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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
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
We examine intra-day market reactions to news in stock-specific sentiment disclosures. Using pre-processed data from an automated news analytics tool based on linguistic pattern recognition we extract information on the relevance as well as the direction of company-specific news. Information-implied reactions in returns, volatility as well as liquidity demand and supply are quantified by a high-frequency VAR model using 20 second intervals. Analyzing a cross-section of stocks traded at the London Stock Exchange (LSE), we find market-wide robust news-dependent responses in volatility and trading volume. However, this is only true if news items are classified as highly relevant. Liquidity supply reacts less distinctly due to a stronger influence of idiosyncratic noise. Furthermore, evidence for abnormal highfrequency returns after news in sentiments is shown. JEL-Classification: G14, C32
We propose a new approach to measuring the effect of unobservable private information or beliefs on volatility. Using high-frequency intraday data, we estimate the volatility effect of a well identified shock on the volatility of the stock returns of large European banks as a function of the quality of available public information about the banks. We hypothesise that, as the publicly available information becomes stale, volatility effects and its persistence should increase, as the private information (beliefs) of investors becomes more important. We find strong support for this idea in the data. We argue that the results have implications for debate surrounding the opacity of banks and the transparency requirements that may be imposed on banks under Pillar III of the New Basel Accord.
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.
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.
Measuring financial asset return and volatilty spillovers, with application to global equity markets
(2008)
We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of return spillovers and volatility spillovers. Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers, and both turn out to be empirically important. In particular, in an analysis of nineteen global equity markets from the early 1990s to the present, we find striking evidence of divergent behavior in the dynamics of return spillovers vs. volatility spillovers: Return spillovers display a gently increasing trend but no bursts, whereas volatility spillovers display no trend but clear bursts.
We provide a simple and intuitive measure of interdependence of asset returns and/or volatilities. In particular, we formulate and examine precise and separate measures of return spillovers and volatility spillovers. Our framework facilitates study of both non-crisis and crisis episodes, including trends and bursts in spillovers, and both turn out to be empirically important. In particular, in an analysis of sixteen global equity markets from the early 1990s to the present, we find striking evidence of divergent behavior in the dynamics of return spillovers vs. volatility spillovers: Return spillovers display a gently increasing trend but no bursts, whereas volatility spillovers display no trend but clear bursts. JEL Classification: F30, G15, F36
Using unobservable conditional variance as measure, latent-variable approaches, such as GARCH and stochastic-volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing "observable" or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA models, have become a promising strategy for modeling and predicting (daily) volatility. In this paper, we show that the residuals of the commonly used time-series models for realized volatility exhibit non-Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility. In an empirical application for S&P500 index futures we show that allowing for time-varying volatility of realized volatility leads to a substantial improvement of the model's fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting. Klassifikation: C22, C51, C52, C53