Stock market volatility is a cornerstone in modern financial analysis applied in a wide range of activities. By exploiting the existence of volatility clusters, conditional heteroskedastic models have been shown to produce superior forecasts and throughout the past two decades a vast literature has emerged testing and extending them. For the emerging markets however, the models have been less rigorously tested although forecasting risk may be especially needed for these highly volatile markets. Further, as the most important advances within conditional heteroskedastic modeling took place before the turn of the millenium, challenging the models with data from the latest decade may spur new light on their capabilities. Through analysis of daily index returns, this paper in several ways challenges the predictive abilities of the models. First, the models are estimated and tested on a sample of emerging markets, all marked by rapid development and high stock market volatility. Second, the sample covers twenty years of data of which seven are reserved to out-of-sample testing. The latter comprise extreme market states in both directions whereby the predictive abilities of the models can be tested under challenging circumstances. Third, to assure the relevance of the research, the estimation and testing approach keeps to the practitioners view. This implies a large number of recursive estimations and a computationally intensive portfolio optimization test. Several conclusions emerge from the analysis. The emerging markets are found to be volatile and their historically low correlations to the world market may not be permanent making volatility modeling highly relevant. For this purpose, the conditional models generally outperform the unconditional in out-of-sample testing as is found in developed markets. This result is fairly consistent and can be extended to the portfolio optimization process where an immense variance decrease was obtainable. Yet, the analysis finds that the conditional models over-shoot volatilities and that the predictive power is markedly lower for the Asian than for the European and Latin American countries. Also, the model fit to the single country generally appears much more important than inclusion of more elaborate model structures. Likewise, inclusion of asymmetric parameters only marginally improves performance.
|Educations||MSc in Applied Economics and Finance, (Graduate Programme) Final Thesis|
|Number of pages||183|