2,449 research outputs found
Magnetic Field Induced Ordering in Quasi-One-Dimensional Quantum Magnets
Three-dimensional magnetic ordering transitions are studied theoretically in
strongly anisotropic quantum magnets. An external magnetic field can drive
quasi-one-dimensional subsystems with a spin gap into a gapless regime, thus
inducing long-range three-dimensional magnetic ordering due to weak residual
magnetic coupling between the subsystems. Compounds with higher spin degrees of
freedom, such as N-leg spin-1/2 ladders, are shown to have cascades of ordering
transitions. At high magnetic fields, zero-point fluctuations within the
quasi-1D subsystems are suppressed, causing quantum corrections to the ordering
temperature to be reduced.Comment: RevTex, 12 pages with 4 figure
Multivariate regimeâswitching GARCH with an application to international stock markets
We develop a multivariate generalization of the Markovâswitching GARCH model introduced by Haas, Mittnik, and Paolella (2004b) and derive its fourthâmoment structure. An application to international stock markets illustrates the relevance of accounting for volatility regimes from both a statistical and economic perspective, including outâofâsample portfolio selection and computation of ValueâatâRisk
Multivariate RegimeâSwitching GARCH with an Application to International Stock Markets
We develop a multivariate generalization of the Markovâswitching GARCH model introduced by Haas, Mittnik, and Paolella (2004b) and derive its fourthâmoment structure. An application to international stock markets illustrates the relevance of accounting for volatility regimes from both a statistical and economic perspective, including outâofâsample portfolio selection and computation of ValueâatâRisk.Conditional Volatility, MarkovâSwitching, Multivariate GARCH
A concept for Lithography-free patterning of silicon heterojunction back-contacted solar cells by laser processing
Silicon heterojunction (SHJ) solar cells with an interdigitated back-contact
(IBC) exhibit high conversion efficiencies of up to 25.6%. However, due to the
sophisticated back-side pattern of the doped layers and electrode structure
many processing and patterning steps are required. A simplification of the
patterning steps could ideally increase the yield and/or lower the production
costs. We propose a patterning approach for IBC SHJ solar cells free of any
photo-lithography with the help of laser-induced forward transfer (LIFT) of the
individual layer stacks to create the required back-contact pattern. The
concept has the potential to lower the number of processing steps significantly
while at the same time giving a large degree of freedom in the processing
conditions optimization of emitter and BSF since deposition of the
intrinsic/doped layers and processing of the wafer are all independent from
each other.Comment: 6 pages, 3 figures, 1 tabl
Mixed normal conditional heteroskedasticity
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, G1
Asymmetric multivariate normal mixture GARCH
An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic correlation structure of the process are provided. In an application to stock market returns, it is shown that the disaggregation of the conditional (co)variance process generated by the model provides substantial intuition. Moreover, the model exhibits a strong performance in calculating outâofâsample ValueâatâRisk measures
Multivariate normal mixture GARCH
We present a multivariate generalization of the mixed normal GARCH model proposed in Haas, Mittnik, and Paolella (2004a). Issues of parametrization and estimation are discussed. We derive conditions for covariance stationarity and the existence of the fourth moment, and provide expressions for the dynamic correlation structure of the process. These results are also applicable to the single-component multivariate GARCH(p, q) model and simplify the results existing in the literature. In an application to stock returns, we show that the disaggregation of the conditional (co)variance process generated by our model provides substantial intuition, and we highlight a number of findings with potential significance for portfolio selection and further financial applications, such as regime-dependent correlation structures and leverage effects. Klassifikation: C32, C51, G10, G11Die vorliegende Arbeit ist einer multivariaten Verallgemeinerung des sog. Normal Mixture GARCH Modells gewidmet, dessen univariate Variante von Haas, Mittnik und Paolella (2004a, siehe auch CFS Working Paper 2002/10) vorgeschlagen wurde. Dieses Modell unterscheidet sich von traditionellen GARCH-Ansätzen insbesondere dadurch, dass es eine Abhängigkeit der Risikoentwicklung von - typischerweise unbeobachtbaren - Marktzuständen explizit in Rechnung stellt. Dies wird durch die Beobachtung motiviert, dass das weit verbreitete GARCH Modell in seiner Standardvariante auch dann keine adäquate Beschreibung der Risikodynamik leistet, wenn die Normalverteilung durch flexiblere bedingte Verteilungen ersetzt wird. Zustandsabhängige Volatilitätsprozesse kĂśnnen etwa durch die variierende Dominanz heterogener Marktteilnehmer oder durch wechselnde Marktstimmungen Ăśkonomisch zu erklären sein. Anwendungen des Normal Mixture GARCH Modells auf zahlreiche Aktien- und Wechselkurszeitreihen (siehe z.B. Alexander und Lazar, 2004, 2005; und Haas, Mittnik und Paolella, 2004a,b) haben gezeigt, dass es sich zur Modellierung und Prognose des Volatilitätsprozesses der Renditen solcher Aktiva hervorragend eignet. Indes beschränken sich diese Analysen bisher auf die Untersuchung univariater Zeitreihen. Zahlreiche Probleme der Finanzwirtschaft erfordern jedoch zwingend eine multivariate Modellierung, mithin also eine Beschreibung der Abhängigkeitsstruktur zwischen den Renditen verschiedener Wertpapiere. Insbesondere fĂźr solche Analysen erweist sich der Mischungsansatz aber als besonders vielversprechend. So spielen etwa im Portfoliomanagement die Korrelationen zwischen einzelnen Wertpapierrenditen eine herausragende Rolle. Die Stärke der Korrelationen ist von entscheidender Bedeutung dafĂźr, in welchem AusmaĂ das Risiko eines effizienten Portfolios durch Diversifikation reduziert werden kann. Nun gibt es empirische Hinweise darauf, dass die Korrelationen etwa zwischen Aktien in Perioden, die durch starke Marktschwankungen und tendenziell fallende Kurse charakterisiert sind, stärker sind als in ruhigeren Perioden. Das bedeutet, dass die Vorteile der Diversifikation in genau jenen Perioden geringer sind, in denen ihr Nutzen am grĂśĂten wäre. Modelle, die die Existenz unterschiedlicher Marktregime nicht berĂźcksichtigen, werden daher dazu tendieren, die Korrelationen in den adversen Marktzuständen zu unterschätzen. Dies kann zu erheblichen Fehleinschätzungen des tatsächlichen Risikos während solcher Perioden fĂźhren. Diese und weitere Implikationen des Mischungsansatzes im Kontext multivariater GARCH Modelle werden in der vorliegenden Arbeit diskutiert, und ihre Relevanz wird anhand einer empirischen Anwendung dokumentiert. ErĂśrtert werden ferner Fragen der Parametrisierung und Schätzung des Modells, und einige relevante theoretische Eigenschaften werden hergeleitet
Modeling and predicting market risk with Laplace-Gaussian mixture distributions
While much of classical statistical analysis is based on Gaussian distributional assumptions, statistical modeling with the Laplace distribution has gained importance in many applied fields. This phenomenon is rooted in the fact that, like the Gaussian, the Laplace distribution has many attractive properties. This paper investigates two methods of combining them and their use in modeling and predicting financial risk. Based on 25 daily stock return series, the empirical results indicate that the new models offer a plausible description of the data. They are also shown to be competitive with, or superior to, use of the hyperbolic distribution, which has gained some popularity in asset-return modeling and, in fact, also nests the Gaussian and Laplace. Klassifikation: C16, C50 . March 2005
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