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on Market Microstructure |
| By: | Numan Ülkü (Central European University Business School); Enzo Weber (University of Regensburg, Osteuropa-Institut, Regensburg (Institut for East European Studies)) |
| Abstract: | This paper provides the first study of foreign investors’ trading in a sizeable European emerging stock market, using a combination of daily and monthly complete data col-lected at the destination. It also introduces the structural conditional correlation (SCC) methodology to identify the contemporaneous interaction between foreign flows and returns. We show that global emerging market returns are an additional driver of foreign flows after controlling for global developed market returns. Foreigners do negative (positive)-feedback-trade with respect to local returns at the monthly (daily) frequency. SCC methodology shows that the standard assumption in the literature, that flows cause returns contemporaneously but not vice versa, is not justified, even at the daily fre-quency, making price impact estimates reported in previous literature questionable |
| Date: | 2011–01 |
| URL: | http://d.repec.org/n?u=RePEc:ost:wpaper:294&r=mst |
| By: | Taufemback, Cleiton; Da Silva, Sergio |
| Abstract: | Applied econometricians tend to show a long neglect for the proper frequency to be considered while sampling the time series data. The present study shows how spectral analysis can be usefully employed to fix this problem. The case is illustrated with ultra-high-frequency data and daily prices of four selected stocks listed on the Sao Paulo stock exchange. |
| Keywords: | Econophysics; Spectral analysis; Aliasing; Sampling; Financial time series |
| JEL: | C81 |
| Date: | 2011 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:28720&r=mst |
| By: | Cecilia Frale (MEF-Ministry of the Economy and Finance-Italy, Treasury Department); Libero Monteforte (Bank of Italy and MEF-Ministry of the Economy and Finance-Italy, Treasury Department) |
| Abstract: | In this paper a dynamic factor model with mixed frequency is proposed (FaMIDAS), where the past observations of high frequency indicators are used following the MIDAS approach. This structure is able to represent with richer dynamics the information content of the economic indicators and produces smoothed factors and forecasts. In addition, the Kalman filter is applied, which is particularly suited for dealing with unbalanced data set and revisions in the preliminary data. In the empirical application for the Italian quarterly GDP the short-term forecasting performance is evaluated against other mixed frequency models in a pseudo-real time experiment, also allowing for pooled forecast from factor models. |
| Keywords: | mixed frequency models, dynamic factor models, MIDAS,forecasting. |
| JEL: | E32 E37 C53 |
| Date: | 2011–01 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_788_11&r=mst |
| By: | Michael C. M\"unnix; Rudi Sch\"afer |
| Abstract: | We analyze the statistical dependency structure of the S&P 500 constituents in the 4-year period from 2007 to 2010 using intraday data from the New York Stock Exchange's TAQ database. With a copula-based approach, we find that the statistical dependencies are very strong in the tails of the marginal distributions. This tail dependence is higher than in a bivariate Gaussian distribution, which is implied in the calculation of many correlation coefficients. We compare the tail dependence to the market's average correlation level as a commonly used quantity and disclose an neraly linear relation. |
| Date: | 2011–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1102.1099&r=mst |