کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7360631 | 1478824 | 2017 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Using dynamic model averaging in state space representation with dynamic Occam's window and applications to the stock and gold market
ترجمه فارسی عنوان
با استفاده از مدل پویایی میانگین ها در نمایندگی فضایی دولتی با پنجره پویا اکام و برنامه های کاربردی آن در بازار سهام و طلا
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کلمات کلیدی
موضوعات مرتبط
علوم انسانی و اجتماعی
اقتصاد، اقتصادسنجی و امور مالی
اقتصاد و اقتصادسنجی
چکیده انگلیسی
We combine the Onorante and Raftery (2016) dynamic Occam's window approach with the Raftery et al. (2010) DMA/DMS estimator in state space representation to create forecasts using a data-rich forecasting environment. Our approach is mainly related to economic and financial time series that are subject to periods of high volatility, which increases the necessity of a time varying parameter framework. In a forecasting exercise for the stock and gold markets, we highlight the economic value-added of our approach by applying a simple trading rule to the return series. By combining both assets, we show that our approach performs better when compared to alternative forecasting models such as machine learning algorithms and standard DMA/DMS. Results for the complexity of the forecasting models highlight the advantages of high dimensional forecasting approaches in times of economic uncertainty, such as the recent financial crisis. The economic performance of the trading rule weakens when we consider transaction costs.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Empirical Finance - Volume 44, December 2017, Pages 158-176
Journal: Journal of Empirical Finance - Volume 44, December 2017, Pages 158-176
نویسندگان
Marian Risse, Ludwig Ohl,