Modeling Heavy-Tailed Stock Index Returns Using the Generalized Hyperbolic Distribution
AbstractIn the present study, we estimate the parameters of the Generalized Hyperbolic Distribution for a series of stock index returns including the Romanian BETC and indexes from other two Eastern European countries, Hungary and the Czech Republic. Using different econometric techniques, we investigate whether the estimated Generalized Hyperbolic Distribution is an appropriate approximation for the empirical distribution computed by non-parametric kernel econometric methods. The main finding of the analysis is that the probability density function of the estimated Generalized Hyperbolic Distribution represents a very close approximation (at least up to the 4th order term) of the empirical probability distribution function.
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Bibliographic InfoArticle provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.
Volume (Year): 6 (2009)
Issue (Month): 2 (June)
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Generalized Hyperbolic Distribution; heavy-tailed returns; non-parametric density estimation;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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- Necula, Ciprian, 2010. "Modeling the Dependency Structure of Stock Index Returns using a Copula Function Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 93-106, September.
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