The LSTM approach for Value at Risk prediction
| Author: | Bc. Nikanor Goreglyad |
|---|---|
| Year: | 2021 - summer |
| Leaders: | Marek Hauzr |
| Consultants: | |
| Work type: | Bachelors |
| Language: | English |
| Pages: | 78 |
| Awards and prizes: | |
| Link: | https://dspace.cuni.cz/handle/20.500.11956/147936 |
| Abstract: | This thesis describes a new Value at Risk forecasting method based on a neural network with Long Short-term Memory architecture trained with Joint Supervision loss function (JS LSTM). By optimizing the number of data points on both sides of the predicted value, JS LSTM produces VaR prediction for a given confidence level. The JS LSTM is trained to predict one-day-ahead VaR for PX, WIG20, BUX, and SAX market indexes. The result was compared with FIGARCH model, EVT-POT model, and LSTM model trained with realized VaR. The performance evaluation shows that the proposed model has marginally better performance than benchmark models in periods of normal volatility but underperform in periods of increased volatility. |
