Andrew Thomas Lesniewski

Professor

Weissman School of Arts and Sciences

Department: Mathematics

Areas of expertise:

Email Address: andrew.lesniewski@baruch.cuny.edu

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Journal Articles

Hagan, P., Lesniewski, A., Skoufis, G. E., & Woodward, D. (2019). Convexity without Replication. Wilmott,

Hagan, P., & Lesniewski, A. (2019). Bartlett's Delta in the SABR Model. Wilmott, May(101). 54 - 61.

Hagan, P., Lesniewski, A., Woodward, D., & Skoufis, G. E. (2019). Explicit Pricing of Quadratic Derivatives under SABR. Wilmott,

Lesniewski, A., & Richter, A. (2018). Portfolio Optimization Under Uncertain Parameters. In Progress.

Hagan, P., Lesniewski, A., & Woodward, D. (2018). Implied Volatility for Heston Models. Wilmott, November(98). 44 - 57.

Lesniewski, A., Hagan, P., & Richter, A. (2018). FKK filtering for observations with state dependent diffusion coefficients. In Progress.

Hagan, P., Lesniewski, A., & Woodward, D. (2018). Effective Media Analysis for Stochastic Volatility Models. Wilmott, January(93). 46 - 55.

Hagan, P., Lesniewski, A., & Woodward, D. (2018). Managing Vol Surfaces. Wilmott, January(93). 24 - 43.

Hagan, P., Lesniewski, A., & Woodward, D. (2017). Implied Volatilities for mean reverting SABR Models. In Progress.

N/A, P., Lesniewski, A., Kumar, D., & Woodward, D. (2016). Universal Smiles. Wilmott, January(84). 40 - 55.

Lesniewski, A., & Richter, A. (2016). Managing Counterparty Credit Risk Via BSDEs.

Hagan, P., Lesniewski, A., Kumar, D., & Woodward, D. (2014). Arbitrage-Free SABR. Wilmott, January(69). 60 - 75.

Book Chapters

Hagan, P., Lesniewski, A., & Woodward, D. (2015). Probability Distribution in the SABR Model of Stochastic Volatility. In Friz, P. K., Gatheral, J., Gulisashvili, A., Jacquier, A., & Teichmann, J. (Eds.), Large Deviations and Asymptotic Methods in Finance (pp. 1-36). Germany. Springer.

Presentations

Lesniewski, A. (2014, March 24). Option Smile and the SABR Model of Stochastic Volatility. Traders @ MIT Seminar. Cambridge, MA: Massachussetts Institute of Technology.

Research Currently in Progess

Richter, A., & Lesniewski, A.(n.d.). Approximate stochastic maximum principle via a low noise expansion. In Progress.

We develop a method for approximate stochastic optimal control using a low noise approximation of the stochastic maximum principle.

Richter, A., & Lesniewski, A.(n.d.). FKK filtering for observations with state dependent diffusion coefficients. In Progress.

Stochastic filtering is an approach that aims to estimate the "true" state or signal of a system based on incomplete and noisy observations. One typically assumes a system of stochastic differential equations of which only parts can be observed. From the observable part of the system one obtains an estimate of the "true" state. The general equations of stochastic filtering have been established under the assumption that the diffusion coefficient of the observed process is independent of the state, hence excluding many financial models. Based on directed acyclic graphs, we believe we can find a stochastic filtering equation with a state dependent diffusion coefficient.

Richter, A., & Lesniewski, A.(n.d.). Portfolio optimization under incomplete information. In Progress.

We study multiple optimal investment problem under incomplete information which can be solved explicitly. We use Backward Differential Equations (BSDE) to characterize the value function and then solve the BSDEs in terms of a system of ordinary differential equations.

Richter, A., Lesniewski, A., & Lewis, H.(n.d.). Wrong Way Risk and the XVAs. In Progress.

In "Managing counterparty credit risk via backward stochastic differential equation (BSDEs)", Andrew Lesniewski and I developed a general approach to counterparty credit risk modeling. Particularly, the paper provides an efficient numerical method for solving a BSDE fundamental to the study of counterparty credit risk. Here, we extend this method to study wrong way risk which occurs whenever the exposure to a counterparty depends unfavorably on the default of that counterparty.