کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5097437 | 1376589 | 2006 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Analysis of high dimensional multivariate stochastic volatility models
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کلمات کلیدی
Metropolis-Hastings algorithmMarginal likelihood - احتمال عدالتValue-at-Risk - ارزش در معرض خطرRisk measurement - اندازه گیری ریسکBayesian inference - تخمین بیزین یا استنباط بیزیStudent-t distribution - توزیع دانشجوییMarkov chain Monte Carlo - زنجیره مارکف مونت کارلوSimulation - شبیه سازیParticle Filter - فیلتر ذرهState space model - مدل فضایی دولتیVolatility - نوسانTime-varying correlations - همبستگی متغیر زمانForecasting - پیش بینی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
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چکیده انگلیسی
This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional multivariate time series models with time varying correlations. The model proposed and considered here combines features of the classical factor model with that of the heavy tailed univariate stochastic volatility model. A unified analysis of the model, and its special cases, is developed that encompasses estimation, filtering and model choice. The centerpieces of the estimation algorithm (which relies on MCMC methods) are: (1) a reduced blocking scheme for sampling the free elements of the loading matrix and the factors and (2) a special method for sampling the parameters of the univariate SV process. The resulting algorithm is scalable in terms of series and factors and simulation-efficient. Methods for estimating the log-likelihood function and the filtered values of the time-varying volatilities and correlations are also provided. The performance and effectiveness of the inferential methods are extensively tested using simulated data where models up to 50 dimensions and 688 parameters are fit and studied. The performance of our model, in relation to various multivariate GARCH models, is also evaluated using a real data set of weekly returns on a set of 10 international stock indices. We consider the performance along two dimensions: the ability to correctly estimate the conditional covariance matrix of future returns and the unconditional and conditional coverage of the 5% and 1% value-at-risk (VaR) measures of four pre-defined portfolios.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Econometrics - Volume 134, Issue 2, October 2006, Pages 341-371
Journal: Journal of Econometrics - Volume 134, Issue 2, October 2006, Pages 341-371
نویسندگان
Siddhartha Chib, Federico Nardari, Neil Shephard,