Paper review: Can We Use Daily Internet Search Query Data to improve Predicting Power of EGARCH Models for Financial Time Series Volatility?

Seeing as Risteski & Davcev (2014) has honored me with a reference in their paper, I will share my thoughts on their research.

Their paper is available here.

Risteski & Davcev uses daily Internet Search Data from Google Trends to improve the predictive power of an EGARCH volatility model for the CAC40 index. They find that the predictive ability of their model, called EGARCH-SVI in the paper, is greater than the normal EGARCH model. The results are in line with my own research.

Data

Ristesku & Davcev uses daily and weekly index returns of the CAC40 index. They extend the EGARCH model with search volume for the term "CAC40". They point out that there is a two day delay in the data from Google Trends, something that is important to take into consideration when contemplating any practical implementation of a predictive model. In accordance with this practical limitation of the data, Ristesku & Davcev uses a two day lag for the daily model, and a one week lag for the weekly data.

Method

To compare the strength of the EGARCH-SVI model, they compare it to a normal EGARCH model. They kindly provide a reference to this blog for the method I have developed to create daily time series from Google Trends on time periods longer than the 90 days provided by Google. The strength of the Search Volume Index variable is measured by its significance level in the EGARCH regression. The forecasting performance is measured by difference in the mean square error, mean absolute error, the information criteria, and Diebold-Mariano between the normal EGARCH and the EGARCH-SVI model.

Results

The results show that the EGARCH-SVI model has better forecasting power than the basic EGARCH model. The in sample test results are stronger than the out of sample results, but both point to an improvement in the predictive ability of the EGARCH-SVI model as compared to the basic EGARCH model.

Comments on the paper

Seeing as the EGARCH model has been shown in previous research to be powerful in accounting for heteroskedasticity in financial time series, it is an ideal method to append with search volume data. The results of Ristesku & Davcev are in line with my own results.