کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5107435 1377579 2017 30 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH
موضوعات مرتبط
علوم انسانی و اجتماعی مدیریت، کسب و کار و حسابداری کسب و کار و مدیریت بین المللی
پیش نمایش صفحه اول مقاله
Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH
چکیده انگلیسی
This paper examines whether nonlinear models, like Principal Components Combining, neural networks and GARCH are more accurate on realized volatility forecasting than the Heterogeneous Autoregressive (HAR) model. The answer is no. The realized volatility property of persistence is too important to leave out of a realized volatility forecasting model. However, the Principal Components Combining model is ranked very close to HAR. Analysis is implemented in seven US financial markets: spot equity, spot foreign exchange rates, exchange traded funds, equity index futures, US Treasury bonds futures, energy futures, and commodities options.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Research in International Business and Finance - Volume 39, Part B, January 2017, Pages 824-839
نویسندگان
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