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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
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.
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.
This paper provides an in-depth analysis of the properties of popular tests for the existence and the sign of the market price of volatility risk. These tests are frequently based on the fact that for some option pricing models under continuous hedging the sign of the market price of volatility risk coincides with the sign of the mean hedging error. Empirically, however, these tests suffer from both discretization error and model mis-specification. We show that these two problems may cause the test to be either no longer able to detect additional priced risk factors or to be unable to identify the sign of their market prices of risk correctly. Our analysis is performed for the model of Black and Scholes (1973) (BS) and the stochastic volatility (SV) model of Heston (1993). In the model of BS, the expected hedging error for a discrete hedge is positive, leading to the wrong conclusion that the stock is not the only priced risk factor. In the model of Heston, the expected hedging error for a hedge in discrete time is positive when the true market price of volatility risk is zero, leading to the wrong conclusion that the market price of volatility risk is positive. If we further introduce model mis-specification by using the BS delta in a Heston world we find that the mean hedging error also depends on the slope of the implied volatility curve and on the equity risk premium. Under parameter scenarios which are similar to those reported in many empirical studies the test statistics tend to be biased upwards. The test often does not detect negative volatility risk premia, or it signals a positive risk premium when it is truly zero. The properties of this test furthermore strongly depend on the location of current volatility relative to its long-term mean, and on the degree of moneyness of the option. As a consequence tests reported in the literature may suffer from the problem that in a time-series framework the researcher cannot draw the hedging errors from the same distribution repeatedly. This implies that there is no guarantee that the empirically computed t-statistic has the assumed distribution. JEL: G12, G13 Keywords: Stochastic Volatility, Volatility Risk Premium, Discretization Error, Model Error
Tests for the existence and the sign of the volatility risk premium are often based on expected option hedging errors. When the hedge is performed under the ideal conditions of continuous trading and correct model specification, the sign of the premium is the same as the sign of the mean hedging error for a large class of stochastic volatility option pricing models. We show, however, that the problems of discrete trading and model mis-specification, which are necessarily present in any empirical study, may cause the standard test to yield unreliable results.
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.
We selectively survey, unify and extend the literature on realized volatility of financial asset returns. Rather than focusing exclusively on characterizing the properties of realized volatility, we progress by examining economically interesting functions of realized volatility, namely realized betas for equity portfolios, relating them both to their underlying realized variance and covariance parts and to underlying macroeconomic fundamentals.
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.
Volatility forecasting
(2005)
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1.