AB
Anastasia Borovykh
Authored
3 records found
A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning fr
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We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions that allow it to access a broad range of historical data when forecasting. It als
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Extracting implied information, like volatility and dividend, from observed option prices is a challenging task when dealing with American options, because of the complex-shaped early-exercise regions and the computational costs to solve the corresponding mathematical problem rep
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Contributed
4 records found
In this thesis, we study the sequential Monte Carlo method for training neural networks in the context of time series forecasting. Sequential Monte Carlo can be particularly useful in problems in which the data is sequential, noisy and non-stationary. We compare this algorithm ag
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Clients with a mortgage loan may prepay a part of their loan before the contractual date. This is called prepayment. In the case of a prepayment, the bank who issued the loan earns less interest than ini- tially agreed. It is therefore essential to build accurate models for predi
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Since the liberalization of the energy markets, the storage of energy is decoupled from the production and sales. In Western-Europe the storage of natural gas becomes more and more important because production fields get depleted and governments force companies to slow down their
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Recent works have shown that neural networks can be employed to solve partial differential equations, bringing rise to the framework of physics informed neural networks.The aim of this project is to gain a deeper understanding of these novel methods, and to use these insights to
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