WorldCat Identities

Haas, Markus

Overview
Works: 17 works in 35 publications in 1 language and 148 library holdings
Roles: Author, dgs, Other
Publication Timeline
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Most widely held works by Markus Haas
Mixed normal conditional heteroskedasticity by Markus Haas( )

5 editions published between 2002 and 2010 in English and Undetermined and held by 21 WorldCat member libraries worldwide

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
Empirical investigations of current monetary and fiscal policy issues by Sebastian Christoph Müller( Book )

3 editions published in 2017 in English and held by 18 WorldCat member libraries worldwide

Multivariate regimeswitching GARCH with an application to international stock markets by Markus Haas( )

4 editions published in 2008 in English and Undetermined and held by 17 WorldCat member libraries worldwide

We develop a multivariate generalization of the Markov-switching GARCH model introduced by Haas, Mittnik, and Paolella (2004b) and derive its fourth-moment structure. An application to international stock markets illustrates the relevance of accounting for volatility regimes from both a statistical and economic perspective, including out-of-sample portfolio selection and computation of Value-at-Risk
Multivariate normal mixture GARCH( )

2 editions published in 2006 in English and held by 14 WorldCat member libraries worldwide

Assessing Central Bank credibility during the ERM crises comparing option and spot market-based forecasts( )

2 editions published in 2005 in English and held by 14 WorldCat member libraries worldwide

Dynamic mixture models for financial time series by Markus Haas( Book )

1 edition published in 2004 in English and held by 14 WorldCat member libraries worldwide

Inaugural -Dissertation zur Erlangung des Grades eines Doktors der Wirtschafts -und Sozialwissenschaften der Wirtschafts -und Sozialwissenschaftlichen Fakultät der Christian -Albrechts -Universität zu Kiel The objective of this study is the development and application of models for financial time series to which normal mixture distributions are central. The use of mixed normal distributions for modeling the returns of financial assets is appealing because it maintains the assumption of conditionally normally distributed asset returns, yet can still adequately capture often observed "stylized facts" of both conditional and unconditional return distributions, in particular, fat-tailedness and asymmetries. The main part of this study is devoted to the development and investigation of univariate dynamic mixture models for financial time series, namely, mixed normal and Markov-switching GARCH models. In the second part of the study, we also consider multivariate problems, such as portfolio choice when the asset returns under study have a multivariate normal mixture distribution
Modeling and predicting market risk with Laplace-Gaussian mixture distributions( )

2 editions published in 2005 in English and held by 14 WorldCat member libraries worldwide

Asymmetric multivariate normal mixture GARCH( )

2 editions published in 2008 in English and held by 14 WorldCat member libraries worldwide

Assessing Central Bank Credibility During the Erm Crises Comparing Option and Spot Market-Based Forecasts by Markus Haas( )

3 editions published between 2004 and 2007 in English and Undetermined and held by 4 WorldCat member libraries worldwide

Financial markets embed expectations of central bank policy into asset prices. This paper compares two approaches that extract a probability density of market beliefs. The first is a simulated moments estimator for option volatilities described in Mizrach (2002); the second is a new approach developed by Haas, Mittnik and Paolella (2004a) for fat-tailed conditionally heteroskedastic time series. We find, in an application to the ERM crises of 1992-93, that both the options and the underlying exchange rates provide useful information for policy makers
Multivariate normal mixture GARCH by Markus Haas( )

2 editions published in 2006 in Undetermined and English and held by 3 WorldCat member libraries worldwide

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 improve upon the existing 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
Modeling and Predicting Market Risk with Laplace-Gaussian Mixture Distributions by Markus Haas( )

2 editions published in 2005 in Undetermined and English and held by 3 WorldCat member libraries worldwide

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
Asymmetric Multivariate Normal Mixture GARCH by Markus Haas( )

2 editions published in 2008 in Undetermined and English and held by 3 WorldCat member libraries worldwide

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 out-of-sample Value-at-Risk measures
Essays in Empirical Dynamic Asset Pricing: Methods and Applications in Foreign Exchange by Dennis Umlandt( )

1 edition published in 2020 in English and held by 3 WorldCat member libraries worldwide

Essays on Empirical Asset Pricing by Alexandra Aurelia Koehl( )

1 edition published in 2019 in English and held by 3 WorldCat member libraries worldwide

Stable mixture GARCH models( )

1 edition published in 2011 in English and held by 1 WorldCat member library worldwide

A new model class for univariate asset returns is proposed which involves the use of mixtures of stable Paretian distributions, and readily lends itself to use in a multivariate context for portfolio selection. The model nests numerous ones currently in use, and is shown to outperform all its special cases. In particular, an extensive out-of-sample risk forecasting exercise for seven major FX and equity indices confirms the superiority of the general model compared to its special cases and other competitors. An improved method (in terms of speed and accuracy) is developed for the computation of the stable Paretian density. Estimation issues related to problems associated with mixture models are discussed, and a new, general, method is proposed to successfully circumvent these. The model is straightforwardly extended to the multivariate setting by using an independent component analysis framework. The tractability of the relevant characteristic function then facilitates portfolio optimization using expected shortfall as the downside risk measure. Density Forecasting, Expected Shortfall, Fat Tails, ICA, GARCH, Mixtures, Portfolio Selection, Stable Paretian Distribution, Value-at-Risk
Mixed normal conditional heteroskedasticity by Markus HAAS( )

1 edition published in 2004 in English and held by 1 WorldCat member library worldwide

Time-varying mixture GARCH models and asymmetric volatility( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

 
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Audience Level
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  General Special  
Audience level: 0.92 (from 0.90 for Mixed norm ... to 0.99 for Mixed norm ...)

Languages
English (29)