کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5100280 1478826 2017 45 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?
ترجمه فارسی عنوان
پیش بینی نوسانات سهام کل بازار با استفاده از پیش بینی های مالی و اقتصاد کلان: کدام مدل بهترین، زمان و چرا پیش بینی می شود؟
موضوعات مرتبط
علوم انسانی و اجتماعی اقتصاد، اقتصادسنجی و امور مالی اقتصاد و اقتصادسنجی
چکیده انگلیسی
This paper revisits the topic of forecasting aggregate stock market volatility using financial and macroeconomic predictors in a comprehensive Bayesian model averaging framework. Candidate models include time-varying (with various degrees of dynamics) and constant-coefficient autoregressions based on the logarithm of monthly realized volatility augmented with exogenous predictors capturing risk premia, leverage, bond rates and proxies for credit risk. Thus, we simultaneously address parameter instability and model uncertainty that unavoidably impact volatility predictions. Applied to monthly S&P 500 volatility from 1926 to 2010, we find that Bayesian model averaging with time-varying regression coefficients provides very competitive density and modest improvements in point forecasts compared to rival approaches.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Empirical Finance - Volume 42, June 2017, Pages 131-154
نویسندگان
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