Bitcoin and S&P500: Co-movements of high-order moments in the time-frequency domain.

Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency.

Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers.

The aim of this study is to examine the extreme return spillovers among the US stock market sectors in the light of the COVID-19 outbreak. To this end, we extend the now-traditional Diebold-Yilmaz spillover index to the quantiles domain by building networks of generalized forecast error variance decomposition of a quantile vector autoregressive model specifically for extreme returns. Notably, we control for common movements by using the overall stock market index as a common factor for all sectors and uncover the effect of the COVID-19 outbreak on the dynamics of the network.

Nowcasting unemployment rates with Google searches: evidence from the Visegrad Group countries.

The online activity of Internet users has repeatedly been shown to provide a rich information set for various research fields. We focus on job-related searches on Google and their possible usefulness in the region of the Visegrad Group--the Czech Republic, Hungary, Poland and Slovakia. Even for rather small economies, the online searches of inhabitants can be successfully utilized for macroeconomic predictions.

What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis.

The Bitcoin has emerged as a fascinating phenomenon in the Financial markets. Without any central authority issuing the currency, the Bitcoin has been associated with controversy ever since its popularity, accompanied by increased public interest, reached high levels. Here, we contribute to the discussion by examining the potential drivers of Bitcoin prices, ranging from fundamental sources to speculative and technical ones, and we further study the potential influence of the Chinese market.

Detrended fluctuation analysis as a regression framework: estimating dependence at different scales.

We propose a framework combining detrended fluctuation analysis with standard regression methodology. The method is built on detrended variances and covariances and it is designed to estimate regression parameters at different scales and under potential nonstationarity and power-law correlations. The former feature allows for distinguishing between effects for a pair of variables from different temporal perspectives.

Underpricing, underperformance and overreaction in initial public offerings: Evidence from investor attention using online searches.

Online activity of Internet users has proven very useful in modeling various phenomena across a wide range of scientific disciplines. In our study, we focus on two stylized facts or puzzles surrounding the initial public offerings (IPOs) - the underpricing and the long-term underperformance. Using the Internet searches on Google, we proxy the investor attention before and during the day of the offering to show that the high attention IPOs have different characteristics than the low attention ones.

Spectrum-based estimators of the bivariate Hurst exponent.

We discuss two alternate spectrum-based estimators of the bivariate Hurst exponent in the power-law cross-correlations setting, the cross-periodogram and local X-Whittle estimators, as generalizations of their univariate counterparts. As the spectrum-based estimators are dependent on a part of the spectrum taken into consideration during estimation, a simulation study showing performance of the estimators under varying bandwidth parameter as well as correlation between processes and their specification is provided as well. These estimators are less biased than the already existent averaged periodogram estimator, which, however, has slightly lower variance.

BitCoin meets Google Trends and Wikipedia: quantifying the relationship between phenomena of the Internet era.

Digital currencies have emerged as a new fascinating phenomenon in the financial markets. Recent events on the most popular of the digital currencies--BitCoin--have risen crucial questions about behavior of its exchange rates and they offer a field to study dynamics of the market which consists practically only of speculative traders with no fundamentalists as there is no fundamental value to the currency. In the paper, we connect two phenomena of the latest years--digital currencies, namely BitCoin, and search queries on Google Trends and Wikipedia--and study their relationship.

Fractal markets hypothesis and the global financial crisis: wavelet power evidence.

We analyze whether the prediction of the fractal markets hypothesis about a dominance of specific investment horizons during turbulent times holds. To do so, we utilize the continuous wavelet transform analysis and obtained wavelet power spectra which give the crucial information about the variance distribution across scales and its evolution in time. We show that the most turbulent times of the Global Financial Crisis can be very well characterized by the dominance of short investment horizons which is in hand with the assertions of the fractal markets hypothesis.

Can Google Trends search queries contribute to risk diversification?

Portfolio diversification and active risk management are essential parts of financial analysis which became even more crucial (and questioned) during and after the years of the Global Financial Crisis. We propose a novel approach to portfolio diversification using the information of searched items on Google Trends. The diversification is based on an idea that popularity of a stock measured by search queries is correlated with the stock riskiness.