PaperUsing neural networks to forecast the S&P 100 implied volatility
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Online adaptive machine learning based algorithm for implied volatility surface modeling
2019, Knowledge-Based SystemsCitation Excerpt :A few examples are as follows. Malliaris and Salchenberger [18] apply artificial neural nets to forecast S&P100 implied volatility with past volatilities and other options market factors. Fengler et al. [19] model IVS dynamics using a semiparametric factor model by means of a principal component analysis, with empirical experiments using the DAX index options data.
Machine learning versus econometric jump models in predictability and domain adaptability of index options
2019, Physica A: Statistical Mechanics and its ApplicationsCitation Excerpt :Most machine learning methods forecasted the prices of financial derivatives with the expectation that the process of underlying assets will be represented implicitly as a learning function of input variables without the explicit form of return processes. Successful machine learning models for predicting financial derivatives include artificial neural networks (NNs) [1–7], support vector machines [8,9], and Gaussian processes (GPs) [10–13]. These models have also considered different types of available market information, but did not consider explicit formulation for underlying processes.
Physics-informed convolutional transformer for predicting volatility surface
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