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Causal relationship between Uncertainty and Crude Oil Prices: A Quantile Regression approach
Ruiz Vargas, Andrés Mauricio ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
This work considers the causal relationship between the news-based uncertainty measures and WTI crude oil price within the quantile causality framework by using daily data for a period from January 4, 2000, to November 14, 2016. We find that the Granger non-causality test in quantiles between crude oil returns and the news-based uncertainty measures uncover the causal relationship over different levels of conditional quantiles of the crude oil returns. In particular, there exists a strong causal relationship in the tails of the crude oil returns distribution. Powered by TCPDF (www.tcpdf.org)
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Multifractal Height Cross-Correlation Analysis
Krištoufek, Ladislav
We introduce a new method for detection of long-range cross- correlations and cross-multifractality – multifractal height cross-correlation analysis (MF-HXA). MF-HXA is a multivariate generalization of the height- height correlation analysis. We show that long-range cross-correlations can be caused by a mixture of the following – long-range dependence of separate processes and additional scaling of covariances between the processes. Simi- lar separation applies for cross-multifractality – standard separation between distributional properties and correlations is enriched by division of correlations between auto-correlations and cross-correlations. We further apply the method on returns and volatility of NASDAQ and S&P500 indices as well as of Crude and Heating Oil futures and uncover some interesting results.
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Comovement of Central European stock markets using wavelet coherence: Evidence from high-frequency data
Baruník, Jozef ; Vácha, Lukáš ; Krištoufek, Ladislav
In this paper, we contribute to the literature on international stock market comovement and contagion. The novelty of our approach lies in usage of wavelet tools to high-frequency financial market data, which allows us to understand the relationship between stock market returns in completely different way. Major part of economic time series analysis is done in time or frequency domain separately. Wavelet analysis can combine these two funda- mental approaches, so we can work in time-frequency domain. Using wavelet coherence, we have found very interesting dynamics of cross-correlations be- tween Central European and Western European stock markets. We analyze the high-frequency (5 minute) and low-frequency (daily) data of Czech (PX), Hungarian (BUX) and Polish (WIG) stock indices with a benchmark of German stock index (DAX) on the period of 2008-2009. Our findings provide possibility of a new approach to financial risk modeling.
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Multifractal height cross-correlation analysis
Krištoufek, Ladislav
We introduce a new method for detection of long-range cross-correlations and cross-multifractality – multifractal height cross-correlation analysis (MF-HXA). We show that long-range cross-correlations can be caused by long-range dependence of separate processes and the correlations above them. Similar separation applies for cross-multifractality – standard sep- aration between distributional properties and correlations is enriched by division of correlations between auto-correlations and cross-correlations. Efficiency of the method is showed on two types of simulated series – ARFIMA and Mandelbrot’s Binomial Multifractal model. We further ap- ply the method on returns and volatility of NASDAQ and S&P500 indices and uncover some interesting results.
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