کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
558728 1451740 2015 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian changepoint and time-varying parameter learning in regime switching volatility models
ترجمه فارسی عنوان
تغییر پارامتر بیزی و یادگیری پارامتر زمان متغیر در مدل های نوسان پذیری رژیم
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی

This paper proposes a combined state and piecewise time-varying parameter learning technique in regime switching volatility models using multiple changepoint detection. This approach is a Sequential Monte Carlo method for estimating GARCH & EGARCH based volatility models with an unknown number of changepoints. Modern auxiliary particle filtering techniques are used to calculate the posterior densities and online forecasts. This approach also automatically deals with the common ancestral path dependence problem faced in these type volatility models. The model is tested on Borsa Istanbul (BIST) formerly known as Istanbul Stock Exchange (ISE) market data using daily log returns. A full structural changepoint specification is defined in which all parameters of the conditional variance of the volatility models are dynamic. Finally, it is shown with simulation experiments that the proposed approach partitions the series into several regimes and learns the parameters of each regime's volatility model in parallel with the multiple changepoint detection process.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Digital Signal Processing - Volume 40, May 2015, Pages 198–212
نویسندگان
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