Increment Variance Reduction Techniques with an Application to Multi-name Credit Derivatives

Pierre Rostan (), Alexandra Rostan () and François-Éric Racicot ()
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Pierre Rostan: American University of Paris
Alexandra Rostan: American University of Paris

Computational Economics, 2020, vol. 55, issue 1, No 1, 35 pages

Abstract: Abstract Increment variance reduction techniques are add-ons to Monte Carlo (MC) simulations. They make MC simulations converging faster by repeating the number of simulations with an incremental rate derived from mathematical functions. Besides speeding up MC simulations, the major advantage of increment techniques is their ability to handle large numbers of simulations avoiding memory saturation and overflow which occur when a plain MC simulation is involved in the pricing of multi-name credit derivatives. A trend among authors pricing financial securities with MC simulation has been to choose Quasi-Monte Carlo (QMC) methods using deterministic sequences instead of MC methods involving pseudorandom generators such as congruential generator and Mersenne twister. The Increment family models circumvent the constraint of identifying the optimal QMC sequence to price a given security by using a common generator such as Matlab-LCG-Xor RNG and determining the optimal mathematical function of incrementation of MC simulations that will make the pricing of the security adequate. Market participants in need of selecting a reliable numerical method for pricing complex financial securities such as multi-name credit derivatives will find our paper appealing.

Keywords: Monte Carlo simulation; Multi-name credit derivatives; Credit derivatives pricing; Synthetic CDOs; Variance reduction technique (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10614-018-9828-3

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