Reference : Regime switching model for financial data: empirical risk analysis
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Mathematics
Security, Reliability and Trust
http://hdl.handle.net/10993/21249
Regime switching model for financial data: empirical risk analysis
English
Khaled, Salhi [IECL - Institut Élie Cartan de Lorraine (UMR7502) > INRIA TOSCA Team]
Deaconu, Madalina [IECL - Institut Élie Cartan de Lorraine (UMR7502) > INRIA TOSCA Team]
Lejay, Antoine [IECL - Institut Élie Cartan de Lorraine (UMR7502) > INRIA TOSCA Team]
Champagnat, Nicolas [IECL - Institut Élie Cartan de Lorraine (UMR7502) > INRIA TOSCA Team]
Navet, Nicolas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
1-Nov-2016
Physica A: Statistical Mechanics and its Applications
Elsevier Science
461
148–157
Yes (verified by ORBilu)
International
0378-4371
Amsterdam
The Netherlands
[en] Value-at-Risk ; Power tail distribution ; Hidden Markov Model ; Regime switching ; Market risk ; Financial markets
[en] This paper constructs a regime switching model for the univariate Value-at-Risk estimation. Extreme value theory (EVT) and hidden Markov models (HMM) are combined to estimate a hybrid model that takes volatility clustering into account. In the first stage, HMM is used to classify data in crisis and steady periods, while in the second stage, EVT is applied to the previously classified data to rub out the delay between regime switching and their detection. This new model is applied to prices of numerous stocks exchanged on NYSE Euronext Paris over the period 2001–2011. We focus on daily returns for which calibration has to be done on a small dataset. The relative performance of the regime switching model is benchmarked against other well-known modeling techniques, such as stable, power laws and GARCH models. The empirical results show that the regime switching model increases predictive performance of financial forecasting according to the number of violations and tail-loss tests. This suggests that the regime switching model is a robust forecasting variant of power laws model while remaining practical to implement the VaR measurement.
Researchers ; Professionals
http://hdl.handle.net/10993/21249
10.1016/j.physa.2016.05.002
http://hal.inria.fr/hal-01095299

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