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EN
The spread of crises across the financial and capital markets of different countries has been studied. The standard method of contagion detection is based on the evolution of the correlation matrix for the example of exchange rates or returns, usually after removing univariate dynamics with the GARCH model. It is a common observation that crises that have occurred in one financial market are usually transmitted to other financial markets/countries simultaneously and that they are visible in different financial variables such as returns and volatility which determine probability distribution. The changes in distributions can be detected through changes in the descriptive statistics of, e.g., returns characterised by expected value, variance, skewness, kurtosis, and other statistics. They determine the shape of the distribution function of returns. These descriptive statistics display dynamics over time. Moreover, they can interreact within the given financial or capital market and among markets. We use the FX currency cluster represented by some of the major currencies and currencies of the Višegrad group. In analysing capital markets in terms of equity indexes, we chose developed markets, such as DAX 30, AEX 25, CAC 40, EURSTOXX 50, FTSE 100, ASX 200, SPX 500, NASDAQ 100, and RUSSEL 2000. We aim to check the changes in descriptive statistics, matrices of correlation concerning exchange rates, returns and volatility based on the data listed above, surrounding two crises: the global financial crisis (GFC) in 2007–2009 and Covid 2019.
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The main goal of this paper is to examine the effects of selected methods of estimation (the Geweke and Porter-Hudak, modified Geweke and Porter-Hudak, Whittle, R/S Rescaled Range Statistic, aggregated variance, aggregated absolute value, and Peng’s variance of residuals methods) and data frequency on properties of Hurst exponents for stock returns, volatility, and trading volumes of 43 companies and eight stock market indices. The calculations have been performed for a time series of log-returns, squared log-returns, and log-volume (based on hourly and daily data) by nine methods. Descriptive statistics and distribution laws of Hurst exponents depend on the method of estimation and, to some extent, on data frequency (daily and hourly). While by and large in log-returns no long memory has been detected, some estimation methods confirm the existence of long memory in squared log-returns. All of the applied estimation methods show long memory in log-volume data.
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