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

Abstract

Managing market risk under unknown future shocks is a critical issue for policymakers, investors, and professional risk managers. Despite important developments in market risk modeling and forecasting over recent years, market participants are still skeptical about the ability of existing econometric designs to accurately predict potential losses, particularly in the presence of hidden structural changes. In this paper, we introduce Markov‐switching APARCH models under the skewed generalized t and the generalized hyperbolic distributions to fully capture the fuzzy dynamics and stylized features of financial market returns and to generate value‐at‐risk (VaR) forecasts. Our empirical analysis of six major stock market indexes shows the superiority of the proposed models in detecting and forecasting unobservable shocks on market volatility, and in calculating daily capital charges based on VaR forecasts.

Russia–Ukrainian war: measuring the intraday risk dynamics of energy futures contracts using VaR and CVaR

“…Further, we found that amongst the GARCH family models, the estimates of the studentised ARMA (1,1)-APARCH (1,1) are more accurate than those generated by the other models and students ARMA (1,1)-APARCH (1,1) considerably improves the forecasts of one-day ahead VaR forecast. The results are in line with the findings of Assaf (2015), Diamandis et al (2011), andBenSaı €da et al (2018), which also received support by backtesting of the results along the lines of Chen et al (2012b) and implementation of the DQ test of Engle and Manganelli (2004) that the risk embedded in the contracts is better captured at the 1% level.…”

“…The results are in line with the findings of Assaf (2015), Diamandis et al . (2011), and BenSaïda et al . (2018), which also received support by backtesting of the results along the lines of Chen et al .…”

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model

“…In other words, the state probability of the next moment is obtained only by the transition of the state probability at the previous moment. According to the literatures (BenSaïda et al, 2018 ; Lu et al, 2020 ; Ma et al, 2019 ), the transition probabilities are: …”

Previsão de value-at-risk para o mercado de criptomoedas usando modelos EGARCH com regimes markovianos

“…Dadas essas descobertas, tornam-se essenciais modelos adequados para determinar se mudanças de regime estão presentes na dinâmica de volatilidade das criptomoedas. Embora já existam na literatura aplicações empíricas de mudança de regime nos modelos GARCH (Sajjad et al, 2008) e Asymmetric Power ARCH (APARCH) (Bensaida et al, 2018), existem evidências de que o desempenho de um modelo de mudança de regime tende a ser consideravelmente melhorado se combinado com o modelo Exponential GARCH (EGARCH) proposto por Nelson (1991) (Henry, 2009;Dendramis et al, 2014).…”