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The paper presents and tests Dynamic Value at Risk (VaR) estimation procedures for equity index returns. Volatility clustering and leptokurtosis are well-documented characteristics of such time series. An ARMA (1, 1)-GARCH (1, 1) approach models the inherent autocorrelation and dynamic volatility. Fat-tailed behavior is modeled in two ways. In the first approach, the ARMA-GARCH process is run assuming alternatively that the standardized residuals are distributed with Pearson Type IV, Johnson S U , Manly's exponential transformation, normal and t-distributions. In the second approach, the ARMA-GARCH process is run with the pseudo-normal assumption, the parameters calculated with the pseudo maximum likelihood procedure, and the standardized residuals are later alternatively modeled with Mixture of Normal distributions, Extreme Value Theory and other power transformations such as John-Draper, Bickel-Doksum, Manly, Yeo-Johnson and certain combinations of the above. The first approach yields five models, and the second approach yields nine. These are tested with six equity index return time series using rolling windows. These models are compared by computing the 99%, 97.5% and 95% VaR violations and contrasting them with the expected number of violations.

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PurposeThe study aims to understand the role of different streams of oil shocks (demand, supply and risk shocks) on the oil-importing and exporting countries' stock returns. The study also examines the impact of crude oil shocks across the economic regimes and market states. Besides, the role of the Global Financial Crisis (GFC) of 2008 in shaping the oil–stock relationship is also investigated.Design/methodology/approachThe authors revisit the impact of oil shocks on emerging equity markets by using the novel shock decomposition algorithm proposed by Ready (2018). The authors consider 24 emerging equity markets for the period spanning over July 15, 2002, to June 18, 2018, and bifurcate them based on oil dependence. The authors use rolling and dynamic conditional correlation analysis to understand the time-varying co-movements between oil prices and stock returns. The regime and state-specific dependence of stock returns on the structural oil shocks are captured by the Markov regime switching and quantile regression models.FindingsThe authors find that the demand shocks are positively associated with stock markets, whereas the supply shocks are negatively related, except in some of the oil-exporting countries. The risk-based shocks also appear to have a negative association with stocks. The authors do not find evidence of strong regime dependence and the direction of relationship across the high and low regimes is somewhat stable. Further, the authors observe an intense oil–stock relationship in the bearish market conditions. Besides, the authors also report evidences of changes in oil–stock relationship onset the GFC.Originality/valueThis is among the first studies to use the oil shock decomposition algorithm of Ready (2018) in the context of emerging equity markets. Additionally, oil shocks' role on the stock market movements across the regimes and market states is studied comprehensively. Thus, the nature of oil shock and the extent to which the emerging markets are exposed is observed in this study.