Testing the Bivariate Mixture Hypothesis Using German Stock Market Data
Testing the Bivariate Mixture Hypothesis Using German Stock Market Data

Author: Robert Jung

Publisher:

Published: 1998

Total Pages:

ISBN-13:

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According to the bivariate mixture hypothesis (BMH) as proposed by Tauchen and Pitts (1983) and Harris (1986, 1987) the daily price changes and the corresponding trading volume on speculative markets follow a joint mixture of distributions with the unobservable number of daily information events serving as the mixing variable. Using German stock market data of 15 major companies the distributional properties of the BMH is tested employing maximum-likelihood as well as generalized method of moments estimation techniques. In addition to providing a new approach for the pointwise estimation of the latent information arrival rate based on the maximum-likelihood method, we investigate the time-series properties of the BMH. The major results can be summarized as follows: (i) the distributional characteristics of the data (especially leptokurtosis and skewness in the distribution of price changes and volume respectively) cannot be explained satisfactorily by the BMH; univariate mixture models for price changes and trading volume separately reveal a possible specification error in the model; (ii) a univariate normal mixture model can account for the observed distributional characteristics of price changes; (iii) the estimated process of the latent information rate cannot fully explain the time-series characteristics of the data (especially the volatility clustering or ARCH-effects).

Modelling Irregularly Spaced Financial Data
Modelling Irregularly Spaced Financial Data

Author: Nikolaus Hautsch

Publisher: Springer Science & Business Media

Published: 2011-01-07

Total Pages: 297

ISBN-13: 3642170153

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This book provides a methodological framework to model univariate and multivariate irregularly spaced financial data. It gives a thorough review of recent developments in the econometric literature, puts forward existing approaches and opens up new directions. The book presents alternative ways to model so-called financial point processes using dynamic duration as well as intensity models and discusses their ability to account for specific features of point process data, like the occurrence of time-varying covariates, censoring mechanisms and multivariate structures. Moreover, it illustrates the use of various types of financial point processes to model financial market activity from different viewpoints and to construct volatility and liquidity measures under explicit consideration of the passing trading time.

There's More to Volatility than Volume
There's More to Volatility than Volume

Author: Laszlo Gillemot

Publisher:

Published: 2005

Total Pages: 26

ISBN-13:

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It is widely believed that fluctuations in transaction volume, as reflected in the number of transactions and to a lesser extent their size, are the main cause of clustered volatility. Under this view bursts of rapid or slow price diffusion reflect bursts of frequent or less frequent trading, which cause both clustered volatility and heavy tails in price returns. We investigate this hypothesis using tick by tick data from the New York and London Stock Exchanges and show that only a small fraction of volatility fluctuations are explained in this manner. Clustered volatility is still very strong even if price changes are recorded on intervals in which the total transaction volume or number of transactions is held constant. In addition the distribution of price returns conditioned on volume or transaction frequency being held constant is similar to that in real time, making it clear that neither of these are the principal cause of heavy tails in price returns. We analyze recent results of Ane and Geman (2000) and Gabaix et al. (2003), and discuss the reasons why their conclusions differ from ours. Based on a cross-sectional analysis we show that the long-memory of volatility is dominated by factors other than transaction frequency or total trading volume.

Handbook of Quantitative Finance and Risk Management
Handbook of Quantitative Finance and Risk Management

Author: Cheng-Few Lee

Publisher: Springer Science & Business Media

Published: 2010-06-14

Total Pages: 1700

ISBN-13: 0387771174

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Quantitative finance is a combination of economics, accounting, statistics, econometrics, mathematics, stochastic process, and computer science and technology. Increasingly, the tools of financial analysis are being applied to assess, monitor, and mitigate risk, especially in the context of globalization, market volatility, and economic crisis. This two-volume handbook, comprised of over 100 chapters, is the most comprehensive resource in the field to date, integrating the most current theory, methodology, policy, and practical applications. Showcasing contributions from an international array of experts, the Handbook of Quantitative Finance and Risk Management is unparalleled in the breadth and depth of its coverage. Volume 1 presents an overview of quantitative finance and risk management research, covering the essential theories, policies, and empirical methodologies used in the field. Chapters provide in-depth discussion of portfolio theory and investment analysis. Volume 2 covers options and option pricing theory and risk management. Volume 3 presents a wide variety of models and analytical tools. Throughout, the handbook offers illustrative case examples, worked equations, and extensive references; additional features include chapter abstracts, keywords, and author and subject indices. From "arbitrage" to "yield spreads," the Handbook of Quantitative Finance and Risk Management will serve as an essential resource for academics, educators, students, policymakers, and practitioners.

Stochastic Volatility, Trading Volume, and the Daily Flow of Information
Stochastic Volatility, Trading Volume, and the Daily Flow of Information

Author: Jeff Fleming

Publisher:

Published: 2009

Total Pages: 39

ISBN-13:

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We use state-space methods to investigate the relation between volume, volatility, and ARCH effects within a Mixture-of-Distributions Hypothesis (MDH) framework. In most recent studies of the MDH, the information flow or its logarithm is modeled as an AR(1) process. We argue that this is too restrictive because it requires the information flow to be highly persistent. Using a more general process, we find evidence of a large nonpersistent component of return volatility that is closely related to the contemporaneous nonpersistent component of trading volume. However, unlike previous studies that fit volume-augmented GARCH models, we find no evidence that trading volume subsumes ARCH effects. Because volume-augmented GARCH models are subject to simultaneity bias, our findings should be more robust than these prior results.