مقاله انگلیسی رایگان در مورد نوسانات بازده سهام – الزویر 2020

This study examines the predictability of stock market implied volatility on stock volatility in five developed economies (the US, Japan, Germany, France, and the UK) using monthly volatility data for the period 2000 to 2017. We utilize a simple linear autoregressive model to capture predictive relationships between stock market implied volatility and stock volatility. Our insample results show there exists very significant Granger causality from stock market implied volatility to stock volatility. The out-of-sample results also indicate that stock market implied volatility is significantly more powerful for stock volatility than the oil price volatility in five developed economies.

Introduction

Stock market volatility is a crucial input for risk management, asset pricing and portfolio management. Therefore, modeling and forecasting stock market volatility remain a hot topic in financial econometrics. To improve the stock market volatility forecasting, some studies have constructed new and powerful predictors or factors. Schwert (1989) finds limited support for links between volatility and macroeconomic predictors, whereas more recent papers such as Christiansen, Schmeling, and Schrimpf (2012), Paye (2012), Engle, Ghysels, and Sohn (2013), Conrad and Loch (2015), and Nonejad (2017), Mohsen and Sujata (2019) arrive at somewhat more encouraging results by constructing the macroeconomic and financial variables. Very recently, Feng, Wang, and Yin (2017) find that oil volatility risk premium (oil VRP) does exhibit statistically and economically significant in-sample and out-of-sample forecasting power for stock market volatility in G7 countries. Wang, Wei, Wu, and Yin (2018) also show that the crude oil volatility is predictive of stock volatility in the short-term from both in-sample and out-ofsample perspectives. In addition, Bašta and Molnár (2018) study the comovement between volatility of the equity market and the oil market, both for implied and realized volatilities by using the wavelets method.