Výsledky vyhledávání - "Petneházi, Gábor"

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Volatility Forecasting with 1-dimensional CNNs via transfer learning

Autor: Aradi, Bernadett, Petneházi, Gábor, Gáll, József

Volatility is a natural risk measure in finance as it quantifies the variation of stock prices. A frequently considered problem in mathematical finance is to forecast different estimates of volatility. What makes it promising to use deep learning met

Externí odkaz: http://arxiv.org/abs/2009.05508

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Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?

Autor: Petneházi, Gábor, Gáll, József

This article applies a long short-term memory recurrent neural network to mortality rate forecasting. The model can be trained jointly on the mortality rate history of different countries, ages, and sexes. The RNN-based method seems to outperform the

Externí odkaz: http://arxiv.org/abs/1909.05501

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Quantile Convolutional Neural Networks for Value at Risk Forecasting

Autor: Petneházi, Gábor

This article presents a new method for forecasting Value at Risk. Convolutional neural networks can do time series forecasting, since they can learn local patterns in time. A simple modification enables them to forecast not the mean, but arbitrary qu

Externí odkaz: http://arxiv.org/abs/1908.07978

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Akademický článek

Immortal Mortality: Erasmus, Terminus and the Two Missing Skulls.

Autor: Petneházi, Gábor¹

Publikováno v: Terminus. 2024, Vol. 26 Issue 2, p125-140. 16p.

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Recurrent Neural Networks for Time Series Forecasting

Autor: Petneházi, Gábor

Time series forecasting is difficult. It is difficult even for recurrent neural networks with their inherent ability to learn sequentiality. This article presents a recurrent neural network based time series forecasting framework covering feature eng

Externí odkaz: http://arxiv.org/abs/1901.00069

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Exploring the predictability of range-based volatility estimators using RNNs

Autor: Petneházi, Gábor, Gáll, József

We investigate the predictability of several range-based stock volatility estimators, and compare them to the standard close-to-close estimator which is most commonly acknowledged as the volatility. The patterns of volatility changes are analyzed usi

Externí odkaz: http://arxiv.org/abs/1803.07152

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