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Volatility Forecasting Performance: Evaluation of GARCH type volatility models on Nordic equity indices
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis examines the volatility forecasting performance of six commonly used forecasting models; the simple moving average, the exponentially weighted moving average, the ARCH model, the GARCH model, the EGARCH model and the GJR-GARCH model. The dataset used in this report are three different Nordic equity indices, OMXS30, OMXC20 and OMXH25. The objective of this paper is to compare the volatility models in terms of the in-sample and out-of-sample fit. The results were very mixed. In terms of the in-sample fit, the result was clear and unequivocally implied that assuming a heavier tailed error distribution than the normal distribution and modeling the conditional mean significantly improves the fit. Moreover a main conclusion is that yes, the more complex models do provide a better in-sample fit than the more parsimonious models. However in terms of the out-of-sample forecasting performance the result was inconclusive. There is not a single volatility model that is preferred based on all the loss functions. An important finding is however not only that the ranking differs when using different loss functions but how dramatically it can differ. This illuminates the importance of choosing an adequate loss function for the intended purpose of the forecast. Moreover it is not necessarily the model with the best in-sample fit that produces the best out-of-sample forecast. Since the out-of-sample forecast performance is so vital to the objective of the analysis one can question whether the in-sample fit should even be used at all to support the choice of a specific volatility model.

Place, publisher, year, edition, pages
TRITA-MAT-E, 2014:37
Keyword [en]
Conditional Variance, ARCH, GARCH, EGARCH, GJR-GARCH, volatility forecasting, Parkinson’s estimator
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-146656OAI: diva2:725958
Subject / course
Mathematical Statistics
Educational program
Master of Science - Mathematics
Available from: 2014-06-17 Created: 2014-06-12 Last updated: 2014-06-17Bibliographically approved

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