392 research outputs found
Testing power-law cross-correlations: Rescaled covariance test
We introduce a new test for detection of power-law cross-correlations among a
pair of time series - the rescaled covariance test. The test is based on a
power-law divergence of the covariance of the partial sums of the long-range
cross-correlated processes. Utilizing a heteroskedasticity and auto-correlation
robust estimator of the long-term covariance, we develop a test with desirable
statistical properties which is well able to distinguish between short- and
long-range cross-correlations. Such test should be used as a starting point in
the analysis of long-range cross-correlations prior to an estimation of
bivariate long-term memory parameters. As an application, we show that the
relationship between volatility and traded volume, and volatility and returns
in the financial markets can be labeled as the one with power-law
cross-correlations.Comment: 15 pages, 4 figure
What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis
Bitcoin has emerged as a fascinating phenomenon of the financial markets.
Without any central authority issuing the currency, it has been associated with
controversy ever since its popularity and public interest reached high levels.
Here, we contribute to the discussion by examining potential drivers of Bitcoin
prices ranging from fundamental to speculative and technical sources as well as
a potential influence of the Chinese market. The evolution of the relationships
is examined in both time and frequency domains utilizing the continuous
wavelets framework so that we comment on development of the interconnections in
time but we can also distinguish between short-term and long-term connections.Comment: 19 pages, 5 figure
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. We penalize
the popular stocks by assigning them lower portfolio weights and we bring
forward the less popular, or peripheral, stocks to decrease the total riskiness
of the portfolio. Our results indicate that such strategy dominates both the
benchmark index and the uniformly weighted portfolio both in-sample and
out-of-sample.Comment: 11 pages, 3 figure
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