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The Stochastic Conditional Duration Model
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Book Synopsis The Stochastic Conditional Duration Model by : Luc Bauwens
Download or read book The Stochastic Conditional Duration Model written by Luc Bauwens and published by . This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a class of models for the analysis of durations, which we call stochastic conditional duration (SCD) models. These models are based on the assumption that the durations are generated by a dynamic stochastic latent variable. The model yields a wide range of shapes of hazard functions. The estimation of the parameters is performed by quasi-maximum likelihood and using the Kalman filter. The model is applied to trade, price and volume durations of stocks traded at NYSE. We also investigate the relation between price durations, spread, trade intensity and volume.
Book Synopsis Stochastic Conditional Duration Model with a Mixture-of-Normal Error Distribution by : Dinghai Xu
Download or read book Stochastic Conditional Duration Model with a Mixture-of-Normal Error Distribution written by Dinghai Xu and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper provides theoretical properties and Monte-Carlo studies of a stochastic conditional duration model with mixture-of-normal error distributions an effcient estimation approach via a continuous empirical characteristic function. The empirical version of this paper is studied in Xu, Knight, and Wirjanto (2011). The proposed model is shown to be capable of capturing various density shapes of the expected duration of financial trades as well as accommodating various types of dependence structure between the error processes. Detailed Monte-Carlo results are provided to assess the performance of the proposed model and estimation approach.
Book Synopsis Threshold Stochastic Conditional Duration Model for Transaction Data by : Tony S. Wirjanto
Download or read book Threshold Stochastic Conditional Duration Model for Transaction Data written by Tony S. Wirjanto and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a threshold stochastic conditional duration (SCD) model for financial data at the transaction level. In addition to assuming that the innovations of the duration process follow a threshold distribution with positive support, we also assume that the latent first-order autoregressive process of the log conditional durations switches between two regimes. The regimes are determined by the levels of the observed durations and the threshold SCD model is specified to be self-excited. Markov Chain Monte Carlo methods within a Bayesian framework are then developed for parameter estimation. For model comparison, we employ a deviance information criteria, which does not depend on the number of model parameters directly. Duration forecasting is constructed by using an auxiliary particle filter based on the fitted models. Simulation studies demonstrate that our proposed model and estimation approach work well in terms of parameter estimation and duration forecasting. Lastly the proposed models and estimation approach are applied to two benchmark data sets that have been studied in the literature, namely IBM and Boeing transaction data.
Book Synopsis Asymmetric Stochastic Conditional Duration Model :a Mixture of Normals Approach by : Dinghai Xu
Download or read book Asymmetric Stochastic Conditional Duration Model :a Mixture of Normals Approach written by Dinghai Xu and published by . This book was released on 2008 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Analysis of the Stochastic Conditional Duration Model by : Chris M. Strickland
Download or read book Bayesian Analysis of the Stochastic Conditional Duration Model written by Chris M. Strickland and published by . This book was released on 2003 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymmetric Stochastic Conditional Duration Model -- A Mixture-of-Normal Approach by : Dinghai Xu
Download or read book Asymmetric Stochastic Conditional Duration Model -- A Mixture-of-Normal Approach written by Dinghai Xu and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper extends the stochastic conditional duration model first proposed by Bauwens and Veredas (2004) by imposing mixtures of bivariate normal distributions on the innovations of the observation and latent equations of the duration process. This extension allows the model not only to capture various density shapes of the durations but also to easily accommodate a richer dependence structure between the two innovations. In addition, it applies an estimation methodology based on the empirical characteristic function. Empirical applications based on the IBM and Boeing transaction data are provided to assess and illustrate the performance of the proposed model and the estimation method. One interesting empirical finding in this paper is that there is a significantly positive correlation under both the contemporaneous and lagged intertemporal dependence structures for the IBM and Boeing duration data.
Book Synopsis Stochastic Conditional Duration Models with Mixture Processes by : Tony S. Wirjanto
Download or read book Stochastic Conditional Duration Models with Mixture Processes written by Tony S. Wirjanto and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies stochastic conditional duration models with a mixture of distribution processes for financial asset's transaction data. The mixture component distributions include exponential, gamma and Weibull. The models allow for a correlation between the observed durations and the logarithm of the conditional expected durations. Suitable MCMC algorithms are developed for Bayesian inference of parameters and duration forecasting of the models. Unlike much of the existing studies in this literature, simulation studies and empirical applications suggest that the proposed models and method are able to t the left tail of the marginal distribution of duration time series relatively well.
