Refine
Year of publication
Document Type
- Working Paper (19)
Has Fulltext
- yes (19)
Is part of the Bibliography
- no (19)
Keywords
- Volatilität (19) (remove)
Institute
Der vorliegende Beitrag führt eine detaillierte empirische Untersuchung über die Rolle der amtlichen Kursmakler an der Frankfurter Wertpapierbörse durch. Der verwendete Datensatz erlaubt eine Analyse des Einflusses der Maklertätigkeit auf Liquidität und Volatilität sowie eine Beurteilung der Profitabilität der Eigengeschäfte.
Die Beteiligung der Makler am Präsenzhandel ist erheblich. Ihre Eigengeschäfte machen über 20% des Handelsvolumens zu gerechneten Kursen und über 40% des Handelsvolumens im variablen Handel aus. Für letzteren wird zudem dokumentiert, daß die Tätigkeit der Makler zu einer deutlichen Reduktion der Geld-Brief-Spannen beiträgt. Die letztendlich gezahlte effektive Spanne beträgt im Durchschnitt weniger als ein Drittel der Spanne, die sich aus dem Orderbuch ergibt.
Für den Handel zu gerechneten Kursen wird gezeigt, daß die Preisfeststellung durch die Makler zu einer Verringerung der Volatilität führt. Eine Beurteilung des Einflusses der Makler auf die Volatilität im fortlaufenden Handel scheitert daran, daß das hierfür teilweise verwendete Maß, die Stabilisierungsrate, nach unserer Einschätzung keine aussagekräftigen Resultate liefert.
Die Makler erzielten während unseres Untersuchungszeitraums im Durchschnitt keinen Gewinn aus ihren Eigengeschäften. Eine Zerlegung der Gewinne in zwei Komponenten zeigt, daß positive Spannengewinne im Aggregat nicht für entstehende Positionierungsverluste kompensieren können.
Insgesamt zeigt unsere Untersuchung, daß die Kursmakler an den deutschen Wertpapierbörsen einen Beitrag zur Sicherung der Marktqualität leisten. Die Konsequenzen dieser Resultate für die Organisation des Aktienhandels in Deutschland werden diskutiert.
Wir verwenden eine neue, auf der Burr-Verteilung basierende Spezifikation aus der Familie der Autoregressive Conditional Duration (ACD) Modelle zur ökonometrischen Analyse der Transaktionsintensitäten während der Börseneinführung (IPO) der Deutsche Telekom Aktie. In diesem Fallbeispiel wird die Leistungsfähigkeit des neu entwickelten Burr-ACD-Modells mit den Standardmodellen von Engle und Russell verglichen, die im Burr-ACD Modell als Spezialfälle enthalten sind. Wir diskutieren außerdem alternative Möglichkeiten, Intra- Tagessaisonalitäten der Handelsintensität in ACD Modellen zu berücksichtigen.
This paper examines empirically the question whether the presence of foreign banks and a liberal trade regime with regard to financial services can contribute to a stabilization of capital flows to emerging markets. Since foreign banks, so the argument goes, provide better information to foreign investors and increase transparency, the danger of herding is reduced. Previous findings by Kono and Schuknecht (1998) confirmed empirically that such an effect does exist. This study expands their data set with respect to the length of the time period and the number of countries. Contrary to Kono and Schuknecht, it is found that foreign bank penetration tends to rather increase the volatility of capital flows. The trade regime variables are not significant in explaining cross-country variations in the volatility of capital flows. This result does not change significantly when alternative measures of volatility are considered. This paper was presented at the conference ''Financial crisis in transition countries: recent lessons and problems yet to solve'' on 13-14 July 2000 at the Institute for Economic Research (IWH) in Halle, Germany.
Within a two step GARCH framework we estimate the time-varying spillover effects from European and US return innovations to 10 economic sectors within the euro area, the United States, and the United Kingdom. We use daily data from January 1988 - March 2002. At the beginning of our sample sectors in all three currency areas/blocks formed a quite homogeneous group exhibiting only minor sector-specific characteristics. However, over time sectors became more heterogeneous, that is the response to aggregate shocks increasingly varies across sectors. This provides evidence that sector-specific effects gained in importance. European industries show increased heterogeneity simultaneously with the start of the European Monetary Union, whereas in the US this trend started in the early 1990's. Information technology and non-cyclical services (including telecommunication services) became the most integrated sectors worldwide, which are most affected by aggregate European and US shocks. On the other hand, basic industries, non-cyclical consumer goods, resources, and utilities became less affected by aggregate shocks. Volatility spillovers proved to be small and volatile. JEL_Klassifikation: G1, F36
Forecasting stock market volatility and the informational efficiency of the DAX-index options market
(2002)
Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility. Employing German DAX-index return data it is found that past returns do not contain useful information beyond the volatility expectations already reflected in option prices. This supports the efficient market hypothesis for the DAX-index options market.
Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQ-index data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics have a clearly distinct behavior that is, for example, compatible with the well-known leverage effect. Klassifikation: C22, C51, G10
A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from high-frequency returns coupled with relatively simple reduced-form time series modeling procedures. Building on recent theoretical results from Barndorff-Nielsen and Shephard (2003c,d) for related bi-power variation measures involving the sum of high-frequency absolute returns, the present paper provides a practical framework for non-parametrically measuring the jump component in realized volatility measurements. Exploiting these ideas for a decade of high-frequency five-minute returns for the DM/$ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find the jump component of the price process to be distinctly less persistent than the continuous sample path component. Explicitly including the jump measure as an additional explanatory variable in an easy-to-implement reduced form model for realized volatility results in highly significant jump coefficient estimates at the daily, weekly and quarterly forecast horizons. As such, our results hold promise for improved financial asset allocation, risk management, and derivatives pricing, by separate modeling, forecasting and pricing of the continuous and jump components of total return variability.
We consider three sets of phenomena that feature prominently - and separately - in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) in asset return signs, and dependence (and hence forecastability) in asset return volatilities. We show that they are very much interrelated, and we explore the relationships in detail. Among other things, we show that: (a) Volatility dependence produces sign dependence, so long as expected returns are nonzero, so that one should expect sign dependence, given the overwhelming evidence of volatility dependence; (b) The standard finding of little or no conditional mean dependence is entirely consistent with a significant degree of sign dependence and volatility dependence; (c) Sign dependence is not likely to be found via analysis of sign autocorrelations, runs tests, or traditional market timing tests, because of the special nonlinear nature of sign dependence; (d) Sign dependence is not likely to be found in very high-frequency (e.g., daily) or very low-frequency (e.g., annual) returns; instead, it is more likely to be found at intermediate return horizons; (e) Sign dependence is very much present in actual U.S. equity returns, and its properties match closely our theoretical predictions; (f) The link between volatility forecastability and sign forecastability remains intact in conditionally non-Gaussian environments, as for example with time-varying conditional skewness and/or kurtosis.