journal of Applied Probability. 2013, Vol. 50, No. 03, pp. 703-720.

 

Peer-reviewed proceedings

  • Finite variance unbiased estimation of stochastic differential equations with Gobet. Proceedings of 2017 Winter Simulation Conference. pp. 1950-1961.
  • Comparing optimal convergence rate of stochastic mesh and least squares method for Bermudan option pricing with  Juneja. Proceedings of 2013 Winter Simulation Conference.pp. 701-712.