Regression methods for pricing complex American-style options

By John N. Tsitsiklis, Benjamin Van Roy. IEEE Trans. Neural Networks, 2001

676 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/1175ba63ff85c3adbfc042c92e4dd27fbe0c5141

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Citations in the corpus

MONTE CARLO METHODS FOR THE VALUATION OF MULTIPLE‐EXERCISE OPTIONS (2004, neutral): “KEY WORDS: Monte Carlo, clvality, martingale, swing options, multiple-exercise options”
On the Robustness of Least-Squares Monte Carlo (LSM) for Pricing American Derivatives (2007, background): “Tsitsiklis and Van Roy (2001) analyze finite-horizon pricing problems.”
Valuing Real Capital Investments Using The Least-Squares Monte Carlo Method (2006, methodology): “Tsitsiklis and Van Roy (2001) proposed a similar regression-based method for valuing American-style options using simulation.”
Deep Reinforcement Learning: An Overview (2017, background): “RL is a natural solution to some ﬁnance and economics problems (Hull, 2014; Luenberger, 1997), like option pricing (Longstaff and Schwartz, 2001; Tsitsiklis and Van Roy, 2001; Li et al., 2009), and multi-period portfolio optimization (Brandt et al., 2005), where value function based RL meth-ods…”
Error Criteria for Numerical Solutions of Backward SDEs (2010, methodology): “Due to the structure of the American option pricing problem as a linear BSDE with reflection, its connection to optimal stopping problems allows to numerically calculate solutions which have a bias low by the Longstaff-Schwartz algorithm [24] and related algorithms [11, 12, 32].”
Pricing Asset Scheduling Flexibility using Optimal Switching (2008, background): “For example, a (a) finite-difference algorithm Wilmott et al. (1995) can be implemented.”
Scaling Up Robust MDPs by Reinforcement Learning (2013, background): “Previous studies [19, 12] have proposed RL solutions for this task, and shown their utility.”
TRUE UPPER BOUNDS FOR BERMUDAN PRODUCTS VIA NON‐NESTED MONTE CARLO (2009, background): “Due to the Lipschitz continuity of u(Tj+1, ·) results on L2-regularity obtained for the control part of FBSDEs in more general situations by Zhang (2004) and Bender and Zhang (2006) can be applied.”
A New Learning Algorithm for Optimal Stopping (2009, background): “i0 p(i0ji;u) k0(i0)) k(i;u)); 1 j n; ( 31 ) wherefbtg are stepsizes which we shall qualify later.”
Simulation for American Options: Regression Now or Regression Later? (2004, methodology): “This, then, raises a question about the connection between the weighted method of BGH [5] and regression-based methods for pricing American options proposed in Carrière [6], Longstaff and Schwartz [11], and Tsitsiklis and Van Roy [13].”