Book Synopsis A Threshold Stochastic Conditional Duration Model for Financial Transaction Data by : Zhongxian Men
Download or read book A Threshold Stochastic Conditional Duration Model for Financial Transaction Data written by Zhongxian Men and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a threshold stochastic conditional duration (TSCD) model to capture the asymmetric property of financial transactions. The innovation of the observable duration equation is assumed to follow a threshold distribution with two component distributions switching between two regimes. The distributions in different regimes are assumed to be Exponential, Gamma or Weibull. To account for uncertainty in the unobserved threshold level, the observed durations are treated as self-exciting threshold variables. Adopting a Bayesian approach, we develop novel Markov Chain Monte Carlo algorithms to estimate all of the unknown parameters and latent states. To forecast the one-step ahead durations, we employ an auxiliary particle filter where the filter and prediction distributions of the latent states are approximated. The proposed model and the developed MCMC algorithms are illustrated by using both simulated and actual financial transaction data. For model selection, a Bayesian deviance information criterion is calculated to compare our model with other competing models in the literature. Overall, we find that the threshold SCD model performs better than the SCD model when a single positive distribution is assumed for the innovation of the duration equation.
Book Synopsis Bayesian Inference of Asymmetric Stochastic Conditional Duration Models by : Zhongxian Men
Download or read book Bayesian Inference of Asymmetric Stochastic Conditional Duration Models written by Zhongxian Men and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper extends a stochastic conditional duration (SCD) model for financial transaction data to allow for correlation between error processes or innovations of observed duration process and latent log duration process with the aim of improving the statistical fit of the model. Suitable algorithms of Markov Chain Monte Carlo (MCMC) are developed to t the resulting SCD model under various distributional assumptions about the innovation of the measurement equation. Unlike the estimation methods commonly used to estimate the SCD model in the literature, we work with the original specification of the model, without subjecting the observation equation to a logarithmic transformation. Results of simulation studies suggest that our proposed model and corresponding estimation methodology perform quite well. We also apply an auxiliary particle filter technique to construct one-step-ahead in-sample and out-of-sample duration forecasts of the fitted models. Applications to the IBM transaction data allows comparison of our model and method to those existing in the literature.
Book Synopsis Forecasting Transaction Rates by : Robert F. Engle
Download or read book Forecasting Transaction Rates written by Robert F. Engle and published by . This book was released on 1994 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper will propose a new statistical model for the analysis of data that does not arrive in equal time intervals such as financial transactions data, telephone calls, or sales data on commodities that are tracked electronically. In contrast to fixed interval analysis, the model treats the time between observation arrivals as a stochastic time varying process and therefore is in the spirit of the models of time deformation initially proposed by Tauchen and Pitts (1983), Clark (1973) and more recently discussed by Stock (1988), Lamoureux and Lastrapes (1992), Muller et al. (1990) and Ghysels and Jasiak (1994) but does not require auxiliary data or assumptions on the causes of time flow. Strong evidence is provided for duration clustering beyond a deterministic component for the financial transactions data analyzed. We will show that a very simple version of the model can successfully account for the significant autocorrelations in the observed durations between trades of IBM stock on the consolidated market. A simple transformation of the duration data allows us to include volume in the model.
Book Synopsis Bayesian Inference of Multiscale Stochastic Conditional Duration Models by : Tony S. Wirjanto
Download or read book Bayesian Inference of Multiscale Stochastic Conditional Duration Models written by Tony S. Wirjanto and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we revisit the notion that a single factor of duration running on single time scale is adequate to capture the dynamics of the duration process of financial transaction data. The documented poor fit of the left tail of the marginal distribution of the observed durations in some existing one-factor stochastic duration models may be indicative of the possible existence of multiple stochastic duration factors running on different time scales. This paper proposes multiscale stochastic conditional duration (MSCD) models to describe the dynamics of duration of financial transaction data. Suitable algorithms of MCMC are developed to fit the resulting MSCD models under three distributional assumptions about the innovation of the measurement equation. Simulation studies suggest that our proposed models and methods result in improved in-sample fits as well as improved duration forecasts. Applications of our models and methods to two duration data sets of FIAT and IBM indicate the existence of at least two factors governing the dynamics of the duration of the stock transactions.
