Predicting Exchange Rate Value-at-Risk and Expected Shortfall: A Neural Network Approach
(2019) NEKN02 20191Department of Economics
- Abstract
- On the basis of the recommendation of the Basel Committee on Banking Supervision to transition from Value-at-Risk (VaR) to Expected Shortfall (ES) in determining market risk capital, this paper attempts to investigate whether a Recurrent Neural Network provides more accurate VaR and ES predictions of the EUR/USD exchange rate compared to the conventional GARCH(1,1) model. A number of previous studies has confirmed the forecasting ability of a plain vanilla Feedforward Neural Network over traditional statistical models. However, standard neural networks have limitations. Most notably, they rely on the assumption of independency among data observations, which presents a problem when data points are related in time. To circumvent this... (More)
- On the basis of the recommendation of the Basel Committee on Banking Supervision to transition from Value-at-Risk (VaR) to Expected Shortfall (ES) in determining market risk capital, this paper attempts to investigate whether a Recurrent Neural Network provides more accurate VaR and ES predictions of the EUR/USD exchange rate compared to the conventional GARCH(1,1) model. A number of previous studies has confirmed the forecasting ability of a plain vanilla Feedforward Neural Network over traditional statistical models. However, standard neural networks have limitations. Most notably, they rely on the assumption of independency among data observations, which presents a problem when data points are related in time. To circumvent this restriction, this study employs a Gated Recurrent Unit type of neural network to produce one-step-ahead volatility forecasts of the EUR/USD exchange rate, which are then used to compute VaR and ES predictions. The VaR and ES forecasts for both models are obtained through a Volatility Weighted Historical Simulation, and evaluated with backtesting procedures. The empirical results indicate that the GARCH(1,1) model outperforms the Gated Recurrent Unit neural network for VaR95%, while the Gated Recurrent Unit neural network appears more adequate in forecasting ES at a 95% confidence level. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8989138
- author
- Bijelic, Anna LU and Ouijjane, Tilila
- supervisor
- organization
- course
- NEKN02 20191
- year
- 2019
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Value-at-Risk, Expected Shortfall, Recurrent Neural Networks, GRU, GARCH(1, 1), Exchange Rate Volatility, Intra-day Data
- language
- English
- id
- 8989138
- date added to LUP
- 2019-08-08 10:30:25
- date last changed
- 2019-08-08 10:30:25
@misc{8989138, abstract = {{On the basis of the recommendation of the Basel Committee on Banking Supervision to transition from Value-at-Risk (VaR) to Expected Shortfall (ES) in determining market risk capital, this paper attempts to investigate whether a Recurrent Neural Network provides more accurate VaR and ES predictions of the EUR/USD exchange rate compared to the conventional GARCH(1,1) model. A number of previous studies has confirmed the forecasting ability of a plain vanilla Feedforward Neural Network over traditional statistical models. However, standard neural networks have limitations. Most notably, they rely on the assumption of independency among data observations, which presents a problem when data points are related in time. To circumvent this restriction, this study employs a Gated Recurrent Unit type of neural network to produce one-step-ahead volatility forecasts of the EUR/USD exchange rate, which are then used to compute VaR and ES predictions. The VaR and ES forecasts for both models are obtained through a Volatility Weighted Historical Simulation, and evaluated with backtesting procedures. The empirical results indicate that the GARCH(1,1) model outperforms the Gated Recurrent Unit neural network for VaR95%, while the Gated Recurrent Unit neural network appears more adequate in forecasting ES at a 95% confidence level.}}, author = {{Bijelic, Anna and Ouijjane, Tilila}}, language = {{eng}}, note = {{Student Paper}}, title = {{Predicting Exchange Rate Value-at-Risk and Expected Shortfall: A Neural Network Approach}}, year = {{2019}}, }