Volatility Forecast Using GARCH, News Sentiment and Implied Volatility

2016 ◽  
Author(s):  
Jamie Atkinson ◽  
Gautam Mitra ◽  
Xiang Yu
2017 ◽  
Vol 5 (9) ◽  
pp. 41
Author(s):  
Guillermo Benavides

There has been substantial research effort aimed to forecast futures price return volatilities of financial assets. A significant part of the literature shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of several volatility forecast models for the case of the Mexican peso-USD exchange rate futures returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), two option implied volatility models and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. Different to other works in the literature, in this paper there is a more rigorous analysis of the option implied volatilities calculations. The results show that the option implied models are superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-squared-errors. However, the results should be taken with caution given that the coefficient of determination in the regressions was relatively low. According to these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied.


2021 ◽  
Vol 71 ◽  
pp. 943-954
Author(s):  
Dehong Liu ◽  
Yucong Liang ◽  
Lili Zhang ◽  
Peter Lung ◽  
Rizwan Ullah

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