کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
478268 1446040 2014 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation
ترجمه فارسی عنوان
رویکرد نیمه پارامتریک بیزی برای تجزیه و تحلیل سری زمانی مالی با برنامه های کاربردی برای ارزش گذاری در برآورد خطر
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی


• We develop a Bayesian semiparametric approach to GARCH-type models.
• The innovations follow the class of scale mixtures of Gaussian distributions with a Dirichlet process prior in the mixing distribution.
• It is also shown how to undertake Bayesian prediction of the Value at Risk (VaR).
• We have obtained significant differences in the predictive distribution of the returns, especially in the tails.
• We have observed different results in the VaR estimation with different specifications.

GARCH models are commonly used for describing, estimating and predicting the dynamics of financial returns. Here, we relax the usual parametric distributional assumptions of GARCH models and develop a Bayesian semiparametric approach based on modeling the innovations using the class of scale mixtures of Gaussian distributions with a Dirichlet process prior on the mixing distribution. The proposed specification allows for greater flexibility in capturing the usual patterns observed in financial returns. It is also shown how to undertake Bayesian prediction of the Value at Risk (VaR). The performance of the proposed semiparametric method is illustrated using simulated and real data from the Hang Seng Index (HSI) and Bombay Stock Exchange index (BSE30).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: European Journal of Operational Research - Volume 232, Issue 2, 16 January 2014, Pages 350–358
نویسندگان
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