Volatility Forecasting in Financial Risk Management with Statistical Models and ARCH-RBF Neural Networks

DOI

10.2991/jrarc.2014.4.2.4How to use a DOI?

Keywords

volatility, forecasting, ARCH-RBF, EUR/GBP, currency, risk in management

Abstract

As volatility plays very important role in financial risk management, we investigate the volatility dynamics of EUR/GBP currency. While a number of studies examines volatility using statistical models, we also use neural network approach. We suggest the ARCH-RBF model that combines information from ARCH with RBF neural network for volatility forecasting. We also use a large number of statistical models as well as different optimization techniques for RBF network such as genetic algorithms or clustering. Both insample and out-of-sample forecasts are evaluated using appropriate evaluation measures. In the final comparison none of the considered models performed significantly better than the rest with respect to the considered criteria. Finally, we propose upgrades of our suggested model for the future.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Dusan Marcek
AU  - Lukas Falat
PY  - 2014
DA  - 2014/06/19
TI  - Volatility Forecasting in Financial Risk Management with Statistical Models and ARCH-RBF Neural Networks
JO  - Journal of Risk Analysis and Crisis Response
SP  - 77
EP  - 95
VL  - 4
IS  - 2
SN  - 2210-8505
UR  - https://doi.org/10.2991/jrarc.2014.4.2.4
DO  - 10.2991/jrarc.2014.4.2.4
ID  - Marcek2014
ER  -