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on Market Microstructure |
| By: | Robert S. Chirinko (University of Illinois at Chicago and CESifo); Hisham Foad (Department of Economics, San Diego State University) |
| Abstract: | What role does noise play in equity markets? Answering this question usually leads immediately to specifying a model of fundamentals and hence the pervasive joint hypothesis quagmire. We avoid this dilemma by measuring noise volatility directly by focusing on the behavior of country closed-end funds (CCEF’s) during foreign (i.e., non-U.S.) holidays – for example, the last days of Ramadan in Islamic countries. These holiday periods are times when the flow of fundamental information relevant to foreign equity markets is substantially reduced and hence trading of CCEF’s in U.S. markets can be responding only weakly, if at all, to fundamental information. We find that, controlling for the effects of industry and global shocks and of the overall U.S. market, there remains a substantial amount of noise in the equity returns of U.S. CCEF’s. In the absence of noise, the noise ratio statistic would be near zero. However, our results indicate statistically significant departures from zero, with values averaged over all U.S. CCEF’s ranging from 76-84%xx depending on assumptions about the leakage of information during holiday periods and kurtosis. Noise is negatively related to institutional ownership of U.S. CCEF's and is much less important for U.K. CCEF's. The lower levels of noise for matched U.K. and U.S. CCEF’s provide some initial evidence that the U.K. securities transaction tax is effective in reducing stock market noise. |
| Date: | 2007–08 |
| URL: | http://d.repec.org/n?u=RePEc:sds:wpaper:0025&r=mst |
| By: | Nikolaus Hautsch; Dieter Hess; Christoph Müller |
| Abstract: | Bayesian learning provides a core concept of information processing in financial markets. Typically it is assumed that market participants perfectly know the quality of released news. However, in practice, news’ precision is rarely disclosed. Therefore, we extend standard Bayesian learning allowing traders to infer news’ precision from two different sources. If information is perceived to be imprecise, prices react stronger. Moreover, interactions of the different precision signals affect price responses nonlinearly. Empirical tests based on intra-day T-bond futures price reactions to employment releases confirm the model’s predictions and reveal statistically and economically significant effects of news’ precision. |
| Keywords: | Bayesian learning, information quality, precision signals, macroeconomic announcements |
| JEL: | E44 G14 |
| Date: | 2008–03 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2008-025&r=mst |
| By: | Alexander Subbotin (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Panthéon-Sorbonne - Paris I, Higher School of Economics - State University) |
| Abstract: | We decompose volatility of a stock market index both in time and scale using wavelet filters and design a probabilistic indicator for valatilities, analogous to the Richter scale in geophysics. The peak-over-threshold method is used to fit the generalized Pareto probability distribution for the extreme values in the realized variances of wavelet coefficients. The indicator is computed for the daily Dow Jones Industrial Averages index data from 1986 to 2007 and for the intraday CAC 40 data from 1995 to 2006. The results are used for comparison and structural multi-resolution analysis of extreme events on the stock market and for the detection of financial crises. |
| Keywords: | Stock market, volatility, wavelets, multi-resolution analysis, financial crisis. |
| Date: | 2008–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:papers:halshs-00261514_v1&r=mst |