COLUMBIA UNIVERSITY
GRADUATE SCHOOL OF BUSINESS

Paul Glasserman

Brief bio and directory entry
Jack R. Anderson Professor
1114 Kravis Hall
665 West 130th Street
New York, NY 10027
(212) 854-4102
pg20 AT columbia DOT edu

Last updated October 2023


Book

   [Book cover: Monte Carlo Methods in Financial Engineering]       [Book cover: Monte Carlo Methods in Financial Engineering]

Downloadable Papers

Time Variation in the News-Returns Relationship,

P. Glasserman, F. Li, and H. Mamaysky, JFQA, to appear.

Should Bank Stress Tests Be Fair?

P. Glasserman and M. Li, Management Science, to appear

Linear Classifiers Under Infinite Imbalance
P. Glasserman and M. Li, working paper

Swing Pricing: Theory and Evidence,

A. Capponi, P. Glasserman, and M. Weber, Annual Review of Financial Economics, to appear.
W-Shaped Implied Volatility Curves and the Gaussian Mixture Model
P. Glasserman and D. Pirjol, Quantitative Finance 23(4), 557-577, 2023.

Total Positivity and Relative Convexity of Option Prices,

P. Glasserman and D. Pirjol, Frontiers of Mathematical Finance 2(1), 1-32, 2023.
New News is Bad News, working paper

P. Glasserman, H. Mamaysky, and J. Qin

Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis,

P. Glasserman and C. Lin, working paper

Dynamic Information Regimes in Financial Markets
P. Glasserman, H. Mamaysky, and Y. Shen, Management Science, to appear.

Combined Derivative Estimators,

P. Glasserman, Advances in Modeling and Simulation, Festschrift for Pierre L'Ecuyer, 2022.
Tail Risk Monotonicity Under Temporal Aggregation in GARCH(1,1) Models,
P. Glasserman, D. Pirjol, and Q. Wu, working paper
Investor Information Choice with Macro and Micro Information
P. Glasserman and H. Mamaysky, Review of Asset Pricing Studies, 13(1), 1-52, 2023. Internet Appendix.
Maximum Entropy Distributions with Applications to Graph Simulation

P. Glasserman and E. Lelo de Larrea, Operations Research 71(5), 1908-1924, 2023.

Choosing News Topics to Explain Stock Market Returns
P. Glasserman, K. Krstovski, P. Laliberte, and H. Mamaysky, Proceedings of the ACM International Conference on AI in Finance, 2020.
Collateralized Networks
S. Ghamami, P. Glasserman, and H.P. Young, Management Science 68(3), 2202-2225, 2022.
Buy Rough, Sell Smooth
P. Glasserman and P. He, Quantitative Finance, vol. 20(3), 363-378, 2020.
Swing Pricing for Mutual Funds: Breaking the Feedback Loop Between Fire Sales and Fund Runs,
A. Capponi, P. Glasserman, and M. Weber, Management Science, vol. 66(8), 3295-3798, 2020.
Does Unusual News Forecast Market Stress?
H. Mamaysky and P. Glasserman, JFQA, vol. 54(5), 1937-1974, 2019.
Bounding Wrong-Way Risk in CVA Calculation
P. Glasserman and L. Yang, Mathematical Finance, vol. 28, 268-305, 2018.
Contingent Capital, Tail Risk, and Debt-Induced Collapse
N. Chen, P. Glasserman, B. Nouri, and M. Pelger, Review of Financial Studies, vol. 30, 3711-3758, 2017.
Does OTC Derivatives Reform Incentivize Central Clearing?
S. Ghamami and P. Glasserman, OFR working paper, Journal of Financial Intermediation, vol. 32, 76-87, 2017.
Market-Triggered Changes in Capital Structure: Equilibrium Price Dynamics
P. Glasserman and B. Nouri, Econometrica, vol. 84, 2113-2153, 2016.
Submodular Risk Allocation
S. Ghamami and P. Glasserman, Management Science, vol. 65(1), 4656-4675, 2019.
Contagion in Financial Networks
P. Glasserman and H. P. Young, Journal of Economic Literature, vol. 54, 779-831, 2016.
The Market-Implied Probability of European Government Intervention in Distressed Banks
R. Neuberg, P. Glasserman, B. S. Kay and S. Rajan, OFR working paper.
Persistence and Procyclicality in Margin Requirements
P. Glasserman and Q. Wu, Management Science, vol. 64(12), 5461-5959, 2018.
Hidden Illiquidity with Multiple Central Counterparties
P. Glasserman, C. C. Moallemi, and K. Yuan, OFR working paper, Operations Research, vol. 64, 1143-1158, 2016.
How Likely is Contagion in Financial Networks?

