Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets

Abstract

This research observes a time varying relationship between stock returns, volatilities and the online search volume in regard to selected CESEE (Central, Eastern and South-Eastern European) stock markets. The main hypothesis of the research assumes that a feedback relationship exists between stock returns, volatilities and the investor’s attention variable (captured by the online search volume). Moreover, the relationship is assumed to be time varying due to changing market conditions. Previous research does not deal with the time-varying multi-directional relationship. Thus, the contribution to existing research consists of estimating the aforementioned relationship between return, volatility and the search volume series for selected CESEE countries by using a novel approach of spillover indices within the VAR (Vector AutoRegression) model framework. The results indicate that the Google search volume affects the risk series more than the return series on the selected markets.

Higher Moments Actually Matter: Spillover Approach for Case of CESEE Stock Markets

“…There are very short-length approaches, such as the 50-day (or two months) length in [89], which focuses on volatility spillover in the US stock market. Other applications utilize 30 months, such as [90] for a macroeconomic model or [91] for the case of stock market data "Google-ing". There are also studies with longer lengths of the rolling window, such as [92], where Euro area financial markets spillovers were examined on a 2-year (or 104 weeks, i.e., 26 months) basis.…”

What threatens stock markets more - The coronavirus or the hype around it?

“…The mood of stock market investors affects their willingness to take risks ( Yuen & Lee, 2003 ), which, in its turn, affects the volatility of stock markets ( Gupta et al (2018) ; Smales (2014) ), liquidity of shares ( Shyu, Gao, Wu, & Zhu, 2020 ) and the volume of trading ( Sifat & Thaker, 2020 ). Uncertainty and anxiety, as reflected in an increased number of Google queries, have a negative effect on stock market indices ( Škrinjarić (2019) ; Maneejuk and Yamaka (2019) ). Twitter blogs also demonstrate forecast effects ( Zhang, Fuehres, and Gloor (2012) ; De Jong, Elfayoumy, and Schnusenberg (2017) ).…”