Book Synopsis Stochastic Conditional Intensity Processes by : Nikolaus Hautsch
Download or read book Stochastic Conditional Intensity Processes written by Nikolaus Hautsch and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this article, we introduce the so-called stochastic conditional intensity (SCI) model by extending Russell's (1999) autoregressive conditional intensity (ACI) model by a latent common dynamic factor that jointly drives the individual intensity components. We show by simulations that the proposed model allows for a wide range of (cross-)autocorrelation structures in multivariate point processes. The model is estimated by simulated maximum likelihood (SML) using the efficient importance sampling (EIS) technique. By modeling price intensities based on NYSE trading, we provide significant evidence for a joint latent factor and show that its inclusion allows for an improved and more parsimonious specification of the multivariate intensity process.
Book Synopsis Autoregressive Conditional Duration by : Jeffrey R. Russell
Download or read book Autoregressive Conditional Duration written by Jeffrey R. Russell and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a new statistical model for the analysis of data that do not arrive in equal time intervals, such as financial transactions data, telephone calls, or sales data on commodities that are tracked electronically. In contrast to fixed interval analysis, the model treats the time between events as a stochastic time varying process. We propose a new model for point processes with intertemporal correlation. Because the model focuses on the time interval between events it is called the Autoregressive Conditional Duration (ACD) model. Strong evidence is provided for transaction clustering for the financial transactions dataanalyzed, even after time-of-day effects are removed. Although the model is most naturally applied to the arrival of transactions, we suggest a thinning algorithm to model characteristics associated with the arrival times, allowing the investigator to model processes that are observed in irregular time intervals, not just the arrival times of the data. Models for transaction events, the flow of volume, and the rate of change for prices are estimated.
Book Synopsis Extreme Value Models in a Conditional Duration Intensity Framework by : Rodrigo Herrera
Download or read book Extreme Value Models in a Conditional Duration Intensity Framework written by Rodrigo Herrera and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The analysis of return series from financial markets is often based on the Peaks-over-threshold (POT) model. This model assumes independent and identically distributed observations and therefore a Poisson process is used to characterize the occurrence of extreme events. However, stylized facts such as clustered extremes and serial dependence typically violate the assumption of independence. In this paper we concentrate on an alternative approach to overcome these difficulties. We consider the stochastic intensity of the point process of exceedances over a threshold in the framework of irregularly spaced data. The main idea is to model the time between exceedances through an Autoregressive Conditional Duration (ACD) model, while the marks are still being modelled by generalized Pareto distributions. The main advantage of this approach is its capability to capture the short-term behaviour of extremes without involving an arbitrary stochastic volatility model or a prefiltration of the data, which certainly impacts the estimation. We make use of the proposed model to obtain an improved estimate for the Value at Risk. The model is then applied and illustrated to transactions data from Bayer AG, a blue chip stock from the German stock market index DAX. -- Extreme value theory ; autoregressive conditional duration ; value at risk ; self-exciting point process ; conditional intensity
Book Synopsis Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts by : Sebastian Trojan
Download or read book Modeling Intraday Stochastic Volatility and Conditional Duration Contemporaneously with Regime Shifts written by Sebastian Trojan and published by . This book was released on 2014 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Stochastic Conditional Distribution Models with Applications in Finance by : Jacob Lundbeck Serup
Download or read book Stochastic Conditional Distribution Models with Applications in Finance written by Jacob Lundbeck Serup and published by . This book was released on 2015 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: To be able to use the model in practice and to truly use the fact that this is a dynamic model, it should be possible to estimate the parameters of the model from observed conditional cumulants and that problem will also be addressed. This problem is complicated by the fact that the factors driving the market are not observable, but nevertheless a search algorithm can be constructed which estimates the parameters of the model very well and most importantly facilitates extracting the latent factors. This in turn will allow for the validity of the model to be verified by investigating if the time-series properties of the factors and checking if these are consistent with the assumptions of the model.
Book Synopsis Autoregressive Conditional Duration by : Robert F. Engle
Download or read book Autoregressive Conditional Duration written by Robert F. Engle and published by . This book was released on 1995 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt:
