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Title: Machine learning applications to valuation of options on non-liquid markets Authors:  Jiri Witzany - University of Economics in Prague (Czech Republic) [presenting]
Milan Ficura - University of Economics in Prague (Czech Republic)
Abstract: Recently, there has been considerable interest in machine learning (ML) applications for the valuation of options. However, it is usually assumed that there is a relatively liquid market with plain vanilla option quotations that can be used to calibrate (using an ML approach such as a neural network - NN) the volatility surface, or to estimate parameters of an advanced stochastic model. In the second stage, the calibrated volatility surface (or the model parameters) are used to value given exotic options, again using a trained NN (or another ML model). The two NNs are typically trained offline by sampling many model and market parameter combinations and calculating the options market values. We focus on the quite common situation of a non-liquid option market where we lack sufficiently many plain vanilla option quotations to calibrate the volatility surface, but we still need to value an exotic option or a just plain vanilla option subject to a more advanced stochastic model as it is typical on energy markets. We show that it is possible to use selected moments of the underlying historical price return series complemented with a volatility risk premium estimate to value such options using the ML approach.