Regime switching model for financial data: Empirical risk analysis

Abstract

h i g h l i g h t s• This paper introduces a regime switching model for Value-at-Risk estimation.• Hidden Markov models and extreme value theory are combined into a hybrid model.• The regime switching model is applied to real data NYSE Euronext stocks.• Classifying data in two states permits a fast detection of regime switching. 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.

Value‐at‐risk under market shifts through highly flexible models

“…A large literature has been devoted to the VaR estimation and forecasting using various methodologies (e.g., Billio & Pelizzon, 2000;Giot & Laurent, 2003;Nieto & Ruiz, 2016;Rodríguez & Ruiz, 2012;Salhi, Deaconu, Lejay, Champagnat, & Navet, 2016) to the extent that VaR estimates are central to ass et allocation and capital adequacy decisions of banking and financial institutions. For example, Giot and Laurent (2003) use several univariate and multivariate GARCH-based models with the skewed-t distribution to model the VaR for traders that have both short and long trading positions.…”

Volatility Regime and Equity Portfolio Return: Evidence from Europe

“…The results of their study suggested that the regime shifts of bull-to-bear and bear-to-crash carried substantial prices of risk. Salhi et al (2016) also employed a regime switching model and estimated Value-at-Risk (VaR) using data of NYSE Euronext stocks from 2001 to 2011. The results of their study showed that the regime switching model employed in their analyses improved the predictive performance of VaR forecasting.…”

Trade clustering and power laws in financial markets

“…These power-law findings are highly consequential, mainly because extreme outcomes are by definition rare, so attempts to estimate prices or quantities with tail risk sensitivity through nonparametric methods are deeply problematic (Salhi et al 2016). Thus, information on the specific functional form of the tails of these distributions has great value for econometricians and practitioners.…”