Performance of Value at Risk models in the midst of the global financial crisis in selected CEE emerging capital markets

Abstract

The aim of this paper is to investigate the performance of Value at Risk (VaR) models in selected Central and Eastern European (CEE) emerging capital markets. Daily returns of Croatian (CROBEX), Czech (PX50), Hungarian (BUX) and Romanian (BET) stock exchange indices are analysed for the period January, 2000 -February, 2012, while daily returns of the Serbian (BELEX15) index is examined for the period September, 2005 -February, 2012. In recent years there has been much research conducted into VaR in developed markets, while papers dealing with VaR calculation in CEE are rare. Furthermore, VaR models created and suited for liquid and welldeveloped markets that assume normal distribution are less reliable for capital markets in emerging economies, such as Central and Eastern European Union member and candidate states. Since capital markets in European emerging economies are highly volatile, less liquid and strongly dependent on the unexpected external shocks, market risk estimation based on normality assumption in CEE countries is more problematic. This motivates us to implement GARCH-type methods that involve time varying volatility and heavy tails of the empirical distribution of returns. We test the hypothesis that using the assumption of heavy tailed distribution it is possible to forecast market risk more precisely, especially in times of crisis, than under the assumption of normal distribution or using historical simulations method. Our backtesting results for the last 500 observations are based on the Kupiec POF and Christoffersen independence test. They show that GARCH-type models with t error distribution in most analysed cases give better VaR estimation than GARCH type models with normal errors in the case of a 99% confidence level, while in the case of a 95% confidence level it is the opposite. The results of backtesting analysis for the crisis period (after the collapse of Lehman Brothers) show that GARCH-type models with t-distribution of residuals provide better VaR estimates compared with GARCH-type models with normal distribution, historical simulations and RiskMetrics methods. The RiskMetrics method in the most cases underestimates market risk.

Investment Environment Problems Analysis and Evaluation: An Ex Post Empirical Analysis and Performance Implications

“…The research results indicate the importance of the simultaneous application of different VaR models since the priority cannot be given to one over another in all observed emerging markets. Miletic et al (2015) explore the impact of the global economic crisis on the performance of the VaR model in transitional markets. The authors especially emphasise the possibility of market risk prediction in the crisis business conditions, with the assumption of normal distribution or the use of historical data (HS VaR).…”

Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states

“…Stock markets differ dramatically between states. Many researchers have been comparing the performance of the models before and after turbulent states (among others, in McAleer, Jiménez-Martín, and Pérez-Amaral (2013); Degiannakis, Floros, and Livada (2012); Chlebus (2016); Burchi and Martelli (2016)) and between different stock markets (among others in Žiković (2007); Djaković and Radiscaron (2010); Mutu, Balogh, and Moldovan (2011); Miletic and Miletic (2015); Buczyński and Chlebus (2018)). Most of them notice the advantages of GARCH models in periods of increased volatility, mentioning its conservatism and abrupt fit to the market situation.…”

Untitled

“…e.g. [Wiener 1997], [Bohdalova 2007] or [Miletic, Miletic 2015]. VaR estimation for different types of funds can be found, among others in the works [Grau-Carles et al 2009], [Fedor 2010].…”