Andrew Papanicolaou Papanicolaou, A., Fu, H., Krishnamurthy, P., & Khorrami, F. (2023). A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs. IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2023.3245570 Papanicolaou, A., Fu, H., Krishnamurthy, P., Healy, B., & Khorrami, F. (2023). An optimal control strategy for execution of large stock orders using long short-term memory networks. JOURNAL OF COMPUTATIONAL FINANCE, 26(4), 37–65. https://doi.org/10.21314/JCF.2023.003 Dai, B., Krishnamurthy, P., Papanicolaou, A., & Khorrami, F. (2023). State constrained stochastic optimal control for continuous and hybrid dynamical systems using DFBSDE. AUTOMATICA, 155. https://doi.org/10.1016/j.automatica.2023.111146 Papanicolaou, A. (2022). Consistent time‐homogeneous modeling of SPX and VIX derivatives. Mathematical Finance, 32(3), 907–940. https://doi.org/10.1111/mafi.12348 Avellaneda, M., Healy, B., Papanicolaou, A., & Papanicolaou, G. (2022). Principal Eigenportfolios for U.S. Equities. SIAM Journal on Financial Mathematics, 13(3), 702–744. https://doi.org/10.1137/20M1383501 Bossu, S., Carr, P., & Papanicolaou, A. (2022). Static replication of European standard dispersion options. Quantitative Finance, 22(5), 799–811. https://doi.org/10.1080/14697688.2022.2040743 Li, T. N., & Papanicolaou, A. (2022). Statistical Arbitrage for Multiple Co-integrated Stocks. Applied Mathematics & Optimization, 86(1). https://doi.org/10.1007/s00245-022-09838-3 Amir-Ghassemi, F., Papanicolaou, A., & Perlow, M. (2021). Aggregate Alpha in the Hedge Fund Industry: A Further Look at Best Ideas. The Journal of Portfolio Management, 48(3), 220–239. https://doi.org/10.3905/jpm.2021.1.313 Dai, B., Krishnamurthy, P., Papanicolaou, A., & Khorrami, F. (2021). State Constrained Stochastic Optimal Control Using LSTMs. Presented at the 2021 American Control Conference (ACC). https://doi.org/10.23919/acc50511.2021.9482832 Avellaneda, M., Li, T. N., Papanicolaou, A., & Wang, G. (2021). Trading Signals in VIX Futures. Applied Mathematical Finance, 28(3), 275–298. https://doi.org/10.1080/1350486x.2021.2010584 Bossu, S., Carr, P., & Papanicolaou, A. (2020). A functional analysis approach to the static replication of European options. Quantitative Finance, 21(4), 637–655. https://doi.org/10.1080/14697688.2020.1810857 Avellaneda, M., Healy, B., Papanicolaou, A., & Papanicolaou, G. (2020). PCA for Implied Volatility Surfaces. The Journal of Financial Data Science, 2(2), 85–109. https://doi.org/10.3905/jfds.2020.1.032 Amaral, L. R., & Papanicolaou, A. (2019). PRICE IMPACT OF LARGE ORDERS USING HAWKES PROCESSES. The ANZIAM Journal, 61(02), 161–194. https://doi.org/10.1017/S1446181119000038 Avellaneda, M., & Papanicolaou, A. (2019). STATISTICS OF VIX FUTURES AND APPLICATIONS TO TRADING VOLATILITY EXCHANGE-TRADED PRODUCTS. International Journal of Theoretical and Applied Finance, 22(01), 1850061. https://doi.org/10.1142/s0219024918500619 Chandra, S., & Papanicolaou, A. (2019). Singular Perturbation Expansion for Utility Maximization with Order-ε Quadratic Transaction Costs. International Journal of Theoretical and Applied Finance, 22(7), 1950039. https://doi.org/10.1142/S0219024919500390 Papanicolaou, A. (2018). Backward SDEs for control with partial information. Mathematical Finance, 29(1), 208–248. https://doi.org/10.1111/mafi.12174 Papanicolaou, A. (2018). Extreme-Strike Comparisons and Structural Bounds for SPX and VIX Options. SIAM Journal on Financial Mathematics, 9(2), 401–434. https://doi.org/10.1137/141001615 Papanicolaou, A., & Spiliopoulos, K. (2017). Dimension Reduction in Statistical Estimation of Partially Observed Multiscale Processes. SIAM/ASA Journal on Uncertainty Quantification, 5(1), 1220–1247. https://doi.org/10.1137/16m1085930 Fouque, J.-P., Papanicolaou, A., & Sircar, R. (2017). Perturbation Analysis for Investment Portfolios Under Partial Information with Expert Opinions. SIAM Journal on Control and Optimization, 55(3), 1534–1566. https://doi.org/10.1137/15m1006854 Papanicolaou, A. (2016). Analysis of VIX Markets with a Time-Spread Portfolio. Applied Mathematical Finance, 23(5), 374–408. https://doi.org/10.1080/1350486x.2017.1290534 Lee, S., & Papanicolaou, A. (2016). PAIRS TRADING OF TWO ASSETS WITH UNCERTAINTY IN CO-INTEGRATION'S LEVEL OF MEAN REVERSION. International Journal of Theoretical and Applied Finance, 19(08), 1650054. https://doi.org/10.1142/s0219024916500540 Fouque, J.-P., Papanicolaou, A., & Sircar, R. (2015). Filtering and portfolio optimization with stochastic unobserved drift in asset returns. Communications in Mathematical Sciences, 13(4), 935–953. https://doi.org/10.4310/cms.2015.v13.n4.a5 Papanicolaou, A., & Spiliopoulos, K. (2014). Filtering the Maximum Likelihood for Multiscale Problems. Multiscale Modeling & Simulation, 12(3), 1193–1229. https://doi.org/10.1137/140952648 Fuertes, C., & Papanicolaou, A. (2014). Implied Filtering Densities on the Hidden State of Stochastic Volatility. Applied Mathematical Finance, 21(6), 483–522. https://doi.org/10.1080/1350486x.2014.891357 Papanicolaou, A., & Sircar, R. (2013). A regime-switching Heston model for VIX and S&P 500 implied volatilities. Quantitative Finance, 14(10), 1811–1827. https://doi.org/10.1080/14697688.2013.814923 Papanicolaou, A. (2013). Dimension Reduction in Discrete Time Portfolio Optimization with Partial Information. SIAM Journal on Financial Mathematics, 4(1), 916–960. https://doi.org/10.1137/120897596 Papanicolaou, A. (2012). Nonlinear Filters for Hidden Markov Models of Regime Change with Fast Mean-Reverting States. Multiscale Modeling & Simulation, 10(3), 906–935. https://doi.org/10.1137/110819937 Papanicolaou, A. (2010). Filtering for fast mean-reverting processes. Asymptotic Analysis, 70(3-4), 155–176. https://doi.org/10.3233/ASY-2010-1011