Monte Carlo Methods in Financial Engineering

By Paul Glasserman. 2003

3,847 citations recorded by the source corpus.

Subfields: Discrete Mathematics and Combinatorics, Finance, Probabilistic & Bayesian Methods

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Citations in the corpus

Gradient Estimation Using Stochastic Computation Graphs (2015, background): “See Glasserman [4] for a detailed discussion of the technical requirements for these gradient estimators to be valid.”
Control functionals for Monte Carlo integration (2014, methodology): “…and Casella, 2004; Douc and Robert, 2011; Ghosh and Clyde, 2011; Olsson and Ryden, 2011), (e) Riemann sums (Philippe, 1997), (f) control variates (Glasserman, 2004; Mira et al., 2013; Li et al., 2016), (g) multilevel MC and related techniques (e.g. Heinrich (1995), Giles (2013) and Giles…”
Learning in Implicit Generative Models (2016, methodology): “This leads to the ﬁrst of three tools available for non-differentiable models: weak derivative and related stochastic ﬁnite difference estimators, which require forward-simulation only and compute gradients by perturbation of the parameters (Fu, 2005; Glasserman, 2003).”
A transform approach to compute prices and Greeks of barrier options driven by a class of Lévy processes (2008, methodology): “Here we should remark that we did not employ any special methods that could have considerably improved the speed of convergence of the Monte Carlo simulation, such as Malliavin weights, likelihood ratio methods or sampling techniques such as the bridge (see, eg, Glasserman 2004 ).”
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning (2019, methodology): “The use of control variates is a classical technique to reduce variance in Monte Carlo sampling methods (cf. (Glasserman, 2013)).”
Joint calibration to SPX and VIX options with signature‐based models (2023, methodology): “We here discuss variance reduction techniques (see e.g. Glasserman (2004)) that can speed up the calibration in the subsequently applied Monte Carlo approach further.”
Minimum variance importance sampling via Population Monte Carlo (2007, methodology): “Details on the symmetrized Euler approximation can be found in Glasserman [13] as well as Bally and Bossy [3, 4] and Talay et al.”
Strong convergence rates for backward Euler on a class of nonlinear jump-diffusion problems (2007, methodology): “The Poisson increment over a time interval of length t was computed using a 25 method detailed in [4].”
Malliavin Greeks without Malliavin calculus (2007, background): “Several technical results are collected in Appendices A–C.”
Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading (2011, positive): “discretization (see e.g. Glasserman (2004, pp. 110) ).”
Variance reduction for Monte Carlo methods to evaluate option prices under multi-factor stochastic volatility models (2004, methodology): “Examples include pricing and hedging financial instruments with complex structure or high dimensionality [10].”
Relativistic Brownian Motion (2008, methodology): “on relations may take a particularly elegant form (cf. Section 6.2 in Ref. [77], and Ref. [112]). A.5 Numerical integration A detailed introduction to the numerical simulation of SDEs can be found in [73, 89, 420]. A simple Monte-Carlo algorithm for numerically integrating Eqs. (A.31) follows direct”
Monte Carlo Gradient Estimation in Machine Learning (2019, methodology): “Equation (31c) is the form in which Ho and Cao (1983) initially introduced the estimator, and it corresponds to the strategy of differentiating both sides of the transformation noted by Glasserman (2013).”
Handbook of Financial Engineering (2011, neutral): “θ(2) p(τ) = ∫ (τ − rp)(2)pdf(r1, r2)dr1dr2, (29)”