Yield Curve Quantization and Simulation with Neural Networks

9 Pages Posted: 18 May 2020

Date Written: April 20, 2020


We present a method for simulating yield curve dynamics by learning the curve distribution from historical data using Artificial Neural Networks (ANN) in a two step procedure. The first step involves an autoencoder which performs a quantization of curve moves, generating a set of representative curve shapes. The second step learns a probability distribution over the quantized shapes, conditional on the current curve and the shift of a single pivot tenor point. This allows to simulate the curve by first drawing the the pivot tenor shift and then the shape of the curve move from its dynamic distribution. A suitable choice of regularizers allows to keep the simulation statistics close to the original data.

Keywords: Neural Networks, Yield Curve Simulation

JEL Classification: C61, C63, G13

Suggested Citation

Benedetti, Giuseppe, Yield Curve Quantization and Simulation with Neural Networks (April 20, 2020). Available at SSRN: https://ssrn.com/abstract=3577555 or http://dx.doi.org/10.2139/ssrn.3577555

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