P. Glasserman and H. P. Young, Journal of Banking and Finance, vol. 50, 383-399, 2015.
Stress Scenario Selection by Empirical Likelihood
P. Glasserman, C. Kang, and W. Kang, Quantitative Finance, vol. 15, 25-41, 2015.
Design of Risk Weights
P. Glasserman and W. Kang, Operations Research, vol. 62, 2014.
Robust Risk Measurement and Model Risk
P. Glasserman and X. Xu, Quantitative Finance, vol. 14, 29-58, 2014.
Robust Portfolio Control with Stochastic Factor Dynamics
P. Glasserman and X. Xu, Operations Research, 1-20, 2013.
Contingent Capital with a Capital-Ratio Trigger
P. Glasserman and B. Nouri, Management Science 2012 (with typos corrected).
Quadratic Transform Approximation for CDO Pricing in Multifactor Models
P. Glasserman and S. Suchintabandid, SIAM Journal on Financial Mathematics, vol. 3, 137-162, 2012.
Forward and Future Implied Volatility
P. Glasserman and Q. Wu, IJTAF vol. 14, 407-432, 2011.
Valuing the Treasury's Capital Assistance Program
P. Glasserman and Z. Wang, Management Science vol. 57, 1195-1211, 2011.
Risk Horizon and Rebalancing Horizon in Portfolio Risk Measurement
P. Glasserman, Mathematical Finance vol 22, 215-249, 2012.
Gamma Expansion of the Heston Stochastic Volatility Model
P. Glasserman and K. Kim, Finance and Stochastics 1-30, 2009.
Sensitivity Estimates for Portfolio Credit Derivatives Using Monte Carlo
Z. Chen and P. Glasserman, Finance and Stochastics vol 12, 507-540, 2008.
Moment Explosions and Stationary Distributions in Affine Diffusion Models
P. Glasserman and K. Kim, Mathematical Finance vol 20, 1-33, 2010.
Saddlepoint Approximations for Affine Jump-Diffusion Models
P. Glasserman and K. Kim, Journal of Economic Dynamics and Control, vol 33, 37-52, 2009.
Beta Approximations for Bridge Sampling
P. Glasserman and K. Kim, Proceedings of the Winter Simulation Conference, 569-577, 2008.
Sensitivity Estimates from Characteristic Functions
P. Glasserman and Z. Liu, Operations Research, vol. 58, 1611-1623, 2010.
Estimating Greeks in Simulating Levy-Driven Models
P. Glasserman and Z. Liu, Journal of Computational Finance, vol. 14, 3-56, 2010/2011.
Malliavin Greeks without Malliavin Calculus
N. Chen and P. Glasserman, Stochastic Processes and Their Applications, vol. 117, 1689-1723, 2007.
Correlation Expansions for CDO Pricing
P. Glasserman and S. Suchintabandid, Journal of Banking and Finance, vol. 31, 1375-1398, 2007.
Fast Pricing of Basket Default Swaps
Z. Chen and P. Glasserman, Operations Research, vol. 56, 286-303, 2008.
Uniformly Efficient Importance Sampling for the Tail Distribution of Sums of Random Variables
P. Glasserman and S. Juneja, Mathematics of Operations Research, vol. 33, 36-50, 2008.
Fast Simulation of Multifactor Portfolio Credit Risk
P. Glasserman, W. Kang, and P. Shahabuddin, Operations Research, vol. 56, 1200-1217, 2008.
Additive and Multiplicative Duals for American Option Pricing
N. Chen and P. Glasserman, Finance and Stochastics, 11, 153-179, 2007.
Large Deviations of Multifactor Portfolio Credit Risk
P. Glasserman, W. Kang, and P. Shahabuddin, Mathematical Finance, vol. 17, 345-379, 2007.
Perwez Shahabuddin, 1962-2005: A Professional Appreciation
S. Androdottir, P. Glasserman, P.W. Glynn, P. Heidelberger and S. Juneja, ACM TOMACS, vol. 17, 2007.
A Conversation with Chris Heyde
P. Glasserman and S. G. Kou, Statistical Science, vol. 21, 286-298, 2006.
Smoking Adjoints: Fast Monte Carlo Greeks
M. Giles and P. Glasserman, Risk, vol. 19, 88-92, 2006.
Importance Sampling for Portfolio Credit Risk
P. Glasserman and Jingyi Li, Management Science, vol 51, 1643-1656, 2005.
Measuring Marginal Risk Contributions in Credit Portfolios
P. Glasserman, Journal of Computational Finance, vol. 9, 1-41, 2005.
Tail Approximations for Portfolio Credit Risk
P. Glasserman, Journal of Derivatives, 24-42,Winter 2004.
Number of Paths Versus Number of Basis Functions in American Option Pricing

P. Glasserman and Bin Yu, Annals of Applied Probability, vol. 14, no. 4, 2090-2119, 2004.
Pricing American Options by Simulation:  Regression Now or Regression Later?

