دانلود رایگان مقاله انگلیسی نفت و پیش بینی کوتاه مدت نوسانات بازده سهام - الزویر 2018

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رایگان

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عنوان فارسی

نفت و پیش بینی کوتاه مدت نوسانات بازده سهام

عنوان انگلیسی

Oil and the short-term predictability of stock return volatility

صفحات مقاله فارسی

صفحات مقاله انگلیسی

سال انتشار

2018

نشریه

الزویر - Elsevier

فرمت مقاله انگلیسی

PDF

نوع مقاله

ISI

نوع نگارش

مقالات پژوهشی (تحقیقاتی)

رفرنس

دارد

پایگاه

اسکوپوس

کد محصول

E9016

رشته های مرتبط با این مقاله

علوم اقتصادی

گرایش های مرتبط با این مقاله

اقتصاد مالی، اقتصاد پولی و اقتصاد نفت و گاز

مجله

مجله امور مالی عملی - Journal of Empirical Finance

دانشگاه

School of Economics and Management - Nanjing University of Science and Technology - China

کلمات کلیدی

نوسان نفت خام، نوسان سهام، رگرسیون پیش بینی شده، عملکرد خارج از نمونه، اهمیت اقتصادی

doi یا شناسه دیجیتال

https://doi.org/10.1016/j.jempfin.2018.03.002

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۰.۰ (هنوز امتیازی ثبت نشده است)

چکیده

abstract

The goal of this paper is to show that crude oil volatility is predictive of stock volatility in the short-term from both in-sample and out-of-sample perspectives. The revealed predictability is also of economic significance, as shown by examining the performance of portfolios constructed on the oil-based forecasts of stock volatility. Results from robustness tests suggest that oil volatility provides different information from traditional macro variables. Further analysis shows that simple linear regression is sufficient for capturing predictive relationships between oil and stock volatility. Oil volatility is found to predict return volatilities of a significant number of industry portfolios during recent periods.

نتیجه گیری

9. Conclusions

This paper examines the evidence for short-term predictability of U.S. stock volatility using crude oil volatility as predictor. We establish several findings. First, the slope coefficients in predictive regressions of stock volatility on the oil volatility are significantly positive via an in-sample analysis. Second, adding oil volatility to the benchmark of autoregressive model can significantly improve out-of-sample forecasting performance. Third, we establish the economic significance of the stock volatility predictability by showing that the portfolio constructed on oil-based forecasts of stock volatility displays a higher certainty equivalent return than the benchmark forecasts.

We examine the robustness of predictability by considering a wide range of alternative benchmark models with macro variables. Our results suggest that the predictability is not affected by the change of benchmark models to a large extent. Our finding gives a new factor explaining and forecasting stock return volatility. We further extend the forecasting exercise in three dimensions. First, we use nonlinear models to capture the relationship between oil and stock volatility. However, we find little evidence supporting the superiority of nonlinear models over the simple linear models. Second, we do forecasting analysis for longer horizons. Our evidence indicates that oil cannot predict stock volatility for longer horizons of 9 months. Finally, we forecast industry portfolio return volatilities using oil volatility. The results show the existence of significant predictability for a significant number of portfolios during recent periods.