• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.463-477
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• 2009

Parameter estimation methods such as maximum likelihood estimation method, probability weighted moments method, regression method have been popularly applied to various extreme value models in numerous literature. Among three methods above, the performance of regression method has not been rigorously investigated yet. In this paper the regression method is compared with the other methods via Monte Carlo simulation studies for estimation of parameters of the Generalized Extreme Value(GEV) distribution and the Generalized Pareto(GP) distribution. Our simulation results indicate that the regression method tends to outperform other methods under small samples by providing smaller biases and root mean square errors for estimation of location parameter of the GEV model. For the scale parameter estimation of the GP model under small samples, the regression method tends to report smaller biases than the other methods. The regression method tends to be superior to other methods for the shape parameter estimation of the GEV model and GP model when the shape parameter is -0.4 under small and moderately large samples.

• Environmental and Resource Economics Review
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• v.14 no.4
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• pp.943-964
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• 2005

This study estimated a wide range of stochastic process models using the frameworks of CKLS (1992) and Nowman and Wang (2001). For empirical analysis, the GMM estimation procedure is adopted for the monthly Brent crude oil prices from January 1996 to January 2005. Using the simulated price series, European call option premiums were calculated and compared each other. The empirical results suggest that the crude oil price has a strong dependency of volatility on the price level. Contrary to the results of previous related studies, it shows a weak tendency of mean reversion. In addition, the models provide different implications for pricing derivatives on crude oil.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.92-99
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• 2022

This study show the empirical results and the models that explain the content creator's personal brand value in the user-generated content platform. Producer's brand value performance could have enhancement and dilution by their activities for the long-term and repetitive change. Therefore, for the measure and analysis, the models have to catch the effect from producer's the diverse activities. This study would find the guideline by competitive analysis between (1) the impact of in-group user's self-motivated participation and (2) the impact of the social links from the outside platform. Based on the analysis results, producer's creation activity as focused on the specific and professional category increase their brand value for the long-term. However, producers would have to upload diverse category, after users are bored to their similar videos' as before. These empirical results would be a guidelines to the content management strategies for producers and the platform.