14 citations · 39 across the 16 of their papers we have counts for
18 papers
Fitting an Escalier to a Curve
Sebastien Bossu, Andrew Papanicolaou, Nour El Hatto
We analyze the problem of fitting a fonction en escalier or multi-step function to a curve in L^2 Hilbert space. We propose a two-stage optimization approach whereby the step posit…
State Constrained Stochastic Optimal Control for Continuous and Hybrid Dynamical Systems Using DFBSDE
Bolun Dai, Prashanth Krishnamurthy, Andrew Papanicolaou +1
We develop a computationally efficient learning-based forward-backward stochastic differential equations (FBSDE) controller for both continuous and hybrid dynamical (HD) systems su…
Partial-Information Q-Learning for General Two-Player Stochastic Games
Negash Medhin, Andrew Papanicolaou, Marwen Zrida
In this article we analyze a partial-information Nash Q-learning algorithm for a general 2-player stochastic game. Partial information refers to the setting where a player does not…
A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs
Andrew Papanicolaou, Hao Fu, Prashanth Krishnamurthy +1
We analyze a fixed-point algorithm for reinforcement learning (RL) of optimal portfolio mean-variance preferences in the setting of multivariate generalized autoregressive conditio…
An Optimal Control Strategy for Execution of Large Stock Orders Using LSTMs
A. Papanicolaou, H. Fu, P. Krishnamurthy +2
In this paper, we simulate the execution of a large stock order with real data and general power law in the Almgren and Chriss model. The example that we consider is the liquidatio…
State Constrained Stochastic Optimal Control Using LSTMs
Bolun Dai, Prashanth Krishnamurthy, Andrew Papanicolaou +1
In this paper, we propose a new methodology for state constrained stochastic optimal control (SOC) problems. The solution is based on past work in solving SOC problems using forwar…
Trading Signals In VIX Futures
M. Avellaneda, T. N. Li, A. Papanicolaou +1
We propose a new approach for trading VIX futures. We assume that the term structure of VIX futures follows a Markov model. Our trading strategy selects a position in VIX futures b…
PCA for Implied Volatility Surfaces
Marco Avellaneda, Brian Healy, Andrew Papanicolaou +1
Principal component analysis (PCA) is a useful tool when trying to construct factor models from historical asset returns. For the implied volatilities of U.S. equities there is a P…
Singular Perturbation Expansion for Utility Maximization with Order- Quadratic Transaction Costs
Andrew Papanicolaou, Shiva Chandra
We present an expansion for portfolio optimization in the presence of small, instantaneous, quadratic transaction costs. Specifically, the magnitude of transaction costs has a coef…
Statistical Arbitrage for Multiple Co-Integrated Stocks
T. N. Li, A. Papanicolaou
In this article, we analyse optimal statistical arbitrage strategies from stochastic control and optimisation problems for multiple co-integrated stocks with eigenportfolios being…
Consistent Time-Homogeneous Modeling of SPX and VIX Derivatives
Andrew Papanicolaou
This paper shows how to recover a stochastic volatility model (SVM) from a market model of the VIX futures term structure. Market models have more flexibility for fitting of curves…
Backward SDEs for Control with Partial Information
Andrew Papanicolaou
This paper considers a non-Markov control problem arising in a financial market where asset returns depend on hidden factors. The problem is non-Markov because nonlinear filtering…
Dimension Reduction in Statistical Estimation of Partially Observed Multiscale Processes
Andrew Papanicolaou, Konstantinos Spiliopoulos
We consider partially observed multiscale diffusion models that are specified up to an unknown vector parameter. We establish for a very general class of test functions that the fi…
Introduction to Stochastic Differential Equations (SDEs) for Finance
Andrew Papanicolaou
These are course notes on the application of SDEs to options pricing. The author was partially supported by NSF grant DMS-0739195.
Stochastic Analysis Seminar on Filtering Theory
Andrew Papanicolaou
These notes were originally written for the Stochastic Analysis Seminar in the Department of Operations Research and Financial Engineering at Princeton University, in February of 2…
Filtering the Maximum Likelihood for Multiscale Problems
Andrew Papanicolaou, Konstantinos Spiliopoulos
Filtering and parameter estimation under partial information for multiscale problems is studied in this paper. After proving mean square convergence of the nonlinear filter to a fi…
Implied Filtering Densities on Volatility's Hidden State
Carlos Fuertes, Andrew Papanicolaou
We formulate and analyze an inverse problem using derivatives prices to obtain an implied filtering density on volatility's hidden state. Stochastic volatility is the unobserved st…
Nonlinear Filters for Hidden Markov Models of Regime Change with Fast Mean-Reverting States
Andrew Papanicolaou
We consider filtering for a hidden Markov model that evolves with multiple time scales in the hidden states. In particular, we consider the case where one of the states is a scaled…