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on Risk Management |
| By: | Helyette Geman (School of Economics, Mathematics & Statistics, Birkbeck); Steve Ohana |
| Date: | 2006–10 |
| URL: | http://d.repec.org/n?u=RePEc:bbk:bbkefp:0610&r=rmg |
| By: | Robert Chirinko; Hisham Foad |
| 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% 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 suggest that the U.K. securities transaction tax is effective in reducing stock market noise. |
| Keywords: | equity market noise, inefficient markets, institutional ownership, securities transaction tax, closed-end funds |
| JEL: | G10 G14 G18 |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_1812&r=rmg |
| By: | Lucia Alessi; Matteo Barigozzi; Marco Capasso |
| Abstract: | We use the Generalized Dynamic Factor Model proposed by Forni et al. [2000] in order to study the dynamics of the rate of growth of output and investment. By using quarterly firm level data relative to hundreds of US firms for 20 years, we investigate the number and the features of the underlying forces leading economic growth: evidence suggests the main shock to be the same across sectors and for the economy as a whole, thus more likely a demand shock than a technology shock. Moreover, we disentangle the component of industrial dynamics which is due to economy-wide factors, the common component, from the component which relates to sectoral or firm-specific phenomena, the idiosyncratic component. We assess the relative importance of these two components at different frequencies and compare common components across sectors. Finally, we investigate the comovements of the common component of output and investment series both at firm level and at sectoral level. |
| Keywords: | Dynamic Factor Analysis, Business Cycle, Comovements |
| Date: | 2006–10–05 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2006/27&r=rmg |
| By: | O. Aspachs; C. Goodhart; M. Segoviano; D. Tsomocos; L. Zicchino |
| Abstract: | We propose a metric of financial stability that is a weighted average of the probability of default and the equity of each country. The weights are obtained in the VAR and must reflect that the welfare changes due to financial instability are produced primarily through changes of the probability of default and secondarily through changes of the equity value. The metric is based on the definition of financial instability suggested by Tsomocos (2003 a and b) and Goodhart, Sunirand and Tsomocos (2006). |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:sbs:wpsefe:2006fe09&r=rmg |
| By: | Stan du Plessis (Department of Economics, University of Stellenbosch) |
| Abstract: | This paper argues that the sometimes-conflicting results of a modern revisionist literature on data mining in econometrics reflect different approaches to solving the central problem of model uncertainty in a science of non-experimental data. The literature has entered an exciting phase with theoretical development, methodological reflection, considerable technological strides on the computing front and interesting empirical applications providing momentum for this branch of econometrics. The organising principle for this discussion of data mining is a philosophical spectrum that sorts the various econometric traditions according to their epistemological assumptions (about the underlying data-generating-process DGP) starting with nihilism at one end and reaching claims of encompassing the DGP at the other end; call it the DGP-spectrum. In the course of exploring this spectrum the reader will encounter various Bayesian, specific-to-general (S-G) as well general-to-specific (G-S) methods. To set the stage for this exploration the paper starts with a description of data mining, its potential risks and a short section on potential institutional safeguards to these problems. |
| Keywords: | Data mining, model selection, automated model selection, general to specific modelling, extreme bounds analysis, Bayesian model selection |
| JEL: | C11 C50 C51 C52 C87 |
| Date: | 2006 |
| URL: | http://d.repec.org/n?u=RePEc:sza:wpaper:wpapers29&r=rmg |