P.Glasserman and Bin Yu, Monte Carlo and Quasi-Monte Carlo Methods 2002,
(H. Niederreiter, ed.), Springer, Berlin.
Importance Sampling for a Mixed Poisson Model of Portfolio Credit Risk
P. Glasserman and Jingyi Li, Proceedings of the Winter Simulation Conference 2003
Large Sample Properties of Weighted Monte Carlo Estimators

P. Glasserman and Bin Yu, Operations Research, vol. 53, 298-312, 2005.
Cap and Swaption Approximations in LIBOR Market Models with Jumps
P. Glasserman and N. Merener, Journal of Computational Finance, vol 7, 1-36, 2003.
The Term Structure of Simple Forward Rates with Jump Risk
P. Glasserman and S.G. Kou, Mathematical Finance, July 2003,383-410.
Numerical Solution of Jump-Diffusion LIBOR Market Models
P. Glasserman and N. Merener, Finance and Stochastics 7, 1-27, 2003.
Addendum  
Convergence of a Discretization Scheme for Jump-Diffusion Processes
with State-Dependent Intensities
  
P. Glasserman and N. Merener, Proceedings of the Royal Society of London, Series A, vol. 460, 1--17, 2003.
Portfolio Value-at-Risk with Heavy-Tailed Risk Factors
P. Glasserman, P. Heidelberger, and P. Shahabuddin, Mathematical Finance, vol. 12, 239-270, 2002.
Variance Reduction Techniques for Estimating Value-at-Risk
P. Glasserman, P. Heidelberger, and P. Shahabuddin, Management Science, vol. 46, 1349-1364, 2000.
Efficient Monte Carlo Methods for Value-at-Risk
P. Glasserman, P. Heidelberger, and P. Shahabuddin, in Mastering Risk: Vol 2, Financial Times-Prentice Hall, 2001.
Importance Sampling and Stratification for Value-at-Risk
P. Glasserman, P. Heidelberger, and P. Shahabuddin, in Computational Finance 1999, Abu-Mostafa, Le Baron, Lo, and Weigend, eds., MIT Press, 2000.
Stratification Issues in Estimating Value-at-Risk
P. Glasserman, P. Heidelberger, and P. Shahabuddin, Proceedings of the Winter Simulation Conference, 351-359, 1999.
Equilibrium Positive Interest Rates:  A Unified View
Y. Jin and P. Glasserman, Review of Financial Studies, 14:187-214 (2001).
Importance Sampling in the Heath-Jarrow-Morton Framework
P. Glasserman, P. Heidelberger, and P. Shahabuddin, Journal of Derivatives, 7(1):32-50, 1999.
Asymptotically Optimal Importance Sampling and Stratification for Pricing Path-Dependent Options,
P. Glasserman, P. Heidelberger, and P. Shahabuddin, Mathematical Finance, 9:117-152, 1999.
Arbitrage-Free Discretization of Lognormal Forward Libor and Swap Rate Models,
P. Glasserman and X. Zhao, Finance and Stochastics 4:35-68 2000.
Discretization of Deflated Bond Prices
P. Glasserman and H. Wang, Advances in Applied Probability, 32:540-563, 2001.
Fast Greeks by Simulation in Forward Libor Models
P. Glasserman and X. Zhao, Journal of Computational Finance, 3:5-39, 1999.
Source code for numerical examples
Comparing Stochastic Discount Factors Through Their Implied Measures
P. Glasserman and Y. Jin
Conditioning on One-Step Survival in Barrier Option Simulations
P. Glasserman and J. Staum, Operations Research, 49:923-937, 2001.
Resource Allocation Among Simulation Time Steps
P. Glasserman and J. Staum, Operations Research, vol. 51, 908-921, 2003.
Stopping Simulated Paths Early
P. Glasserman and J. Staum, Proceedings of the Winter Simulation Conference, 318-325, 2001.
A Stochastic Mesh Method for Pricing High-Dimensional American Options
M. Broadie and P. Glasserman, Journal of Computational Finance, vol. 7, 35-72, 2004.
Pricing American Options by Simulation Using a Stochastic Mesh with Optimized Weights
M. Broadie, P. Glasserman, and Z. Ha, in Probabilistic Constrained Optimization, S.P. Uryasev, ed., 32-50, 2000.
A Continuity Correction for Discrete Barrier Options
M. Broadie, P. Glasserman, S.G. Kou, Mathematical Finance 7:325-348, 1997.
Connecting Discrete and Continuous Path-Dependent Options
M. Broadie, P. Glasserman, S.G. Kou, Finance and Stochastics 3:55-82, 1999.