The Predictive Power of Conditional Models: What Lessons to Draw with Financial Crisis in the Case of Pre-Emerging Capital Markets?

The uncertainty plays a central role in most of the problems which addressed by the modern financial theory. For some time, we know that the uncertainty under the speculative price varies over the time. However, it is only recently that a lot of studies in applied finance and monetary economics using the explicit modelling of time series involving the second and the higher moments of variables. Indeed, the first tool appeared in order to model such variables has been introduced by Engel (1982). This is the autoregressive conditional heteroskedasticity and its many extensions. Thus, with the emergence and development of these models, Value-at-Risk, which plays a major role in assessment and risk management of financial institutions, has become a more effective tool to measure the risk of asset holdings. Following the current financial debacle, we give the simple question about the progress and some achievements made in the context of emerging and pre-emergent financial markets microstructure which can sustain and limit the future fluctuations. Today, we know that the crisis has no spared any financial market in the world. The magnitude and damage of the crisis effects vary in the space and time. In the Moroccan stock market context, it was found that the effects were not so harmful and that the future of these markets faces a compromise or at least a long lethargy. Indeed, inspired by these events, our study attempts to undertake two exercises. In first, we are testing the ability of the nonlinear ARCH and GARCH models (EGARCH, TGARCH, GJR-GARCH, QGARCH) to meet the number of expected exceedances (shortfalls) of VaR measurement. In second, we are providing a forecasting volatility under the time-varying of VaR.