Results 1  10
of
13
Stochastic Volatility in General Equilibrium,”working paper
, 2005
"... The connections between stock market volatility and returns are studied within the context of a general equilibrium framework. The framework rules out a priori any purely statistical relationship between volatility and returns by imposing uncorrelated innovations. The main model generates a twofact ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
The connections between stock market volatility and returns are studied within the context of a general equilibrium framework. The framework rules out a priori any purely statistical relationship between volatility and returns by imposing uncorrelated innovations. The main model generates a twofactor structure for stock market volatility along with timevarying risk premiums on consumption and volatility risk. It also generates endogenously a dynamic leverage effect (volatility asymmetry), the sign of which depends upon the magnitudes of the risk aversion and the intertemporal elasticity of substitution parameters.
Continuous time approximations to GARCH and stochastic volatility models
 AND MIKOSCH, TH. (EDS.), HANDBOOK OF FINANCIAL TIME SERIES
, 2008
"... ..."
Stochastic Volatility Models for Ordinal Valued Time Series with Application to Finance
"... In this paper we introduce a new class of models, called OSV, by combining an ordinal response model and the idea of stochastic volatility. Corresponding time series occur in highfrequency finance when the stocks are traded on a coarse grid. For parameter estimation we develop an efficient Grouped ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
In this paper we introduce a new class of models, called OSV, by combining an ordinal response model and the idea of stochastic volatility. Corresponding time series occur in highfrequency finance when the stocks are traded on a coarse grid. For parameter estimation we develop an efficient Grouped Move Multigrid Monte Carlo (GMMGMC) sampler. This sampler is based on a scale transformation group, whose elements operate on the random samples of a certain conditional distribution. Also volatility estimates are provided. For illustration, we apply our new model class to price changes of the IBM stock. Dependencies on covariates are quantified and compared with theoretical results for such processes.
Modelbased measurement of actual volatility in highfrequency data
"... Please send questions and/or remarks of nonscientific ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Please send questions and/or remarks of nonscientific
Modelling U.K. Inflation Uncertainty 1958−2006
, 2008
"... Abstract Robert Engle’s celebrated article that introduced the concept of autoregressive conditional heteroskedasticity (ARCH) included an application to UK inflation, 195877. This paper updates the estimation of his model and investigates its stability in the light of the well documented changes i ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
Abstract Robert Engle’s celebrated article that introduced the concept of autoregressive conditional heteroskedasticity (ARCH) included an application to UK inflation, 195877. This paper updates the estimation of his model and investigates its stability in the light of the well documented changes in policy towards inflation, 19582006. A simple autoregressive model with structural breaks in mean and variance, constant within subperiods (and with no unit roots), provides a preferred representation of the observed heteroskedasticity. Several measures of inflation forecast uncertainty are presented; these illustrate the difficulties presented by instability, not only for point forecasts but also, receiving increased attention nowadays, their uncertainty.
Unobserved Components Models in Economics and Finance THE ROLE OF THE KALMAN FILTER IN TIME SERIES ECONOMETRICS
"... Economic time series display features such as trend, seasonal, and cycle that we do not observe directly from the data. The cycle is of particular interest to economists as it is a measure of the fluctuations in economic activity. An unobserved components model attempts to capture the features of a ..."
Abstract
 Add to MetaCart
Economic time series display features such as trend, seasonal, and cycle that we do not observe directly from the data. The cycle is of particular interest to economists as it is a measure of the fluctuations in economic activity. An unobserved components model attempts to capture the features of a time series by assuming that they follow stochastic processes that, when put together, yield the observations. The aim of this article is thus to illustrate the use of unobserved components models in economics and finance and to show how they can be used for forecasting and policy making. Setting up models in terms of components of interest helps in model building; see the discussions in [1] and [2] for a comparison with alternative approaches. A detailed treatment of unobserved components models is given in [3]. The statistical treatment of unobserved components models is based on the statespace form. The unobserved Digital Object Identifier 10.1109/MCS.2009.934465 components, which depend on the state vector, are related to the observations by a measurement equation. The Kalman filter is the basic recursion for estimating the state, and hence the unobserved components, in a linear statespace model (see “Kalman Filter”). The estimates, which are based on current and past observations, can be used to make predictions. Backward recursions yield smoothed estimates of components at each point in time based on past, current, and future observations. A set of onestepahead prediction errors, called innovations, is produced by the Kalman filter. In a Gaussian model, the innovations can be used to construct a likelihood function that can be maximized numerically with respect to unknown parameters in the system; see [4]. Once the parameters are estimated, the innovations can be used to construct test statistics that are designed to assess how well the model fits. The STAMP package [5] embodies a modelbuilding procedure in which test statistics are produced as part of the output.
Gibbs estimation of microstructure models: Teaching notes
, 2010
"... This note discusses Gibbs estimation of the Roll model and various modifications. The goal is a more discursive and heuristic treatment of material covered in Hasbrouck (2009). Other applications of Gibbs samplers in market microstructure include Hasbrouck (1999) and Ball and Chordia (2001). The tec ..."
Abstract
 Add to MetaCart
This note discusses Gibbs estimation of the Roll model and various modifications. The goal is a more discursive and heuristic treatment of material covered in Hasbrouck (2009). Other applications of Gibbs samplers in market microstructure include Hasbrouck (1999) and Ball and Chordia (2001). The techniques discussed here follow an approach that relies on simulation to characterize model parameters. Applied to
and
"... Abstract: Financial return series of su ¢ ciently high frequency display stylized facts such as volatility clustering, high kurtosis, low starting and slowdecaying autocorrelation function of squared returns and the socalled Taylor e¤ect. In order to evaluate the capacity of volatility models to r ..."
Abstract
 Add to MetaCart
Abstract: Financial return series of su ¢ ciently high frequency display stylized facts such as volatility clustering, high kurtosis, low starting and slowdecaying autocorrelation function of squared returns and the socalled Taylor e¤ect. In order to evaluate the capacity of volatility models to reproduce these facts, we apply both standard and robust measures of kurtosis and autocorrelation of squares to …rstorder GARCH, EGARCH and ARSV models. Robust measures provide a fresh view on stylized facts which is useful because many …nancial time series are contaminated with outliers.
Tests of timeinvariance
, 2007
"... Quantiles provide a comprehensive description of the properties of a variable and tracking changes in quantiles over time using signal extraction methods can be informative. It is shown here how stationarity tests can be generalized to test the null hypothesis that a particular quantile is constant ..."
Abstract
 Add to MetaCart
Quantiles provide a comprehensive description of the properties of a variable and tracking changes in quantiles over time using signal extraction methods can be informative. It is shown here how stationarity tests can be generalized to test the null hypothesis that a particular quantile is constant over time by using weighted indicators. Corresponding tests based on expectiles are also proposed; these might be expected to be more powerful for distributions that are not heavytailed. Tests for changing dispersion and asymmetry may be based on contrasts between particular quantiles or expectiles. We report Monte Carlo experiments investigating the e¤ectiveness of the proposed tests and then move on to consider how to test for relative time invariance, based on residuals from …tting a timevarying level or trend. Empirical examples, using stock returns and U.S. in‡ation, provide an indication of the practical importance of the tests.