EconPapers    
Economics at your fingertips  
 

GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks

Mateusz Buczyński () and Marcin Chlebus ()

No 2021-08, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: This study proposes a new GARCH specification, adapting a long short-term memory (LSTM) neural network's architecture. Classical GARCH models have been proven to give substantially good results in the case of financial modeling, where high volatility can be observed. In particular, their high value is often praised in the case of Value-at-Risk. However, the lack of nonlinear structure in most of the approaches entails that the conditional variance is not represented in the model well enough. On the contrary, recent rapid advancement of deep learning methods is said to be capable of describing any nonlinear relationships prominently. We suggest GARCHNet - a nonlinear approach to conditional variance that combines LSTM neural networks with maximum likelihood estimators of probability in GARCH. The distributions of the innovations considered in the paper are: normal, t and skewed t, however the approach does enable extensions to other distributions as well. To evaluate our model, we have executed an empirical study on the log returns of WIG 20 (Warsaw Stock Exchange Index) in four different time periods throughout 2005 and 2021 with varying levels of observed volatility. Our findings confirm the validity of the solution, however we present several directions to develop it further.

Keywords: Value-at-Risk; GARCH; neural networks; LSTM (search for similar items in EconPapers)
JEL-codes: C52 C53 C58 G32 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-ecm, nep-ets, nep-for, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://www.wne.uw.edu.pl/index.php/download_file/6457/ First version, 2021 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2021-08

Access Statistics for this paper

More papers in Working Papers from Faculty of Economic Sciences, University of Warsaw Contact information at EDIRC.
Bibliographic data for series maintained by Marcin Bąba ().

 
Page updated 2021-10-12
Handle: RePEc:war:wpaper:2021-08