Article (Scientific journals)
Regime switching model for financial data: empirical risk analysis
Khaled, Salhi; Deaconu, Madalina; Lejay, Antoine et al.
2016In Physica A. Statistical Mechanics and its Applications, 461, p. 148–157
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Keywords :
Value-at-Risk; Power tail distribution; Hidden Markov Model; Regime switching; Market risk; Financial markets
Abstract :
[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.
Disciplines :
Mathematics
Author, co-author :
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 ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
yes
Language :
English
Title :
Regime switching model for financial data: empirical risk analysis
Publication date :
01 November 2016
Journal title :
Physica A. Statistical Mechanics and its Applications
ISSN :
0378-4371
eISSN :
1873-2119
Publisher :
Elsevier Science, Amsterdam, Netherlands
Volume :
461
Pages :
148–157
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust
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