What is he best known for?
The fields of study he is best known for:
- Statistics
- Finance
- Normal distribution
His main research concerns Mathematical optimization, Monte Carlo method, Econometrics, Estimator and Importance sampling.
His work deals with themes such as Stochastic volatility, Event, Mathematical economics, Stochastic game and Markov chain, which intersect with Mathematical optimization.
His study in the fields of Variance reduction under the domain of Monte Carlo method overlaps with other disciplines such as Geometric Brownian motion.
His study on Monte Carlo methods for option pricing is often connected to Node as part of broader study in Econometrics.
The study incorporates disciplines such as Quasi-Monte Carlo method, Antithetic variates, Binomial options pricing model and Asian option in addition to Monte Carlo methods for option pricing.
His Importance sampling research is multidisciplinary, incorporating elements of Large deviations theory, Portfolio, Asymptotically optimal algorithm, Risk management and Default.
His most cited work include:
- Monte Carlo Methods in Financial Engineering (2426 citations)
- Monte Carlo methods for security pricing (727 citations)
- Gradient Estimation Via Perturbation Analysis (540 citations)
What are the main themes of his work throughout his whole career to date?
Paul Glasserman spends much of his time researching Econometrics, Mathematical optimization, Applied mathematics, Estimator and Monte Carlo method.
His work is dedicated to discovering how Econometrics, Portfolio are connected with Market risk and other disciplines.
His work carried out in the field of Mathematical optimization brings together such families of science as Production, Event, Sensitivity, Path and Variance reduction.
His study looks at the relationship between Variance reduction and fields such as Importance sampling, as well as how they intersect with chemical problems.
His research in Applied mathematics intersects with topics in Markov process, Mathematical analysis, Poisson distribution, Queueing theory and Markov chain.
Paul Glasserman combines subjects such as Limit and Conditional expectation with his study of Estimator.
He most often published in these fields:
- Econometrics (27.84%)
- Mathematical optimization (23.53%)
- Applied mathematics (16.86%)
What were the highlights of his more recent work (between 2012-2020)?
- Econometrics (27.84%)
- Monetary economics (6.27%)
- Financial networks (4.31%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Econometrics, Monetary economics, Financial networks, Portfolio and Volatility.
His Econometrics research incorporates elements of Estimator, Actuarial science, Credit risk and Joint probability distribution.
His Monetary economics study integrates concerns from other disciplines, such as Debt overhang and Asset.
His Financial networks research focuses on Leverage and how it relates to Default.
In his study, Model risk is inextricably linked to Market risk, which falls within the broad field of Portfolio.
His research in Volatility focuses on subjects like Stock market volatility, which are connected to Valuation of options and Volatility smile.
Between 2012 and 2020, his most popular works were:
- How likely is contagion in financial networks (250 citations)
- Contagion in Financial Networks (126 citations)
- Robust risk measurement and model risk (113 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Finance
- Normal distribution
His scientific interests lie mostly in Econometrics, Monetary economics, Portfolio, Actuarial science and Stress testing.
His work on Volatility as part of general Econometrics study is frequently linked to Node, therefore connecting diverse disciplines of science.
His Monetary economics study combines topics in areas such as Debt overhang, Financial economics, Financial networks and Stock market volatility.
His study focuses on the intersection of Portfolio and fields such as Errors-in-variables models with connections in the field of Model risk, Kullback–Leibler divergence, Mathematical optimization and Monte Carlo method.
His research investigates the link between Actuarial science and topics such as Portfolio optimization that cross with problems in Profitability index.
His Joint probability distribution study combines topics from a wide range of disciplines, such as Nonparametric statistics, Estimator, Empirical likelihood and Conditional expectation.
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