Abstract
The need for comparative backtesting in the Basel III framework presents the challenge for ranking of internal value-at-risk (VaR) and expected shortfall (ES) models. We use a joint loss function to score the elicitable joint VaR and ES models to select competing tail risk models for the top 9 emerging markets equities and the emerging markets composite index. We achieve this with the model confidence set (MCS) procedure. Our analysis span two sub-sample periods representing turbulent (Eurozone and Global Financial crises periods) and tranquil (post-Global Financial crisis period) market conditions. We find that many of the markets risk models are time-invariant and independent of market conditions. But for China and South Africa this is not true because their risk models are time-varying, market conditions-dependent, percentile-dependent and heterogeneous. Tail risk modelling may be difficult compared to other markets. The resemblance between China and South Africa can stem from the closeness between their equities composition. However, generally, there is evidence of more homogeneity than heterogeneity in risk models. This is indicated by a minimum of three models (out of six) per equity in most of the countries. This may ease the burden for risk managers to find the optimal set of models. Our study is important for internal risk modelling, regulatory oversight, reduce regulatory arbitrage and may bolster confidence in international investors with respect to emerging markets equities.
Highlights
Value-at-risk (VaR) and expected shortfall (ES) have been the two main regulatory capital requirement and portfolio risk measures for a long time
We offer some novel insights to perform this task on emerging markets (EMs) equities in order to reduce regulatory arbitrage
The homogeneity in the superior set models (SSMs) is suggestive of well diversified portfolios for the respective equity, given that different distributional assumptions are applied to the returns
Summary
Value-at-risk (VaR) and expected shortfall (ES) have been the two main regulatory capital requirement and portfolio risk measures for a long time. We seek to model and forecast tail risk of sampled emerging market equities using the (VaR, ES) model in light of the current regulatory framework. Testing procedures for “best” fitting models include; Reality Check (White [32]), Stepwise Multiple Testing (Romano & Wolf [33]), Superior Predictive Ability (Hansen & Lunde [34]) and Conditional Predictive Ability (Giacomini & White [35]), are among the recent ones in the literature These approaches lack a consistent scoring function such as the FZL function. Our study makes important contributions to the literature on EMs equities risk analysis This is among the first studies to model tail risks in EMs equities with the joint (VaR, ES) model based on FZL function, conforming to the current regulatory framework (i.e., Basel III). Six may not be a large number, large SSM is important for portfolio selection under different market conditions
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