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Národní úložiště šedé literatury Nalezeno 3 záznamů.  Hledání trvalo 0.00 vteřin. 
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.
Úplný záznam
Evaluating the Efficient Market Hypothesis by means of isoquantile surfaces and the Hurst exponent
 Ivanková, Kristýna ; Krištoufek, Ladislav ; Vošvrda, Miloslav
This article extends our previous work on applications of isoquantile (formerly isobar) surfaces to market analysis. The approach is applied to lagged returns of selected stock market indices and compared to various estimations of the Hurst exponent. We evaluate the Efficient Market hypothesis by means of the two aforementioned approaches for the ASPI, BET, BUX, JSX, NASDAQ, PX and S&P500 indices. The more does a time series satisfy the EMH, the closer it resembles Brownian motion. In this case isoquantile surfaces form a circle and the Hurst exponent approaches 1/2.
Úplný záznam
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.
Úplný záznam

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